Compare commits

...
Author SHA1 Message Date
robbond 5049435005 docs: add v0.6 release notes 2026-08-03 07:38:05 +01:00
robbond e0d9019c2a docs: document v0.6 reasoning architecture 2026-08-03 07:24:46 +01:00
robbond b2ffc54964 feat: evaluate deterministic cross-branch corroboration 2026-08-03 07:19:20 +01:00
robbond 1d64144e01 feat: separate confidence from reasoning completeness 2026-08-03 07:05:20 +01:00
robbond 49765e95a0 feat: propagate child resolution through reasoning graph 2026-08-03 06:52:52 +01:00
robbond d52690cf2b feat: decompose composite unknowns before questioning 2026-08-03 06:32:12 +01:00
robbond 0723c2f49a feat: decompose composite unknowns before questioning 2026-08-02 19:24:38 +01:00
robbond b1c633ba5c feat: back next questions with explicit graph unknowns 2026-08-02 19:03:07 +01:00
robbond 25a989450c feat: advance reasoning after comparability is resolved 2026-08-02 17:06:34 +01:00
robbond 7d408701b5 fix: defer relationship classification until comparability is established 2026-08-02 16:48:10 +01:00
robbond c97f5f7303 feat: classify observation relationships after comparability 2026-08-02 16:40:24 +01:00
robbond 0c7558d31f feat: introduce comparability assessment before contradiction reasoning 2026-08-02 16:28:11 +01:00
robbond b84989b96a test: verify ambiguity handling across domains 2026-08-02 16:17:37 +01:00
robbond 51ce356218 fix: handle unjustified unknown selection ties 2026-08-02 16:09:00 +01:00
robbond a1f6d0c2b9 test: inspect structural influence in unknown selection 2026-08-02 15:40:06 +01:00
robbond 586802950d feat: explain deterministic unknown selection 2026-08-02 15:27:00 +01:00
robbond 5ef9710293 Implemented Investigation Strategy 2026-08-02 15:10:49 +01:00
robbond a79a7bd524 Merge branch 'feature/emergent-unknowns-v0.5' 2026-08-02 13:07:04 +01:00
robbond 2e4c624a8a docs: add v0.5 release notes 2026-08-02 13:07:04 +01:00
robbond 781d6a462f test: generalise question priority across decisions 2026-08-02 13:02:49 +01:00
robbond 48ce66dddb feat: formulate follow-up questions from graph context 2026-08-02 12:47:34 +01:00
robbond 4affadab4b fix: link emergent unknowns to answer-derived graph nodes 2026-08-02 12:17:49 +01:00
robbond 392564ed61 feat: prioritise follow-up questions by information value 2026-08-02 11:11:55 +01:00
robbond 72ef175971 feat: surface new unknowns after graph updates 2026-08-02 10:30:59 +01:00
robbond 904aec7616 Merge branch 'feature/reconstruction-v0.3' 2026-08-02 10:07:33 +01:00
robbond c3de80f203 docs: orchestrator and handoff 2026-08-02 10:06:48 +01:00
robbond a9bce79658 feat: add one-turn situation graph update UI 2026-08-02 09:43:42 +01:00
robbond a948910ba8 feat: add situation graph update API route 2026-08-02 08:32:18 +01:00
robbond cb77f955ed feat: apply validated graph update proposals 2026-08-02 08:24:56 +01:00
robbond f3cdfce0b0 feat: add graph update proposal orchestration 2026-08-02 08:15:54 +01:00
robbond b38a6a9f2e feat: define graph update proposal contract 2026-08-02 08:00:37 +01:00
robbond 02a6ecd0da fix: normalise compatible live reconstruction responses 2026-08-02 07:50:02 +01:00
robbond 575b8fd971 feat: connect UI to situation graph start flow 2026-08-02 07:21:40 +01:00
robbond 84858107b7 chore: document v0.4 route and test status 2026-08-02 06:59:23 +01:00
robbond 0ccc03c111 feat: add situation graph foundation 2026-08-02 06:59:23 +01:00
robbond 3c1362d8a1 feat: add initial situation graph orchestration 2026-08-02 06:52:26 +01:00
robbond 79ea2f6824 feat: add v0.3 normalised comparison reasoning
Add explicit reasoning guidance for normalising counts by exposure/denominator,
distinguishing total count from rate, and avoiding correlation-as-causation errors.

Changes:
- prompts/reconstruct-v0.3.md: new prompt with normalisation discipline
- lib/reconstruction/prompt.js: v0.3 loader + env var override support
- lib/analysis.js: defer DEFAULT_PROMPT_VERSION to prompt module (defaults to v0.3)
- PROMPT_VERSIONS extended to [v0.1, v0.2, v0.3]
- tests/v03-reasoning.test.js: 34 focused tests covering prompt loading, schema validation, guidance completeness, and target scenario fixture
- playwright.config.js + tests/smoke.test.js: minimal UI smoke test for browser rendering
- package.json: add @playwright/test as devDependency

Default switches to v0.3; v0.2 selectable via promptVersion or RECONSTRUCTION_PROMPT_VERSION env var.
2026-08-01 15:39:30 +01:00
robbond d72c7c5465 chore: establish clean v0.2 baseline
Include only the working reconstruction prototype with Ollama integration:
- double-wrapping fix (lib/llm/provider.js)
- explicit v0.2 JSON output schema (prompts/reconstruct-v0.2.md)
- Zod validation layer (lib/reconstruction/schema.js)
- shared core analysis path (lib/analysis.js)
- prompt versioning infrastructure (lib/reconstruction/prompt.js)
- provider abstraction
- functioning Ollama provider path
- updated API route with centralized analysis
- UI components displaying v0.2 data and validation errors
- .gitignore rules for generated evaluation artifacts

Exclude: evaluator experiments, diagnostic tests, debug scripts,
generated artifacts, comparison findings, test data tied to evaluator.
2026-08-01 14:45:06 +01:00
65 changed files with 19442 additions and 197 deletions
+5
View File
@@ -34,3 +34,8 @@ Thumbs.db
npm-debug.log*
yarn-debug.log*
yarn-error.log*
# Generated evaluation artifacts (regenerated each run)
evaluation-results/
provider-debug-results/
tests-results/
+28 -80
View File
@@ -1,101 +1,49 @@
import { getConfig } from "@/lib/config";
import { getProvider } from "@/lib/llm/provider";
import { reconstructionSchema } from "@/lib/reconstruction/schema";
const MAX_SCENARIO_LENGTH = 10000;
import {
analyseScenario,
PROMPT_VERSIONS,
DEFAULT_PROMPT_VERSION,
} from "@/lib/analysis";
export async function POST(request) {
const startTime = Date.now();
let rawResponse = null;
try {
const body = await request.json();
if (!body.scenario || typeof body.scenario !== "string") {
return Response.json(
{ error: "Request must include a 'scenario' string field" },
{ status: 400 }
{ status: 400 },
);
}
const trimmed = body.scenario.trim();
if (trimmed.length === 0) {
// Optional prompt version override
let promptVersion = DEFAULT_PROMPT_VERSION;
if (body.promptVersion && PROMPT_VERSIONS.includes(body.promptVersion)) {
promptVersion = body.promptVersion;
}
const result = await analyseScenario(body.scenario, { promptVersion });
if (!result.success) {
return Response.json(
{ error: "Scenario cannot be empty" },
{ status: 400 }
{ ...result, reconstruction: result.reconstruction || null },
{ status: Number(result.statusCode) || 500 },
);
}
if (trimmed.length > MAX_SCENARIO_LENGTH) {
return Response.json(
{ error: `Scenario must be under ${MAX_SCENARIO_LENGTH} characters` },
{ status: 400 }
);
}
const configResult = getConfig();
if (!configResult.ok) {
return Response.json(
{ error: "Invalid server configuration" },
{ status: 500 }
);
}
const { OLLAMA_BASE_URL, OLLAMA_MODEL } = configResult.config;
const provider = getProvider();
// Attempt parse to capture raw for debugging
let reconstruction;
try {
reconstruction = await provider.generateReconstruction(trimmed, OLLAMA_MODEL);
} catch (e) {
return Response.json(
{
error: e.message || "Unknown server error",
responseDurationMs: Date.now() - startTime,
modelName: OLLAMA_MODEL,
validationStatus: "invalid",
},
{ status: 500 }
);
}
// Try to stringify for rawResponse display (safe even if it's already an object)
try {
rawResponse = JSON.stringify(reconstruction);
} catch {
rawResponse = String(reconstruction).slice(0, 2000);
}
const duration = Date.now() - startTime;
// Validate with Zod schema
const validationResult = reconstructionSchema.safeParse(reconstruction);
if (!validationResult.success) {
return Response.json({
reconstruction: null,
modelName: OLLAMA_MODEL,
responseDurationMs: duration,
validationStatus: "invalid",
rawResponse: rawResponse?.slice(0, 2000),
errors: validationResult.error.issues.map((i) => `${i.path.join(".")}: ${i.message}`),
});
}
return Response.json({
reconstruction: validationResult.data,
modelName: OLLAMA_MODEL,
responseDurationMs: duration,
validationStatus: "valid",
rawResponse: rawResponse?.slice(0, 2000),
inputClassification: result.inputClassification,
reconstruction: result.reconstruction,
evidence: result.evidence,
nextQuestion: result.nextQuestion,
modelName: result.modelName,
responseDurationMs: result.responseDurationMs,
validationStatus: result.validationStatus,
promptVersion: result.promptVersion,
});
} catch (e) {
const duration = Date.now() - startTime;
return Response.json(
{ error: e.message || "Unknown server error", responseDurationMs: duration },
{ status: 500 }
{ error: e.message || "Unknown server error", responseDurationMs: 0 },
{ status: 500 },
);
}
}
+38
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@@ -0,0 +1,38 @@
import { startCase } from "@/lib/graph/orchestrator.js";
export async function POST(request) {
try {
const body = await request.json();
const result = await startCase(body);
if (result.success) {
return Response.json(result, { status: 200 });
}
const status =
result.statusCode === 400
? 400
: result.statusCode >= 500
? result.statusCode
: 500;
return Response.json(
{
success: false,
error: result.error ?? "Start case failed",
validationErrors: result.validationErrors,
diagnostics: result.diagnostics,
analysisErrors: result.analysisErrors,
},
{ status },
);
} catch {
return Response.json(
{
success: false,
error: "Internal server error",
},
{ status: 500 },
);
}
}
+68
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@@ -0,0 +1,68 @@
import { updateCase } from "@/lib/graph/orchestrator.js";
function mapFailureStatus(result) {
switch (result?.stage) {
case "request_validation":
case "graph_validation":
return 400;
case "provider":
return 502;
case "proposal_validation":
case "proposal_compatibility":
case "application":
return 422;
case "result_validation":
return 500;
default:
return 500;
}
}
function buildFailureResponse(result) {
return {
success: false,
stage: result?.stage ?? "internal",
error: result?.error ?? "Update case failed",
validationErrors: result?.validationErrors,
graphValidationErrors: result?.graphValidationErrors,
proposalErrors: result?.proposalErrors,
providerErrors: result?.providerErrors,
errors: result?.errors,
diagnostics: result?.diagnostics,
};
}
export async function POST(request) {
try {
const body = await request.json();
const result = await updateCase(body, { applyProposal: true });
if (result.success) {
return Response.json(result, { status: 200 });
}
return Response.json(buildFailureResponse(result), {
status: mapFailureStatus(result),
});
} catch (error) {
if (error instanceof SyntaxError) {
return Response.json(
{
success: false,
stage: "request_validation",
error: "Invalid JSON request body",
},
{ status: 400 },
);
}
return Response.json(
{
success: false,
stage: "internal",
error: "Internal server error",
},
{ status: 500 },
);
}
}
+97 -5
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@@ -1,3 +1,5 @@
import React from "react";
const ValidationIndicator = ({ status }) => {
const styles = {
valid: "text-green-600",
@@ -10,17 +12,91 @@ const ValidationIndicator = ({ status }) => {
invalid: "❌ Validation failed",
};
return (
<div className={`flex items-center gap-2 ${styles[status] || "text-gray-500"}`}>
<div
className={`flex items-center gap-2 ${styles[status] || "text-gray-500"}`}
>
<span className="font-medium">{labels[status] || status}</span>
</div>
);
};
const validationIcons = {
valid: "✅",
partial: "⚠️",
invalid: "❌",
};
export default function DiagnosticsView({ result }) {
if (!result) return null;
const diagnostics = result.diagnostics || result;
const metrics = [
{ label: "Model", value: result.modelName || "?" },
{ label: "Duration", value: result.responseDurationMs != null ? `${result.responseDurationMs}ms` : "?" },
{ label: "Validation", value: <ValidationIndicator status={result.validationStatus || "invalid"} /> },
{ label: "Model", value: diagnostics.modelName || result.modelName || "?" },
{ label: "Provider", value: "Ollama" },
{
label: "Prompt version",
value: diagnostics.promptVersion || result.promptVersion || "?",
},
{
label: "Duration",
value:
diagnostics.responseDurationMs != null
? `${diagnostics.responseDurationMs}ms`
: "?",
},
{
label: "Validation",
value: (
<ValidationIndicator
status={diagnostics.validationStatus || result.validationStatus || "invalid"}
/>
),
},
{
label: "Node count",
value:
diagnostics.nodeCount != null
? diagnostics.nodeCount
: diagnostics.graphNodeCount != null
? diagnostics.graphNodeCount
: "?",
},
{
label: "Edge count",
value:
diagnostics.edgeCount != null
? diagnostics.edgeCount
: diagnostics.graphEdgeCount != null
? diagnostics.graphEdgeCount
: "?",
},
{
label: "Graph references",
value:
diagnostics.graphReferenceValidation == null
? "?"
: diagnostics.graphReferenceValidation.valid
? `${validationIcons.valid} valid`
: `${validationIcons.invalid} invalid`,
},
{
label: "Investigation strategy",
value:
diagnostics.investigationStrategy?.key ||
diagnostics.investigationStrategy ||
result.selectedQuestion?.strategy ||
"?",
},
];
const errors = [
...(result.errors || []),
...(result.validationErrors || []),
...(result.graphValidationErrors || []),
...(result.proposalErrors || []),
...(result.providerErrors || []),
...(result.analysisErrors || []),
];
return (
@@ -35,16 +111,32 @@ export default function DiagnosticsView({ result }) {
))}
</dl>
{/* Collapsed raw output for debugging */}
{result.rawResponse && (
<details className="mt-4">
<summary className="cursor-pointer text-xs text-gray-500 underline hover:text-gray-700">
View raw model response
View raw model response (
{(result.rawResponse?.length || 0).toLocaleString()} chars)
</summary>
<pre className="mt-2 max-h-60 overflow-auto rounded bg-gray-900 px-3 py-2 text-xs leading-relaxed text-green-400">
{result.rawResponse}
</pre>
</details>
)}
{/* Errors if present */}
{errors.length > 0 && (
<details className="mt-3">
<summary className="cursor-pointer text-xs text-red-500 underline hover:text-red-700">
Validation errors ({errors.length})
</summary>
<ul className="mt-1 space-y-0.5 text-xs text-red-600">
{errors.map((err, i) => (
<li key={i}>{typeof err === "string" ? err : err?.message || JSON.stringify(err)}</li>
))}
</ul>
</details>
)}
</div>
);
}
+228
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@@ -0,0 +1,228 @@
import React from "react";
function ListSection({ title, items, renderItem = (item) => item }) {
if (!items?.length) return null;
return (
<section className="rounded-lg border border-gray-200 bg-white p-4">
<h3 className="mb-2 text-sm font-semibold text-gray-800">{title}</h3>
<ul className="space-y-1 text-sm text-gray-700">
{items.map((item, index) => (
<li key={`${title}-${index}`}>{renderItem(item)}</li>
))}
</ul>
</section>
);
}
export default function GraphUpdateView({ updateResult }) {
if (!updateResult?.proposal) return null;
const {
resolvedUnknownNodeIds,
affectedNodeIds,
previousActiveUnknownNodeId,
newActiveUnknownNodeId,
selectedQuestion,
changesApplied,
proposal,
previousSituationGraph,
updatedSituationGraph,
reasoningState,
previousReasoningState,
} = updateResult;
const newlySurfacedUnknownNodeIds = (proposal.addedNodes || [])
.filter((node) => node.kind === "unknown")
.map((node) => node.id);
const previousNodesById = new Map(
(previousSituationGraph?.nodes || []).map((node) => [node.id, node]),
);
const updatedNodesById = new Map(
(updatedSituationGraph?.nodes || []).map((node) => [node.id, node]),
);
const proposalUpdatesByNodeId = new Map(
(proposal.updatedNodes || []).map((update) => [update.nodeId, update]),
);
function resolveNodePresentation(nodeId) {
const previousNode = previousNodesById.get(nodeId) || null;
const updatedNode = updatedNodesById.get(nodeId) || null;
const node = updatedNode || previousNode;
const update = proposalUpdatesByNodeId.get(nodeId) || null;
if (!node) {
return (
<div className="space-y-1">
<div className="font-medium text-gray-900">Unknown node (ID: {nodeId})</div>
</div>
);
}
return (
<div className="space-y-1">
<div className="font-medium text-gray-900">{node.label}</div>
<div className="text-xs text-gray-600">
{node.kind} · {node.confidence}
</div>
{node.confidenceAssessment && (
<div className="text-xs text-gray-600">
evidence {node.confidenceAssessment.evidenceConfidence} · completeness {node.confidenceAssessment.completenessStatus} · conclusion {node.confidenceAssessment.conclusionConfidence}
</div>
)}
{(update?.previousStatus || update?.newStatus || node.status) && (
<div className="text-xs text-gray-700">
{update?.previousStatus ? `Previous status: ${update.previousStatus}` : null}
{update?.previousStatus && update?.newStatus ? " → " : null}
{update?.newStatus
? `New status: ${update.newStatus}`
: !update?.previousStatus
? `Status: ${node.status}`
: null}
</div>
)}
{update?.reason && <div className="text-xs text-gray-700">{update.reason}</div>}
</div>
);
}
function resolveActiveUnknown(nodeId) {
if (!nodeId) return null;
const node = updatedNodesById.get(nodeId) || previousNodesById.get(nodeId);
if (!node) {
return `Unknown node (ID: ${nodeId})`;
}
return `${node.label} · ${node.status} · ${node.confidence}`;
}
const changeItems = [
changesApplied?.addedNodeCount
? `${changesApplied.addedNodeCount} node(s) added`
: null,
changesApplied?.updatedNodeCount
? `${changesApplied.updatedNodeCount} node(s) updated`
: null,
changesApplied?.addedEdgeCount
? `${changesApplied.addedEdgeCount} edge(s) added`
: null,
changesApplied?.removedEdgeCount
? `${changesApplied.removedEdgeCount} edge(s) removed`
: null,
changesApplied?.resolvedUnknownCount
? `${changesApplied.resolvedUnknownCount} unknown(s) resolved`
: null,
].filter(Boolean);
const previousComparabilityStatus =
previousReasoningState?.comparabilityStatus ||
previousSituationGraph?.reasoningState?.comparabilityStatus ||
null;
const newComparabilityStatus =
reasoningState?.comparabilityStatus ||
updatedSituationGraph?.reasoningState?.comparabilityStatus ||
null;
const relationshipStatus =
reasoningState?.relationshipStatus ||
updatedSituationGraph?.reasoningState?.relationshipStatus ||
null;
const reasoningStagesAfter =
reasoningState?.reasoningStages ||
updatedSituationGraph?.reasoningState?.reasoningStages ||
[];
return (
<div className="space-y-4">
<section className="rounded-lg border border-blue-200 bg-blue-50 p-4">
<h2 className="mb-2 text-base font-semibold text-blue-900">
Graph update applied
</h2>
<div className="grid gap-2 text-sm text-blue-950 sm:grid-cols-2">
{previousActiveUnknownNodeId && (
<div>
<span className="font-medium">Previous active unknown:</span>{" "}
{resolveActiveUnknown(previousActiveUnknownNodeId)}
</div>
)}
{newActiveUnknownNodeId && (
<div>
<span className="font-medium">New active unknown:</span>{" "}
{resolveActiveUnknown(newActiveUnknownNodeId)}
</div>
)}
{selectedQuestion?.question && (
<div>
<span className="font-medium">Next question:</span>{" "}
{selectedQuestion.question}
</div>
)}
{previousComparabilityStatus && newComparabilityStatus && (
<div>
<span className="font-medium">Comparability:</span>{" "}
{previousComparabilityStatus} {newComparabilityStatus}
</div>
)}
{relationshipStatus && (
<div>
<span className="font-medium">Relationship status:</span>{" "}
{relationshipStatus}
</div>
)}
{!selectedQuestion?.question && !newActiveUnknownNodeId && previousActiveUnknownNodeId && (
<div>
<span className="font-medium">Next question status:</span> No next question selected yet.
</div>
)}
</div>
{reasoningStagesAfter.length > 0 && (
<div className="mt-3 text-sm text-blue-950">
<span className="font-medium">Reasoning stages:</span>{" "}
{reasoningStagesAfter
.map((stage) => `${stage.stage}: ${stage.status}`)
.join(" → ")}
</div>
)}
</section>
<ListSection
title="Resolved unknowns"
items={resolvedUnknownNodeIds}
renderItem={resolveNodePresentation}
/>
<ListSection
title="Newly surfaced unknowns"
items={newlySurfacedUnknownNodeIds}
renderItem={resolveNodePresentation}
/>
<ListSection
title="Affected nodes"
items={affectedNodeIds}
renderItem={resolveNodePresentation}
/>
<ListSection title="Applied changes" items={changeItems} />
<details className="rounded-lg border border-gray-200 bg-gray-50 p-4">
<summary className="cursor-pointer text-sm font-medium text-gray-700 underline">
Proposal details
</summary>
<pre className="mt-3 overflow-auto rounded bg-gray-900 p-3 text-xs text-green-400">
{JSON.stringify(proposal, null, 2)}
</pre>
<pre className="mt-3 overflow-auto rounded bg-gray-900 p-3 text-xs text-green-400">
{JSON.stringify(
{
previousActiveUnknownNodeId,
newActiveUnknownNodeId,
resolvedUnknownNodeIds,
affectedNodeIds,
},
null,
2,
)}
</pre>
</details>
</div>
);
}
+382 -42
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@@ -1,15 +1,8 @@
const categoryLabels = {
observations: "Direct Observations",
reportedClaims: "Reported Claims",
assumptions: "Unsupported Assumptions",
entities: "Entities",
transitions: "Transitions",
expectedButMissing: "Expected But Missing",
presentButUnexpected: "Present But Unexpected",
contradictions: "Contradictions",
openUncertainties: "Open Uncertainties",
};
"use client";
import { useMemo } from "react";
// ── Confidence badge (shared) ────────────────────────
const confidenceColor = {
low: "text-red-600 bg-red-50 border-red-200",
medium: "text-yellow-700 bg-yellow-50 border-yellow-200",
@@ -17,54 +10,401 @@ const confidenceColor = {
};
const ConfidenceBadge = ({ level }) => (
<span className={`inline-block rounded-full border px-2 py-0.5 text-xs font-medium ${confidenceColor[level] || "text-gray-600 bg-gray-100"}`}>
<span
className={`inline-block rounded-full border px-2 py-0.5 text-xs font-medium ${confidenceColor[level] || "text-gray-600 bg-gray-100"}`}
>
{level}
</span>
);
function ItemList({ items, renderExtra }) {
if (!items?.length) return <p className="text-sm italic text-gray-400">None identified</p>;
// ── Evidence type labels (shared) ───────────────────
const evidenceTypeLabels = {
direct_observation: "Direct Observation",
reported_statement: "Reported Statement",
interpretation: "Interpretation",
assumption: "Assumption",
inferred_relationship: "Inferred Relationship",
};
const importanceColors = {
incidental: "text-gray-500 bg-gray-50 border-gray-200",
supporting: "text-blue-700 bg-blue-50 border-blue-200",
important: "text-orange-700 bg-orange-50 border-orange-200",
critical: "text-red-800 bg-red-50 border-red-300 font-semibold",
};
const importanceLabels = {
incidental: "Incidental",
supporting: "Supporting",
important: "Important",
critical: "Critical",
};
// ── Input classification display ────────────────────
function ClassificationDisplay({ classification }) {
if (!classification) return null;
const p = classification.primaryType || classification.primary_type;
const sec =
classification.secondaryTypes || classification.secondary_types || [];
const modes =
classification.reasoningModes || classification.reasoning_modes || [];
// Normalize camelCase to snake_case for display if needed
const primaryLabel = String(p)
.replace(/_/g, " ")
.replace(/\b\w/g, (c) => c.toUpperCase());
const secLabels = sec.map((s) =>
s.replace(/_/g, " ").replace(/\b\w/g, (c) => c.toUpperCase()),
);
const modeLabels = modes.map((m) =>
m.replace(/_/g, " ").replace(/\b\w/g, (c) => c.toUpperCase()),
);
return (
<ul className="space-y-2">
{items.map((item) => (
<li key={item.id} className="rounded border border-gray-200 bg-white px-3 py-2 text-sm">
<div className="flex items-center gap-2">
<span className="font-mono text-xs text-gray-400">#{item.id}</span>
<ConfidenceBadge level={item.confidence} />
</div>
<p className="mt-1">{item.description}</p>
{renderExtra && renderExtra(item)}
</li>
))}
</ul>
<div className="rounded-lg border border-blue-200 bg-blue-50 p-4">
<h3 className="mb-2 text-sm font-semibold text-blue-700">
Input Classification
</h3>
<dl className="grid grid-cols-[auto_1fr] gap-x-4 gap-y-1.5 text-sm">
<dt className="text-blue-500">Primary type</dt>
<dd className="font-medium">{primaryLabel}</dd>
{secLabels.length > 0 && (
<>
<dt className="text-blue-500 pt-1">Secondary types</dt>
<dd>{secLabels.join(" · ")}</dd>
</>
)}
{modeLabels.length > 0 && (
<>
<dt className="text-blue-500 pt-1">Reasoning modes</dt>
<dd>{modeLabels.join(" · ")}</dd>
</>
)}
<dt className="text-blue-500 pt-1">Classification reason</dt>
<dd className="italic">
{classification.classificationReason ||
classification.classification_reason}
</dd>
<dt className="text-blue-500 pt-1">Confidence</dt>
<dd>
<ConfidenceBadge level={classification.confidence} />
</dd>
</dl>
</div>
);
}
// ── Reconstruction summary ──────────────────────────
function SummaryDisplay({ reconstruction }) {
if (!reconstruction?.summary) return null;
const summary = reconstruction.summary || reconstruction.Summary;
return (
<div className="rounded-lg border border-gray-200 bg-white p-4">
<h3 className="mb-2 text-sm font-semibold text-gray-600">
Reconstruction Summary
</h3>
<p className="text-sm leading-relaxed">{summary}</p>
</div>
);
}
// ── Generic item list (used for multiple sections) ──
function ItemList({ title, items, renderExtra }) {
const count = items?.length;
if (!count) return null; // hide empty sections entirely
const itemsArr = Array.isArray(items) ? items : [items];
return (
<div className="mb-4 rounded-lg border border-gray-200 bg-white p-4">
<h3 className="mb-2 text-sm font-semibold text-gray-600">
{title} ({count})
</h3>
<ul className="space-y-2">
{itemsArr.map((item, idx) => (
<li
key={item.id || `${title}-${idx}`}
className="rounded border border-gray-200 bg-white px-3 py-2 text-sm"
>
<div className="flex items-center gap-2">
{item.id && (
<span className="font-mono text-xs text-gray-400">
#{item.id}
</span>
)}
{item.confidence && <ConfidenceBadge level={item.confidence} />}
{item.importance && (
<span
className={`inline-block rounded-full border px-2 py-0.5 text-xs font-medium ${importanceColors[item.importance] || "text-gray-600 bg-gray-100"}`}
>
{importanceLabels[item.importance]}
</span>
)}
</div>
<p className="mt-1">{item.description}</p>
{renderExtra && renderExtra(item)}
</li>
))}
</ul>
</div>
);
}
// ── Plausible interpretations ───────────────────────
function InterpretationsDisplay({ interpretations }) {
if (!interpretations?.length) return null;
const arr = Array.isArray(interpretations)
? interpretations
: [interpretations];
return (
<div className="mb-4 rounded-lg border border-indigo-200 bg-indigo-50 p-4">
<h3 className="mb-2 text-sm font-semibold text-indigo-700">
Plausible Interpretations ({arr.length})
</h3>
<ul className="space-y-3">
{arr.map((interp, idx) => (
<li
key={interp.id || `${idx}`}
className="rounded border border-indigo-200 bg-white px-3 py-2.5 text-sm leading-relaxed"
>
<div className="flex items-center gap-2 mb-1">
<span className="font-medium text-indigo-600">
{interp.description}
</span>
{interp.confidence && (
<ConfidenceBadge level={interp.confidence} />
)}
</div>
{interp.supportingEvidenceIds?.length > 0 && (
<p className="text-xs text-gray-500">
Supporting evidence: {interp.supportingEvidenceIds.join(", ")}
</p>
)}
{interp.assumptionsRequired?.length > 0 && (
<p className="text-xs italic text-gray-500">
Requires assumptions: {interp.assumptionsRequired.join("; ")}
</p>
)}
</li>
))}
</ul>
</div>
);
}
// ── Next question (prominent) ───────────────────────
function NextQuestionDisplay({ question }) {
if (!question?.question) return null;
const q = question.question || question.Question;
const targets = question.targets || question.Targets || [];
const reason = question.reason || question.Reason || "";
const value =
question.expectedInformationValue ||
question.expected_information_value ||
"medium";
const valueLabel =
{ low: "Low", medium: "Medium", high: "High" }[value] || "Medium";
const valueColor =
{
low: "bg-yellow-100 text-yellow-800",
medium: "bg-blue-100 text-blue-800",
high: "bg-green-100 text-green-800",
}[value] || "";
return (
<div className="rounded-lg border-2 border-green-300 bg-green-50 p-5">
<div className="flex items-center gap-2 mb-2">
<h3 className="text-sm font-bold text-green-800">Next Question</h3>
<span
className={`rounded-full px-2 py-0.5 text-xs font-medium ${valueColor}`}
>
{valueLabel} value
</span>
</div>
<p className="mb-2 text-base font-medium text-gray-900">{q}</p>
{targets.length > 0 && (
<p className="text-sm text-gray-600">Targets: {targets.join(", ")}</p>
)}
{reason && (
<p className="text-sm italic text-gray-500">Because: {reason}</p>
)}
</div>
);
}
// ── Evidence list ───────────────────────────────────
function EvidenceDisplay({ evidence }) {
if (!evidence?.length) return null;
const arr = Array.isArray(evidence) ? evidence : [evidence];
const evidenceLabels = {
direct_observation: "👁 Direct Observation",
reported_statement: "🗣 Reported Statement",
interpretation: "💡 Interpretation",
assumption: "❓ Assumption",
inferred_relationship: "🔗 Inferred Relationship",
};
return (
<div className="mb-4 rounded-lg border border-gray-200 bg-white p-4">
<h3 className="mb-2 text-sm font-semibold text-gray-600">
Supporting Evidence ({arr.length})
</h3>
<ul className="space-y-2">
{arr.map((item, idx) => (
<li
key={item.id || `${idx}`}
className="rounded border border-gray-200 bg-white px-3 py-2 text-sm leading-relaxed"
>
<div className="flex items-center gap-2 mb-0.5 flex-wrap">
{item.id && (
<span className="font-mono text-xs text-gray-400">
#{item.id}
</span>
)}
<span
className={`inline-block rounded px-1.5 py-0.5 text-[10px] font-medium ${importanceColors[item.importance] || "text-gray-600 bg-gray-100"}`}
>
{importanceLabels[item.importance]}
</span>
<span className="inline-block rounded px-1.5 py-0.5 text-[10px] font-medium bg-gray-100 text-gray-700">
{evidenceLabels[item.evidenceType] || item.evidenceType}
</span>
{item.confidence && <ConfidenceBadge level={item.confidence} />}
</div>
<p className="text-sm">{item.description}</p>
{(item.source || item.attribution) && (
<p className="mt-0.5 text-xs text-gray-400">
Source: {item.source || item.attribution}
</p>
)}
</li>
))}
</ul>
</div>
);
}
// ── Main component ──────────────────────────────────
export default function ReconstructionView({ reconstruction, partial }) {
// Handle both v0.2 direct object and wrapped result formats
const data = reconstruction;
if (partial) {
return (
<div className="rounded-lg border border-yellow-300 bg-yellow-50 px-4 py-3 text-sm text-yellow-800">
Partial result some fields failed validation. Showing what was accepted.
Partial result some fields failed validation. Showing what was
accepted.
</div>
);
}
const categories = Object.entries(categoryLabels).map(([key, label]) => ({
key,
label,
items: reconstruction[key],
}));
return (
<div className="space-y-1">
<h2 className="mb-3 text-lg font-semibold">Reconstruction</h2>
{categories.map(({ key, label, items }) => (
<div key={key} className="mb-4 rounded border border-gray-200 bg-white p-4">
<h3 className="mb-2 text-sm font-medium text-gray-600">{label}</h3>
<ItemList items={items} />
</div>
))}
<div className="space-y-4">
{/* Classification first */}
{data.inputClassification && (
<ClassificationDisplay classification={data.inputClassification} />
)}
{/* Summary */}
{data.reconstruction?.summary && (
<SummaryDisplay reconstruction={data.reconstruction} />
)}
{/* Key differences */}
{data.reconstruction?.differences && (
<ItemList
title="Key Differences"
items={data.reconstruction.differences}
/>
)}
{/* Unexplained transitions */}
{data.reconstruction?.unexplainedTransitions &&
data.reconstruction.unexplainedTransitions.length > 0 && (
<ItemList
title="Unexplained Transitions"
items={data.reconstruction.unexplainedTransitions}
renderExtra={(i) =>
i.entity && (
<p className="mt-1 text-xs text-gray-500">Entity: {i.entity}</p>
)
}
/>
)}
{/* Contradictions */}
{data.reconstruction?.contradictions &&
data.reconstruction.contradictions.length > 0 && (
<ItemList
title="Contradictions"
items={data.reconstruction.contradictions}
/>
)}
{/* Important unknowns */}
{data.reconstruction?.importantUnknowns &&
data.reconstruction.importantUnknowns.length > 0 && (
<ItemList
title="Important Unknowns"
items={data.reconstruction.importantUnknowns}
/>
)}
{/* Plausible interpretations */}
{data.reconstruction?.plausibleInterpretations &&
data.reconstruction.plausibleInterpretations.length > 0 && (
<InterpretationsDisplay
interpretations={data.reconstruction.plausibleInterpretations}
/>
)}
{/* Secondary reconstruction categories (actors, systems, etc.) */}
{data.reconstruction?.actors && data.reconstruction.actors.length > 0 && (
<ItemList title="Actors" items={data.reconstruction.actors} />
)}
{data.reconstruction?.systemsOrObjects &&
data.reconstruction.systemsOrObjects.length > 0 && (
<ItemList
title="Systems / Objects"
items={data.reconstruction.systemsOrObjects}
/>
)}
{data.reconstruction?.expectedStates &&
data.reconstruction.expectedStates.length > 0 && (
<ItemList
title="Expected States"
items={data.reconstruction.expectedStates}
/>
)}
{data.reconstruction?.observedStates &&
data.reconstruction.observedStates.length > 0 && (
<ItemList
title="Observed States"
items={data.reconstruction.observedStates}
/>
)}
{data.reconstruction?.knownTransitions &&
data.reconstruction.knownTransitions.length > 0 && (
<ItemList
title="Known Transitions"
items={data.reconstruction.knownTransitions}
renderExtra={(i) => (
<div className="mt-1 text-xs text-gray-500">
{i.entity && <span>Entity: {i.entity} · </span>}
From &ldquo;{i.previousState}&rdquo; To &ldquo;{i.currentState}&rdquo; (&quot;{i.explanationStatus}&quot;)
</div>
)}
/>
)}
{/* Next question — prominent */}
<NextQuestionDisplay question={data.nextQuestion} />
{/* Evidence */}
{data.evidence && <EvidenceDisplay evidence={data.evidence} />}
</div>
);
}
+295 -44
View File
@@ -1,37 +1,168 @@
"use client";
import React from "react";
import { useState, useRef } from "react";
import ReconstructionView from "@/components/reconstruction-view";
import DiagnosticsView from "@/components/diagnostics-view";
import GraphUpdateView from "@/components/graph-update-view";
import SituationGraphView from "@/components/situation-graph-view";
const MAX_LENGTH = 10000;
export async function submitScenarioForStartCase(fetchImpl, scenario) {
return fetchImpl("/api/cases/start", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({ scenario }),
});
}
export async function submitAnswerForUpdateCase(
fetchImpl,
{ situationGraph, previousQuestion, answer },
) {
if (!answer?.trim()) {
return {
ok: false,
skipped: true,
data: {
success: false,
stage: "request_validation",
error: "Please enter an answer before updating.",
},
};
}
const response = await fetchImpl("/api/cases/update", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({ situationGraph, previousQuestion, answer }),
});
return {
ok: response.ok,
skipped: false,
data: await response.json(),
};
}
function normaliseStartResult(data) {
return {
...data,
selectedQuestion:
typeof data?.selectedQuestion === "string"
? data.selectedQuestion
: data?.selectedQuestion?.question ?? null,
newlySurfacedNodeIds: data?.newlySurfacedNodeIds ?? [],
};
}
function normaliseUpdateSelectedQuestion(selectedQuestion) {
if (!selectedQuestion) return null;
if (typeof selectedQuestion === "string") return selectedQuestion;
return selectedQuestion.question ?? null;
}
export function ScenarioResultPanels({ status, result }) {
if (!result) return null;
const hasGraph = Boolean(result.situationGraph);
const hasQuestion = Boolean(result.selectedQuestion?.question);
const hasDiagnostics = Boolean(result.diagnostics);
return (
<>
{status === "error" && (
<div className="space-y-3">
{result.error && (
<div className="rounded-lg border border-red-300 bg-red-50 px-4 py-3 text-sm text-red-700 whitespace-pre-wrap">
Error: {result.error}
</div>
)}
{!hasGraph && !hasQuestion && (
<div className="rounded-lg border border-yellow-300 bg-yellow-50 px-4 py-2 text-sm text-yellow-800">
Validation failed no structured graph output was produced.
</div>
)}
</div>
)}
{(status === "success" || hasGraph || hasQuestion) && (
<SituationGraphView
situationGraph={result.situationGraph}
selectedQuestion={result.selectedQuestion}
newlySurfacedNodeIds={result.newlySurfacedNodeIds}
/>
)}
{hasDiagnostics && <DiagnosticsView result={result} />}
</>
);
}
export function UpdateErrorPanel({ updateError }) {
if (!updateError) return null;
const errors = [
...(updateError.errors || []),
...(updateError.validationErrors || []),
...(updateError.graphValidationErrors || []),
...(updateError.proposalErrors || []),
...(updateError.providerErrors || []),
];
return (
<div className="space-y-3">
<div className="rounded-lg border border-red-300 bg-red-50 px-4 py-3 text-sm text-red-700 whitespace-pre-wrap">
Update error: {updateError.error}
</div>
{errors.length > 0 && (
<details className="rounded-lg border border-red-200 bg-red-50 px-4 py-3">
<summary className="cursor-pointer text-sm font-medium text-red-700 underline">
Update details ({errors.length})
</summary>
<ul className="mt-2 space-y-1 text-sm text-red-700">
{errors.map((item, index) => (
<li key={index}>
{typeof item === "string" ? item : item?.message || JSON.stringify(item)}
</li>
))}
</ul>
</details>
)}
</div>
);
}
export default function ScenarioForm() {
const [scenario, setScenario] = useState("");
const [status, setStatus] = useState("idle"); // idle | loading | error | success
const [result, setResult] = useState(null);
const [answer, setAnswer] = useState("");
const [updateStatus, setUpdateStatus] = useState("idle"); // idle | loading | error | success
const [updateError, setUpdateError] = useState(null);
const [updateResult, setUpdateResult] = useState(null);
const textareaRef = useRef(null);
const handleSubmit = async (e) => {
e.preventDefault();
setStatus("loading");
setResult(null);
setAnswer("");
setUpdateStatus("idle");
setUpdateError(null);
setUpdateResult(null);
try {
const res = await fetch("/api/analyse", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({ scenario }),
});
const res = await submitScenarioForStartCase(fetch, scenario);
const data = await res.json();
if (res.ok && data.validationStatus === "valid") {
if (res.ok && data.success) {
setStatus("success");
setResult(data);
setResult(normaliseStartResult(data));
} else {
setStatus("error");
setResult(data);
setResult(normaliseStartResult(data));
}
} catch (err) {
setStatus("error");
@@ -39,8 +170,64 @@ export default function ScenarioForm() {
}
};
// Always show diagnostics when there's a result (even if validation failed)
const hasDiagnostics = result && (result.reconstruction || result.modelName || result.responseDurationMs !== undefined);
const handleUpdate = async (e) => {
e.preventDefault();
const submission = await submitAnswerForUpdateCase(fetch, {
situationGraph: result?.situationGraph,
previousQuestion: result?.selectedQuestion,
answer,
});
if (submission.skipped) {
setUpdateStatus("error");
setUpdateError(submission.data);
return;
}
setUpdateStatus("loading");
setUpdateError(null);
try {
const outcome = submission.data;
if (submission.ok && outcome.success) {
setUpdateStatus("success");
setUpdateResult({
...outcome,
previousSituationGraph: result?.situationGraph ?? null,
});
setResult((current) => ({
...current,
situationGraph: outcome.updatedSituationGraph,
selectedQuestion: normaliseUpdateSelectedQuestion(
outcome.selectedQuestion,
),
newlySurfacedNodeIds: (outcome.proposal?.addedNodes || [])
.filter((node) => node.kind === "unknown")
.map((node) => node.id),
diagnostics: outcome.diagnostics,
}));
setAnswer("");
} else {
setUpdateStatus("error");
setUpdateError(outcome);
}
} catch (err) {
setUpdateStatus("error");
setUpdateError({ error: err.message || "Network request failed" });
}
};
const canRenderAnswerForm =
status === "success" &&
updateStatus === "idle" &&
Boolean(result?.situationGraph) &&
Boolean(result?.selectedQuestion);
const canRenderDisabledFollowUpForm =
updateStatus === "success" &&
Boolean(updateResult?.selectedQuestion?.question || result?.selectedQuestion);
return (
<div className="space-y-6">
@@ -54,7 +241,9 @@ export default function ScenarioForm() {
className="w-full rounded-lg border border-gray-300 px-4 py-3 text-sm focus:border-gray-500 focus:outline-none focus:ring-2 focus:ring-gray-400"
/>
<div className="flex items-center justify-between">
<span className="text-xs text-gray-400">{scenario.length}/{MAX_LENGTH}</span>
<span className="text-xs text-gray-400">
{scenario.length}/{MAX_LENGTH}
</span>
<button
type="submit"
disabled={status === "loading" || !scenario.trim()}
@@ -65,42 +254,104 @@ export default function ScenarioForm() {
</div>
</form>
{status === "error" && (
<div className="space-y-3">
{result?.error && (
<div className="rounded-lg border border-red-300 bg-red-50 px-4 py-3 text-sm text-red-700 whitespace-pre-wrap">
Error: {result.error}
</div>
)}
{hasDiagnostics && result?.modelName && (
<dl className="grid grid-cols-[auto_1fr] gap-x-4 gap-y-1.5 text-sm">
<dt className="text-gray-500">Model</dt>
<dd>{result.modelName}</dd>
<dt className="text-gray-500">Duration</dt>
<dd>{result.responseDurationMs != null ? `${result.responseDurationMs}ms` : "?"}</dd>
</dl>
)}
</div>
)}
{status === "success" && result?.reconstruction && (
<div className="space-y-4">
<ReconstructionView reconstruction={result.reconstruction} />
<DiagnosticsView result={result} />
</div>
)}
{status === "error" && result?.reconstruction && (
<div className="space-y-3">
<div className="rounded-lg border border-yellow-300 bg-yellow-50 px-4 py-2 text-sm text-yellow-800">
Partial result some fields failed validation. Showing what was accepted.
{canRenderAnswerForm && (
<form onSubmit={handleUpdate} className="space-y-4 rounded-lg border border-gray-200 bg-white p-4">
<div>
<h2 className="text-base font-semibold text-gray-900">Selected Question</h2>
<p className="mt-1 text-sm text-gray-700">{result.selectedQuestion}</p>
</div>
<ReconstructionView reconstruction={result.reconstruction} partial />
<div>
<label htmlFor="answer-textarea" className="mb-2 block text-sm font-medium text-gray-700">
Your answer
</label>
<textarea
id="answer-textarea"
value={answer}
onChange={(e) => setAnswer(e.target.value)}
rows={4}
className="w-full rounded-lg border border-gray-300 px-4 py-3 text-sm focus:border-gray-500 focus:outline-none focus:ring-2 focus:ring-gray-400"
placeholder="Enter the answer to the selected question..."
/>
</div>
<div className="flex items-center justify-between gap-4">
<p className="text-xs text-gray-500">
{updateStatus === "loading"
? "Applying validated graph update..."
: "One update turn only in this prototype."}
</p>
<button
type="submit"
disabled={updateStatus === "loading"}
className="rounded-lg bg-blue-700 px-4 py-2 text-sm font-medium text-white transition hover:bg-blue-600 disabled:cursor-not-allowed disabled:opacity-40"
>
{updateStatus === "loading" ? "Updating..." : "Update situation"}
</button>
</div>
</form>
)}
<UpdateErrorPanel updateError={updateError} />
{updateStatus === "success" && updateResult && (
<>
<div className="rounded-lg border border-yellow-300 bg-yellow-50 px-4 py-3 text-sm text-yellow-800">
{updateResult.selectedQuestion?.question
? updateResult.selectedQuestion.question
: "No next question selected yet."}
</div>
{canRenderDisabledFollowUpForm && (
<form className="space-y-4 rounded-lg border border-gray-200 bg-white p-4 opacity-70">
<div>
<h2 className="text-base font-semibold text-gray-900">Selected Question</h2>
<p className="mt-1 text-sm text-gray-700">
{updateResult.selectedQuestion?.question || result?.selectedQuestion}
</p>
</div>
<div>
<label htmlFor="follow-up-disabled-textarea" className="mb-2 block text-sm font-medium text-gray-700">
Your answer
</label>
<textarea
id="follow-up-disabled-textarea"
rows={4}
disabled
className="w-full rounded-lg border border-gray-300 px-4 py-3 text-sm opacity-70"
placeholder="Additional submission is disabled in this one-update prototype."
/>
</div>
<div className="flex items-center justify-between gap-4">
<p className="text-xs text-gray-500">
Additional submission is disabled in this one-update prototype.
</p>
<button
type="button"
disabled
className="rounded-lg bg-blue-700 px-4 py-2 text-sm font-medium text-white disabled:cursor-not-allowed disabled:opacity-40"
>
Update situation
</button>
</div>
</form>
)}
<GraphUpdateView updateResult={updateResult} />
</>
)}
<ScenarioResultPanels status={status} result={result} />
{(status === "loading" || updateStatus === "loading") && (
<div className="py-12 text-center text-sm text-gray-400">
Waiting for model response...
</div>
)}
{status === "loading" && (
<div className="py-12 text-center text-sm text-gray-400">Waiting for model response...</div>
{/* Empty state */}
{status === "idle" && (
<div className="rounded-lg border border-dashed border-gray-300 bg-gray-50 px-6 py-8 text-center">
<p className="text-sm text-gray-400">
Enter a scenario above and click Analyse to begin.
</p>
</div>
)}
</div>
);
+161
View File
@@ -0,0 +1,161 @@
"use client";
import React from "react";
function NodeBadge({ children, tone = "gray" }) {
const tones = {
gray: "border-gray-200 bg-gray-50 text-gray-700",
blue: "border-blue-200 bg-blue-50 text-blue-700",
green: "border-green-200 bg-green-50 text-green-700",
yellow: "border-yellow-200 bg-yellow-50 text-yellow-700",
red: "border-red-200 bg-red-50 text-red-700",
purple: "border-purple-200 bg-purple-50 text-purple-700",
};
return (
<span className={`rounded-full border px-2 py-0.5 text-xs ${tones[tone] || tones.gray}`}>
{children}
</span>
);
}
function NodeGroup({
title,
nodes,
resolvedNodeIds = new Set(),
newlySurfacedNodeIds = new Set(),
activeUnknownNodeId = null,
}) {
if (!nodes?.length) return null;
return (
<section className="rounded-lg border border-gray-200 bg-white p-4">
<h3 className="mb-3 text-sm font-semibold text-gray-700">
{title} ({nodes.length})
</h3>
<ul className="space-y-3">
{nodes.map((node) => (
<li key={node.id} className="rounded border border-gray-100 bg-gray-50 p-3 text-sm">
<div className="flex flex-wrap items-center gap-2">
<span className="font-medium text-gray-900">{node.label}</span>
<NodeBadge tone="blue">{node.status}</NodeBadge>
<NodeBadge tone="green">{node.confidence}</NodeBadge>
{node.confidenceAssessment?.completenessStatus && (
<NodeBadge tone="purple">
completeness: {node.confidenceAssessment.completenessStatus}
</NodeBadge>
)}
{resolvedNodeIds.has(node.id) && (
<NodeBadge tone="red">resolved unknown</NodeBadge>
)}
{newlySurfacedNodeIds.has(node.id) && (
<NodeBadge tone="purple">newly surfaced unknown</NodeBadge>
)}
{activeUnknownNodeId === node.id && (
<NodeBadge tone="yellow">active unknown</NodeBadge>
)}
{node.value != null && (
<NodeBadge tone="yellow">
{node.value}
{node.unit ? ` ${node.unit}` : ""}
</NodeBadge>
)}
</div>
{node.description && node.description !== node.label && (
<p className="mt-1 text-gray-600">{node.description}</p>
)}
{node.confidenceAssessment && (
<p className="mt-1 text-xs text-gray-500">
evidence: {node.confidenceAssessment.evidenceConfidence} ·
conclusion: {node.confidenceAssessment.conclusionConfidence}
</p>
)}
</li>
))}
</ul>
</section>
);
}
export default function SituationGraphView({
situationGraph,
selectedQuestion,
newlySurfacedNodeIds = [],
}) {
if (!situationGraph) return null;
const selectedQuestionText =
typeof selectedQuestion === "string"
? selectedQuestion
: selectedQuestion?.question ?? null;
const activeUnknown = situationGraph.activeUnknownNodeId
? situationGraph.nodes.find((node) => node.id === situationGraph.activeUnknownNodeId)
: null;
const nodesByKind = situationGraph.nodes.reduce((acc, node) => {
if (!acc[node.kind]) acc[node.kind] = [];
acc[node.kind].push(node);
return acc;
}, {});
const resolvedNodeIdSet = new Set(situationGraph.resolvedNodeIds || []);
const newlySurfacedNodeIdSet = new Set(newlySurfacedNodeIds || []);
return (
<div className="space-y-4">
{selectedQuestionText && (
<section className="rounded-lg border-2 border-green-300 bg-green-50 p-5">
<h2 className="mb-2 text-base font-bold text-green-800">Selected Question</h2>
<p className="text-base font-medium text-gray-900">{selectedQuestionText}</p>
</section>
)}
<section className="rounded-lg border border-gray-200 bg-white p-4">
<h2 className="mb-2 text-base font-semibold text-gray-900">Situation Graph</h2>
<dl className="space-y-2 text-sm">
<div>
<dt className="text-gray-500">Central statement</dt>
<dd className="font-medium text-gray-900">{situationGraph.centralStatement}</dd>
</div>
{situationGraph.currentSummary && (
<div>
<dt className="text-gray-500">Current summary</dt>
<dd className="text-gray-800">{situationGraph.currentSummary}</dd>
</div>
)}
{activeUnknown && (
<div>
<dt className="text-gray-500">Active unknown</dt>
<dd className="text-gray-900">{activeUnknown.label}</dd>
</div>
)}
<div>
<dt className="text-gray-500">Edge count</dt>
<dd className="text-gray-900">{situationGraph.edges.length}</dd>
</div>
</dl>
</section>
{Object.entries(nodesByKind).map(([kind, nodes]) => (
<NodeGroup
key={kind}
title={kind.replace(/_/g, " ")}
nodes={nodes}
resolvedNodeIds={resolvedNodeIdSet}
newlySurfacedNodeIds={newlySurfacedNodeIdSet}
activeUnknownNodeId={situationGraph.activeUnknownNodeId}
/>
))}
<details className="rounded-lg border border-gray-200 bg-gray-50 p-4">
<summary className="cursor-pointer text-sm font-medium text-gray-700 underline">
Raw graph JSON
</summary>
<pre className="mt-3 overflow-auto rounded bg-gray-900 p-3 text-xs text-green-400">
{JSON.stringify(situationGraph, null, 2)}
</pre>
</details>
</div>
);
}
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# Orchestrator Contract — Confidence Engine v0.4
## 1. Exported Function Signatures & Shape (JavaScript)
### lib/analysis.js
```js
export async function analyseScenario(scenario, opts = {})
// @param {string} scenario
// @param {{ promptVersion?: "v0.2" | "v0.3" }} [opts]
// @returns {Promise<{ success: boolean, validationStatus: "valid"|"invalid",
// modelName: string|null, responseDurationMs: number, rawResponse: string|null,
// promptVersion: string|null, inputClassification: object|null, reconstruction: object|null,
// evidence: object[]|undefined, nextQuestion: string|undefined, errors: string[]|undefined,
// error: string|undefined, statusCode: number|undefined }>}
export const PROMPT_VERSIONS // { [key: string]: string }
export const DEFAULT_PROMPT_VERSION // "v0.2"
```
### lib/graph/schema.js
```js
export const SituationKind // { observation, reported_claim, metric, state, transition, relationship, assumption, unknown, conclusion }
export const SituationStatus // { known, unknown, provisional, supported, weakened, contradicted, resolved }
export const ConfidenceLevel // { low, medium, high }
export const SituationRelationship // { supports, weakens, contradicts, depends_on, causes, may_cause, measures, compares_with, updates, other }
export const situationNodeSchema // Zod → {@typedef SituationNode}
export const situationEdgeSchema // Zod → {@typedef SituationEdge}
export const situationGraphSchema // Zod → {@typedef SituationGraph}
export const graphUpdateSchema // Zod → {@typedef GraphUpdate}
export const startCaseRequestSchema // { scenario: string (1-10000), promptVersion?: string }
export const updateCaseRequestSchema// { situationGraph: SituationGraph, previousQuestion: string, answer: string (1-5000), promptVersion?: string }
/** @param {string} label */ /** @returns {string} */ export function makeNodeId(label)
/** @param {{ id?, label, description, kind?, status?, confidence?, value?, unit?, ... }} opts */ /** @returns {SituationNode} */ export function makeNode(opts)
/** @param {{ id?, fromNodeId, toNodeId, relationship?, confidence?, description? }} opts */ /** @returns {SituationEdge} */ export function makeEdge(opts)
/** @param {{ centralStatement?, nodes?, edges?, activeUnknownNodeId?, resolvedNodeIds?, currentSummary? }} opts */ /** @returns {SituationGraph} */ export function makeGraph(opts)
```
### lib/graph/utils.js
```js
export function validateGraphReferences(graph) // → { valid: boolean, errors: string[] }
export function detectDuplicateNodeIds(nodes) // → { nodeId, count }[]
export function detectDuplicateEdges(edges) // { edgeId, fromNodeId, toNodeId, relationship }[]
export function findDependentNodes(graph, nodeId) // → string[] (transitive)
export function findAffectedNodes(graph, nodeId) // → string[] (direct + indirect via affects/dependsOn)
/** @param {SituationGraph} graph */ /** @param {string} nodeId */ /** @param {string} newStatus */ /** @param {*} newValue */ /** @param {string} reason */
export function resolveUnknownNode(graph, nodeId, newStatus, newValue, reason) // → { success, error?, previousStatus?, newStatus?, previousValue?, newValue?, reason?, affectedNodes? }
export function selectActiveUnknownCandidate(graph, resolvedNodeIds) // → { nodeId, label, score } | null
/** @param {SituationGraph} graph */ /** @param {GraphUpdate} update */
export function applyGraphUpdate(graph, update) // → { success: boolean, errors?, nodes?, edges?, resolvedNodeIds? }
/** @param {SituationGraph} graph */ /** @param {GraphUpdate} update */
export function validateGraphUpdate(graph, update) // → { valid: boolean, errors: string[] }
```
### lib/graph/builder.js
```js
export function buildInitialGraph(analysisData) // @param {{ reconstruction, evidence? }} → { nodes: SituationNode[], edges: SituationEdge[] }
export function buildMinimalGraph(scenario) // @param {string} → { nodes, edges }
export function describeGraph(graph) // @param {{ nodes, edges }} → string (summary text)
```
## 2. Dependencies Between Files
```
lib/analysis.js
├── getConfig() from lib/config.js
├── getProvider() from lib/llm/provider.js [EXTERNAL]
├── buildPrompt() from lib/reconstruction/prompt.js
└── reconstructionV2/V1Schema from lib/reconstruction/schema.js
lib/graph/utils.js ← imports situationNodeSchema, situationEdgeSchema, situationGraphSchema from schema.js
lib/graph/builder.js ← imports situationNodeSchema, situationEdgeSchema, makeNodeId from schema.js
docs/v0.4-handoff.md → references CaseOrchestrator.startCase()/updateCase() (not in any inspected file)
```
## 3. Side Effects (LLM Calls)
| Function | LLM Call? | Details |
|---|---|---|
| `analyseScenario()` | **Yes** | `provider.generateReconstruction(prompt, model)` — POST to configured LLM. Prompt from `buildPrompt(scenario, version)`. |
| All graph functions (`schema.js`, `utils.js`, `builder.js`) | No | Pure/deterministic only. |
| `startCase()` / `updateCase()` (per handoff) | **Yes** | startCase: calls analyseScenario. updateCase: calls LLM via buildUpdatePrompt context + provider for GraphUpdate, then applyGraphUpdate(). |
## 4. Minimal Proposed Contract for API Functions
### startCase(body)
- **Input:** `{ scenario: string (1-10000), promptVersion?: string }` — validated by `startCaseRequestSchema`.
- **Flow:** validate → `analyseScenario()` → if ok, `buildInitialGraph(result)`; on failure return minimal graph via `buildMinimalGraph()`.
- **Output (success):** `{ success: true, graphSummary: string, nodeCount: number, edgeCount: number, activeUnknownNodeId: string|undefined, nextQuestion: string }`
- **Output (failure):** `{ success: false, error: string, graphSummary: string, nodeCount: number, edgeCount: number }`
### updateCase(body)
- **Input:** `{ situationGraph: SituationGraph, previousQuestion: string (1+), answer: string (1-5000), promptVersion?: string }` — validated by `updateCaseRequestSchema`.
- **Flow:** validate → `buildUpdatePrompt(ctx)` → LLM call for GraphUpdate proposal → `validateGraphUpdate()``applyGraphUpdate()` → resolve unknowns via `resolveUnknownNode()` → pick next candidate via `selectActiveUnknownCandidate()`.
- **Output (success):** `{ success: true, graphSummary: string, nodeChanges: { added, updated, removed }, edgeChanges: { added, removed }, resolvedNodes: string[], nextQuestion: string|null }`
- **Output (failure):** `{ success: false, error: string, graphSummary: string, nodeChanges: {}, edgeChanges: {}, resolvedNodes: [], nextQuestion: null }`
## 5. Missing Interfaces — TODO
1. **[TODO]** `CaseOrchestrator` class described in handoff but absent from all five inspected files. startCase()/updateCase() wrappers need implementation per above contract.
2. **[TODO]** `buildUpdatePrompt(ctx)` (per handoff lives in prompt-builder.js) — not reviewed; input/output needs a separate doc once the file is available.
3. **[TODO]** LLM provider interface (`getProvider()`, `generateReconstruction(prompt, model)`) — external dependency. Assumes rawResponse is parseable JSON matching v0.2/v0.1 schema; needs explicit contract.
4. **[TODO]** Error handling for updateCase() on malformed LLM JSON — handoff notes "generic 500"; needs structured retry/error contract.
5. **[TODO]** Completion heuristic `getCompletionStatus()` referenced in handoff but absent; needs contract (e.g., "complete" when no unresolved unknown nodes).
---
*End of contract.*
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# v0.4 Handoff — Confidence Engine (confidence-engine)
**Date:** 2026-08-01
**Branch:** `feature/reconstruction-v0.3`
**Parent branch:** `main`
---
## 1. What This Project Is
A Next.js app that performs evidence-based situation reconstruction on user-supplied scenarios. An LLM analyses the scenario, builds a directed graph of actors, systems, unknowns and relationships, then iteratively refines the graph through multi-turn Q&A with the user.
---
## 2. Recent Commit History
| Commit | Message |
|--------|---------|
| `79ea2f6` | feat: add v0.3 normalised comparison reasoning |
| `d72c7c5` | chore: establish clean v0.2 baseline |
| `a2f9e47` | chore: preserve initial reconstruction prototype |
Only **one commit** ahead of `main`: `79ea2f6` — the v0.3 normalised comparison reasoning work.
---
## 3. Current State Summary
### What's done and committed to this branch
1. **v0.3 prompt** (`prompts/reconstruct-v0.3.md`) — a full LLM system prompt that adds:
- Normalisation / rate reasoning guidance (distinguishing absolute counts from per-unit rates)
- Interpretation discipline (empty array when evidence is too thin; no speculative filler)
- "Exactly one next question" constraint (no compound questions)
- Evidence type classification: `direct_observation`, `reported_statement`, `interpretation`, `assumption`, `inferred_relationship`
- Importance and confidence scales
- A strict camelCase JSON output schema with four top-level keys: `inputClassification`, `reconstruction`, `evidence`, `nextQuestion`
2. **v0.3 prompt versioning** (`lib/reconstruction/prompt.js`) — exports `PROMPT_VERSIONS`, `DEFAULT_PROMPT_VERSION ("v0.3")`, and `buildPrompt(scenario, version)` for loading prompt templates from disk with scenario substitution.
3. **Schema validation** (`lib/reconstruction/schema.js`) — Zod schemas for v0.2 output (`reconstructionV2Schema`). A `parseReconstructionV2(rawString)` helper is used in the analysis pipeline.
4. **v0.3 reasoning tests** (`tests/v03-reasoning.test.js`) — extensive test suite covering:
- Prompt version registration and loading
- v0.3 guidance completeness (normalisation, rate vs count, correlation-vs-causation)
- Schema validation with a realistic "production/complaints" fixture
- Parse helper tests
5. **Graph library** (`lib/graph/`) — the multi-turn reconstruction pipeline:
| File | Purpose |
|------|---------|
| `schema.js` | Zod schemas for SituationNode, SituationEdge, SituationGraph, GraphUpdate; helpers like `makeNodeId`, `makeNode`, `makeEdge`, `makeGraph` |
| `builder.js` | `buildInitialGraph(reconstruction, evidence)` — converts v0.2/v0.3 analysis output into a SituationGraph with deterministic nodes/edges; `buildMinimalGraph(scenario)` for fallback; `describeGraph(graph)` for display |
| `orchestrator.js` | `CaseOrchestrator` class managing the full multi-turn lifecycle (idle → building → active); exports `startCase(body)` and `updateCase(body)` convenience functions for API routes |
| `prompt-builder.js` | `buildUpdatePrompt(ctx)` — formats current graph state + Q&A context into a system prompt for the LLM update-evaluation turn |
| `utils.js` | Deterministic graph operations: `validateGraphReferences`, `detectDuplicateNodeIds`, `detectDuplicateEdges`, `findDependentNodes`, `findAffectedNodes`, `resolveUnknownNode`, `selectActiveUnknownCandidate`, `applyGraphUpdate`, `validateGraphUpdate` |
6. **API routes** (`app/api/`)
| Route | Purpose |
|-------|---------|
| `POST /api/start-case` | Start a new reconstruction case — accepts `{ scenario, promptVersion? }`, returns graph summary, node/edge counts, next question |
| `POST /api/update-case` | Process a turn — accepts `{ scenario, graph, answer, currentQuestion?, turnCount?, modelName? }`, returns updated graph summary, next question, changes summary |
7. **Smoke test** (`tests/smoke.test.js`) — basic integration test for the start-case API route.
### What's NOT yet committed (untracked files from git status)
| File | Description |
|------|-------------|
| `lib/graph/` (full directory) | The multi-turn graph library — built but NOT yet committed to any branch. These are the new untracked files: `builder.js`, `orchestrator.js`, `prompt-builder.js`, `schema.js`, `utils.js` |
| `tests/graph/` (full directory) | Tests for the graph library — also untracked: `builder.test.js`, `orchestrator.test.js`, `prompt-builder.test.js`, `schema.test.js`, `utils.test.js` |
| `app/api/start-case/route.js` | New API route (untracked) |
| `app/api/update-case/route.js` | New API route (untracked) |
> **Important:** The git status shows these files as untracked (`??`). They exist on disk but have never been staged or committed. You need to decide whether to commit them now or integrate them differently.
---
## 4. Test Status
```
Test Files: 4 failed | 4 passed (8)
Tests: 5 failed | 216 passed (221)
```
### Known failures
The failures cluster in `tests/graph/`:
- **`prompt-builder.test.js`** — test expects the literal string `"Existing or newly added nodes"` but the prompt template currently says `"existing or newly added nodes"` (case mismatch). The SYSTEM_PROMPT_HEADER constant uses lowercase.
- Other graph tests likely have similar fixture/reference issues.
Run `npx vitest run tests/graph/ --reporter=verbose` for full details.
---
## 5. Architecture Overview
```
User scenario
┌──────────────┐ ┌─────────────────┐ ┌──────────────┐
│ analyseScenario│──▶│ buildPrompt │──▶│ LLM (v0.3) │
│ (lib/analysis.js) │ (reconstruction/prompt.js) │ │
└──────────────┘ └─────────────────┘ └──────┬───────┘
┌──────────────┐
│ Parse output │
│ (Zod/parse │
│ Reconstruction│
│ V2) │
└──────┬───────┘
┌───────────────────────────────┤
▼ ▼
┌──────────────┐ ┌──────────────────┐
│buildInitialGraph│ │ buildMinimalGraph │
│ (graph/builder)│ │ (fallback) │
└──────┬─────────┘ └──────────────────┘
┌──────────────┐
│SituationGraph │ ← Zod-validated graph structure
│ {nodes, edges}│ nodes: observation/metric/unknown/...
└──────┬───────┘ edges: supports/weakens/causes/...
(multi-turn loop via updateCase)
┌─────────▼─────────┐
│buildUpdatePrompt │ → LLM proposes GraphUpdate
│ │
│applyGraphUpdate │ → deterministic, validated
│validateGraphUpdate│ (no direct LLM mutation)
└───────────────────┘
```
---
## 6. Key Design Decisions
### Normalisation / rate reasoning (v0.3 focus)
The v0.3 prompt explicitly instructs the model to:
- Always consider whether a denominator/exposure metric is needed when counts change alongside scale
- Distinguish absolute count from rate
- Avoid treating two rising counts as causal evidence (production growth may outpace complaint growth)
- Request the per-unit metric as the highest-value next question
### Graph immutability
LLM proposals are never applied directly. All mutations go through `applyGraphUpdate()` in `lib/graph/utils.js`, which:
- Validates all node/edge references exist
- Rejects duplicate IDs
- Enforces a max graph size (500 nodes) and update size (100KB)
- Returns the full new state for validation
### Prompt versioning
- Default is `"v0.3"` but `PROMPT_VERSIONS` includes `"v0.2"` for backward compatibility
- `RECONSTRUCTION_PROMPT_VERSION` env var can override default at module load time
- Prompts are loaded from `prompts/reconstruct-v0.{version}.md` on disk
### Deterministic node IDs
Node IDs are computed via a deterministic hash of the label: `makeNodeId(label)`. This avoids conflicts but means nodes must be created with consistent labels to get consistent IDs.
---
## 7. Open Questions / TODOs for Next Developer
1. **Untracked graph library**`lib/graph/` and `tests/graph/` are untracked on disk. Do we commit them as part of v0.4, or keep them in a separate branch?
2. **Test failures** — 5 tests fail across the graph test suite. The prompt-builder case-sensitivity issue needs fixing. Review all failing tests before merging.
3. **Missing `RECONSTRUCTION_PROMPT_VERSION` env var docs** — The system uses an env var override but it's not documented in `.env.example`. Add it if it's intended to be configurable.
4. **Provider integration**`lib/llm/provider.js` is imported by the orchestrator (`getProvider()`, `generateReconstruction()`). Verify the provider implementation matches what this code expects.
5. **Graph completeness heuristic**`CaseOrchestrator.getCompletionStatus()` returns `"complete"` when no unknown nodes remain, but doesn't consider whether all important observations have been verified.
6. **Error resilience in update flow** — If the LLM returns malformed JSON, the update route returns a 500 with a generic error message. Consider retry logic or structured error parsing.
7. **`buildUpdatePrompt` SYSTEM_PROMPT_HEADER is a module-level constant** — it's hardcoded and never versioned. If v0.5 changes the update-evaluation prompt style, this will need to become a template.
8. **The `nextQuestion` field on `/api/start-case` response** includes the adapted question (original + active unknown label appended). The client may want the original and adapted separately.
---
## 8. File Inventory (new / changed files on this branch)
### Prompts
- `prompts/reconstruct-v0.3.md`**NEW** — v0.3 system prompt (161 lines)
- `prompts/reconstruct-v0.2.md`**existing** — baseline prompt
### Core library
- `lib/analysis.js`**MODIFIED** — analyseScenario function (uses v0.3 prompt by default)
- `lib/reconstruction/prompt.js`**MODIFIED** — prompt versioning exports
- `lib/reconstruction/schema.js`**existing** — Zod schemas + parseReconstructionV2
### Graph library (untracked on disk)
- `lib/graph/builder.js` — buildInitialGraph, buildMinimalGraph, describeGraph
- `lib/graph/orchestrator.js` — CaseOrchestrator class, startCase, updateCase
- `lib/graph/prompt-builder.js` — buildUpdatePrompt + SYSTEM_PROMPT_HEADER
- `lib/graph/schema.js` — SituationNode/Edge/Graph/Update Zod schemas
- `lib/graph/utils.js` — validation, dedup, dependency, and apply utilities
### API routes (untracked on disk)
- `app/api/start-case/route.js`
- `app/api/update-case/route.js`
### Tests (untracked on disk)
- `tests/graph/builder.test.js`
- `tests/graph/orchestrator.test.js`
- `tests/graph/prompt-builder.test.js`
- `tests/graph/schema.test.js`
- `tests/graph/utils.test.js`
- `tests/v03-reasoning.test.js`**committed** to current branch
- `tests/smoke.test.js`
### Config changes
- `package.json` — added dependency (verify which one)
- `playwright.config.js` — added/modified for integration testing
- `.env.local` — exists locally (not committed)
---
## 9. How to Run
```bash
# Install dependencies
npm install
# Unit tests
npx vitest run
# Graph library tests (has 5 failures)
npx vitest run tests/graph/ --reporter=verbose
# Start dev server
npm run dev
# API endpoints
# POST /api/start-case → { scenario: "..." }
# POST /api/update-case → { graph: {...}, answer: "...", ... }
```
---
## 10. What to Do First (Recommended Priorities)
1. **Review and fix the 5 failing tests** — likely simple string/fixture issues
2. **Decide on the untracked files** — commit them, or create a v0.4 branch from this point
3. **Verify the LLM provider integration** — ensure `getProvider()` and `generateReconstruction()` are wired up correctly
4. **Add env var documentation** for `RECONSTRUCTION_PROMPT_VERSION` to `.env.example`
5. **Smoke test end-to-end** — call `/api/start-case` with a real scenario and verify the full flow
---
*End of handoff.*
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# v0.4 Route Status
- `app/api/cases/start/route.js`
- Current tracked start-case route for the v0.4 graph orchestration path.
- Covered by `tests/app/api/cases-start-route.test.js`.
- `app/api/cases/update/route.js`
- Current tracked update-case route for the v0.4 graph orchestration path.
- Delegates to `updateCase(body, { applyProposal: true })`.
- Covered by `tests/app/api/cases-update-route.test.js`.
- `app/api/start-case/route.js`
- Earlier experiment / duplicate start route.
- No repository UI/test references were found.
- Deleted from the working tree during UI connection cleanup.
- `app/api/update-case/route.js`
- Earlier experimental duplicate update route.
- Removed from the working tree during route consolidation.
- Current UI status
- `components/scenario-form.jsx` now calls `/api/cases/start` for the main experimental flow.
- `/api/cases/update` is the active tracked update route.
- `/api/analyse` remains available for legacy one-shot analysis.
- No UI changes were required for this route milestone.
@@ -0,0 +1,48 @@
# v0.5 Question Priority Generalisation
## Hypothesis
The current deterministic unknown selector and graph-context question formulator should generalise across several decision types by selecting a foundational unknown before downstream implementation or pricing leaves.
## Scenarios
1. Should we hire another engineer?
2. Should we replace the delivery vans?
3. Should we launch in another country?
4. Should we continue a project that is over budget?
5. Should we introduce a paid support tier?
## Results
| Scenario | Selected unknown | Strategy | Pass/Fail |
| ---------------------------- | --------------------------- | -------------------- | --------- |
| Hire another engineer | `hire-success-criteria` | `decision criterion` | Pass |
| Replace the delivery vans | `van-reliability-threshold` | `decision criterion` | Pass |
| Launch in another country | `country-value-threshold` | `actor/customer` | Pass |
| Continue over-budget project | `project-benefit-threshold` | `decision criterion` | Pass |
| Introduce paid support tier | `support-value-threshold` | `actor/customer` | Pass |
## Repeated failure patterns
Two repeated structural formulation failures appeared before the final pass:
1. **Constraint language in surrounding graph context outranked node-local decision-threshold language** in more than one case.
2. **Baseline language in surrounding graph context outranked node-local threshold language** in more than one case.
Both failures affected formulation strategy, not deterministic unknown selection.
## Code change made
A small deterministic change was made in `lib/graph/question-formulator.js`:
- prefer node-local `definition` language before broader criterion inference
- prefer node-local `decision criterion` language before context-only `constraint` inference
- only treat `baseline` or `constraint` as primary when the selected node itself carries that language, otherwise allow them as fallback strategies later
No architecture, UI, persistence, prompt, scoring, additional model turns, or provider calls were added.
## Remaining limitations
- In two passing cases, the selector chose a threshold-style foundational node while the formulator still used an `actor/customer` strategy because related context strongly referenced customers or recipients.
- This experiment is fixture-driven and deterministic; it is useful for regression protection, not scientific validation.
- The suite exercises the production path without model calls, but it does not prove behaviour over arbitrary real-world graph structures.
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# v0.5 Release Notes
## Purpose of v0.5
v0.5 stabilises the graph-backed one-turn update flow so the engine can resolve an answered unknown, surface consequential new unknowns, prioritise the next unknown deterministically, and formulate a deterministic follow-up question without changing the UI or adding more model turns.
## Capabilities proven
v0.5 includes:
- resolving an existing unknown
- surfacing consequential new unknowns
- limiting emergent unknowns
- deterministic information-value prioritisation
- deterministic question formulation
- generalisation across five decision types
- graph-backed one-turn UI update
## Five-case generalisation result
All five deterministic fixture scenarios passed:
1. Should we hire another engineer?
2. Should we replace the delivery vans?
3. Should we launch in another country?
4. Should we continue a project that is over budget?
5. Should we introduce a paid support tier?
The selector chose a foundational unknown first in each case, avoided the downstream leaf first, required no model call, and preserved graph immutability during question formulation.
## Key deterministic safeguards
- proposal application re-selects the active unknown deterministically after validation
- information-value scoring penalises downstream or prerequisite-blocked unknowns
- emergent unknown validation limits additions and requires explicit answer-derived linkage
- final question wording is reformulated from graph context without an extra model turn
- question validation rejects compound, awkward, or pricing-led fallback phrasing
## Known limitation
A correctly selected threshold node can still be phrased using an actor/customer strategy when surrounding graph context strongly references customers or value recipients.
This limitation is recorded for the next experiment and is not being fixed in the v0.5 release-prep task.
## Deliberately excluded work
- no reasoning-logic expansion beyond the small deterministic formulation fixes already landed on the branch
- no new features
- no UI changes
- no persistence
- no additional model turn
- no Ollama calls for validation
- no evaluator-suite runs
- no Playwright runs
## Next experimental question
Can the question formulation strategy remain aligned with the selected node's role when surrounding graph context contains competing signals?
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# v0.6 Ambiguity Generalisation
## Hypothesis
If the selector truly handles unjustified contradiction ties generically, it should return ambiguity across multiple domains without preferring one explanation by wording alone.
## Scenarios
1. Revenue increased by 18%, but cash in the bank fell over the same period.
2. Customer satisfaction scores increased, but complaints also increased.
3. Average delivery time decreased by 25%, but order cancellations increased.
4. Website traffic doubled, but sales remained unchanged.
5. Production output increased by 30%, but quality defects also increased.
## Observed behaviour
All five fixtures produced the same pattern:
- candidate count: 2
- selector status: `ambiguous`
- tie reason: `No justified distinction between leading unknowns.`
- no explanation was favoured
- one broad investigation question was produced from the central contradiction
- neutral label renaming did not collapse ambiguity into a winner
## Repeated failure patterns
None observed across two or more scenarios.
The current ambiguity handling generalised cleanly across the five contradiction fixtures.
## Corrections
No production correction was required in this task.
## Lessons learned
- The current ambiguity path appears domain-agnostic when structure and semantic weights remain intentionally non-discriminating.
- Central-statement-based tie questions are broad enough to avoid prematurely backing one branch.
- The most useful regression signal is whether ambiguity survives neutral relabelling, not whether one label sorts ahead of another in display order.
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# v0.6 Atomicity Experiment
## Hypothesis
After deterministic unknown selection, the engine should assess whether the selected unknown is already atomic or is still too composite to ask directly.
If the unknown is atomic, the engine should proceed exactly as before.
If the unknown is composite, the engine should not ask that parent unknown directly. Instead, it should decompose it into a small set of explicit child unknowns representing broad, independent candidate dimensions that a non-expert could understand.
## Constraints
- No graph redesign
- No persistence
- No UI redesign
- No selection-weight tuning
- No Ollama calls in unit tests
## Deterministic rule introduced
Atomicity assessment is **not** a new investigation strategy.
It runs in the graph update path at this seam:
```text
unknown selection -> atomicity assessment -> optional decomposition -> deterministic reselection -> question formulation
```
The implementation uses deterministic text and graph-shape checks:
- focused unknowns like denominator / threshold / definition / baseline / evidence remain **atomic**
- broad relationship-explanation unknowns and broad “possible causes / what changed / explanation for why X but Y” unknowns become **composite**
## Decomposition behavior
When a selected unknown is composite:
1. The parent unknown remains unresolved.
2. Between 2 and 5 child unknowns are created or reused deterministically.
3. Children become explicit graph nodes.
4. Children link back to the parent with existing `depends_on` edges.
5. Children inherit the same “why it matters” discipline in their descriptions.
6. Deterministic selection reruns across the updated graph.
For the current relationship-explanation experiment, the broad child dimensions are:
- Whether the two observations reflect different timing
- How the two observations were measured
- Change affecting signal A more than signal B
- Change affecting signal B more than signal A
- One-off event during the period
These are intentionally non-jargon and broad enough to generalise across scenarios like:
- Revenue up / Cash down
- Customer satisfaction up / Complaints up
- Delivery time down / Cancellations up
- Traffic up / Sales flat
- Production up / Defects up
## Diagnostics added
The orchestrator now reports:
- `atomicityAssessment`
- `atomicityDecisionReason`
- `decompositionDepth`
- `decompositionAttempted`
- `decompositionAccepted`
- `decompositionStoppedReason`
- `proposedChildCount`
- `acceptedChildCount`
- `rejectedChildren`
- `selectedChildNodeId`
- `childQualitySummary`
- `propagationPerformed`
- `resolvedChildNodeId`
- `parentNodeId`
- `parentStatusBefore`
- `parentStatusAfter`
- `parentConfidenceBefore`
- `parentConfidenceAfter`
- `affectedAncestorIds`
- `nextSelectedSibling`
- `parentResolved`
- `decompositionPerformed`
- `childUnknownCount`
- `childNodeIds`
- `atomicityReason`
This sits alongside the existing explicit-emergent-unknown diagnostics.
## Observed outcome
The experiment was useful.
Before this change, the engine could select a broad explanation unknown and ask it directly.
After this change:
- the broad explanation parent remains explicit in the graph
- the engine decomposes it into child unknowns first
- the next asked question is backed by a more focused child unknown
- repeated updates reuse the same decomposition children deterministically
- child-quality checks reject compound or duplicate children before they enter the graph
- decomposition stops deterministically once a selected child is directly answerable
- resolving one child does not resolve the parent immediately
- resolved child evidence now propagates upward to the parent and ancestor chain deterministically
- parent status and confidence change conservatively after child resolution
- the next sibling becomes eligible for normal deterministic selection without recreating the resolved child
In the revenue-versus-cash case, the selected next question becomes:
> What evidence would clarify how the two observations were measured?
rather than asking the full broad explanation node directly.
## Upward propagation and reconstruction
Recursive reasoning is complete only when decomposition and reconstruction are both deterministic.
Confidence must not outrun completeness or evidence.
For this experiment, reconstruction now behaves as follows:
- when a child unknown resolves, that child keeps its own resolved status and answer evidence
- the parent is updated, but remains unresolved unless the deterministic completion rule is satisfied
- only the ancestor chain connected to that child is updated
- unrelated branches remain unchanged
- the deterministic selector then chooses the next justified unresolved sibling or related follow-up
For the current conservative completion rule:
- **one resolved child** → parent becomes `provisional` with higher confidence, but remains unresolved
- **all direct child unknowns resolved** → parent resolves deterministically with `high` confidence
The confidence model is now explicitly separated into:
- **evidence confidence**: how trustworthy the currently attached support is
- **completeness**: whether the required direct child structure is empty, partial, or complete
- **conclusion confidence**: how strongly the current parent state is justified given both evidence and completeness
Deterministic propagation rules now enforce:
- one resolved child may raise evidence confidence
- unresolved direct children cap conclusion confidence
- contradictory direct children block high conclusion confidence
- duplicate evidence does not increase confidence
- status changes do not raise confidence on their own
- parent resolution still requires the separate completion rule
## Cross-branch corroboration
The next confidence experiment adds deterministic branch interaction checks without changing the graph model.
The engine now distinguishes between:
- **multiple evidence**: more than one branch exists
- **independent corroboration**: distinct resolved branches support the same parent without sharing the same evidence key
- **duplicate evidence**: the same evidence key appears through multiple branches and must not be double-counted
- **conflicting evidence**: branches support incompatible positions, such as `recognised correctly` vs `recognised incorrectly`
Deterministic branch rules:
- corroboration only counts when branches are distinct and their evidence sources differ
- duplicate evidence groups never count as corroboration
- conflicts cap conclusion confidence and prevent a higher confidence upgrade
- independent branches remain interaction-neutral
Additional diagnostics now expose:
- `corroboratingBranchCount`
- `conflictingBranchCount`
- `duplicateEvidenceCount`
- `independentBranchCount`
- `interactionSummary`
- `confidenceAdjustmentReason`
Observed effect:
- independent corroboration can raise `evidenceConfidence`
- duplicate evidence produces no extra confidence increase
- conflicting evidence lowers or caps `conclusionConfidence`
- completeness rules still dominate whether a parent may become highly justified
Example progression:
- parent before: `unknown`, `medium`
- after resolving `How the two observations were measured`: parent becomes `provisional`, `medium`
- evidence confidence becomes `high`, completeness becomes `partial`, conclusion confidence becomes `medium`
- next sibling becomes selectable and the engine moves on without recreating the resolved child
## Interpretation
This supports the idea that recursive decomposition is a fundamental part of graph-backed questioning, not just a prompt refinement.
The main remaining limitation is that sibling selection still inherits the existing deterministic scorer. That means some domains may advance to a justified sibling that is not the intuitively expected next child, even though the propagation itself remains deterministic and graph-valid.
## Validation run
Covered by:
- `tests/graph/atomicity-assessment.test.js`
- `tests/graph/decomposition-quality.test.js`
- `tests/graph/upward-propagation.test.js`
- `tests/graph/apply-proposal.test.js`
- `tests/graph/orchestrator.test.js`
- `tests/graph/question-formulator.test.js`
- `tests/ui/scenario-form.test.jsx`
And then by the broader requested validation pass with lint and build.
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# v0.6 Comparability Experiment
## Hypothesis
The engine should confirm that observations are comparable before treating their difference as a contradiction that needs explanatory follow-up.
## Fixtures
1. Revenue increased by 18%, but cash in the bank fell over the same period.
2. Complaints increased. Production increased.
3. Average delivery time decreased by 25%, but order cancellations increased.
4. Customer satisfaction increased, but complaints increased.
5. Temperature increased. Ice melted.
6. Sales doubled. Sales doubled.
## Results
- The first four scenarios repeated the same failure pattern: contradiction-level investigation could begin before comparability was established.
- A deterministic comparability gate corrected that by producing one comparison question first.
- Confirmed comparability did not by itself imply contradiction.
- Temperature increased / Ice melted was reclassified as a compatible relationship, so no contradiction question was asked.
- Sales doubled / Sales doubled was reclassified as duplicate observations, so no follow-up question was asked.
## Relationship classification stage
After comparability assessment, observations now pass through a deterministic relationship classification stage:
- `contradictory`
- `compatible`
- `potentially_related`
- `duplicate`
- `insufficient_information`
## Whether comparability should become a permanent reasoning stage
Yes, in minimal deterministic form.
The repeated pattern appeared in four scenarios, so a small pre-contradiction comparability assessment is justified.
## Two-step experiment result
A comparison question is useful only if its answer advances the reasoning stage rather than merely adding more text.
In the revenue-versus-cash scenario, the first question now confirms whether the figures are comparable, and the answer resolves that existing uncertainty instead of creating a parallel note. After that update, the engine progresses from comparability assessment to cautious relationship assessment and can select one broad non-expert follow-up question.
Every justified next question should correspond to an explicit unresolved graph node.
The earlier fallback-only path has now been removed from the normal successful progression. After comparability is resolved and a further investigation question is justified, the engine creates or reuses an explicit unresolved reasoning unknown and lets deterministic selection and question formulation proceed through the standard graph pipeline. A fallback is now only acceptable as an explicit failure case, not as the normal source of the next question.
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# v0.6 Reasoning Architecture
## Purpose
This document describes the implemented deterministic reasoning architecture on branch `feature/question-strategy-alignment-v0.6`.
It is written for future developers who need to understand how v0.6 actually executes, what invariants it relies on, where the recursive loops are, and what the system deliberately does **not** attempt to do.
## End-to-end pipeline
The implemented runtime pipeline is:
```text
Scenario input
LLM analysis / reconstruction
Initial graph build
Deterministic unknown selection
Selected question
User answer
LLM graph-update proposal
Proposal parsing / normalisation
Proposal compatibility validation
Deterministic graph update application
Reasoning-state rebuild
Comparability assessment
Relationship classification
Explicit emergent unknown creation / reuse (if required)
Deterministic reselection
Atomicity assessment
Optional decomposition into child unknowns
Deterministic reselection
Resolved-child propagation upward
Confidence / completeness / corroboration update
Next active unknown
Question formulation
```
## Deterministic stages
### 1. Scenario analysis / reconstruction
- **Purpose**: obtain structured reconstruction material from scenario text
- **Input**: scenario, prompt version
- **Output**: analysis payload containing reconstruction, evidence, diagnostics, and optional next question
- **Why it exists**: provides the initial structured substrate from which the graph is built
- **What breaks if removed**: the graph builder has no structured reconstruction to convert into nodes and edges
### 2. Initial graph build
- **Purpose**: convert reconstruction output into an initial `SituationGraph`
- **Input**: reconstruction + evidence
- **Output**: graph nodes and edges, then `makeGraph(...)` wraps them with active/resolved/summary state
- **Why it exists**: all later reasoning is graph-based, not free text
- **What breaks if removed**: no explicit unknown nodes, no deterministic selection, no validated update loop
### 3. Deterministic unknown selection
- **Purpose**: choose the next active unknown from unresolved graph nodes
- **Input**: graph, resolved node IDs
- **Output**: selected candidate or explicit ambiguity result
- **Why it exists**: the system needs a deterministic next investigation target
- **What breaks if removed**: question ordering becomes arbitrary or hidden in prompts
### 4. Selected question exposure
- **Purpose**: expose the chosen unknown as the next question to the user
- **Input**: selected unknown + question formulation or tie-resolution logic
- **Output**: selected question object
- **Why it exists**: the user-facing loop must ask a concrete next question
- **What breaks if removed**: the system can build a graph but cannot continue interaction coherently
### 5. LLM graph-update proposal
- **Purpose**: transform a user answer into a proposed graph change set
- **Input**: current graph, previous question, answer, prompt version
- **Output**: raw JSON-like proposal
- **Why it exists**: the LLM is limited to proposing changes; it does not mutate the graph directly
- **What breaks if removed**: answers cannot affect the graph except through manual hard-coded logic
### 6. Proposal parsing / normalisation
- **Purpose**: parse JSON, remove null array items, apply known aliases, fill omitted optional fields
- **Input**: raw model response
- **Output**: validated `graphUpdateSchema` payload or structured parser failure
- **Why it exists**: model outputs are not trusted as-is
- **What breaks if removed**: malformed or partially missing model output would reach graph logic directly
### 7. Proposal compatibility validation
- **Purpose**: ensure the proposal is graph-safe and semantically valid before application
- **Input**: current graph + proposed update
- **Output**: accepted proposal or compatibility errors
- **Why it exists**: protects graph integrity and reasoning invariants
- **What breaks if removed**: duplicate IDs, missing references, fake selected questions, and no-op updates could corrupt the graph
### 8. Deterministic graph update application
- **Purpose**: apply only validated graph changes to a copied graph
- **Input**: graph + validated proposal
- **Output**: updated nodes, edges, resolved node IDs
- **Why it exists**: separates safe application from generation
- **What breaks if removed**: no explicit, replayable state transition exists
### 9. Reasoning-state rebuild
- **Purpose**: derive fresh comparability/relationship state from the updated graph
- **Input**: updated graph + optional override state
- **Output**: `reasoningState`
- **Why it exists**: reasoning stages are derived from graph state, not stored blindly
- **What breaks if removed**: comparability and relationship decisions drift from actual graph contents
### 10. Comparability assessment
- **Purpose**: decide whether supported observations are comparable enough for relationship reasoning
- **Input**: graph observations + central statement + optional stored override
- **Output**: comparability status/reason + contradiction permission
- **Why it exists**: relationship reasoning is gated by comparability
- **What breaks if removed**: contradiction or relationship reasoning would run over incomparable observations
### 11. Relationship classification
- **Purpose**: classify observation relationships once comparability permits it
- **Input**: graph + comparability result
- **Output**: relationship status, reason, whether a follow-up question is justified
- **Why it exists**: determines whether explanation-style follow-up is needed
- **What breaks if removed**: the system cannot distinguish compatible, duplicate, insufficient, and contradiction-adjacent observation sets
### 12. Explicit emergent unknown creation / reuse
- **Purpose**: ensure any justified relationship follow-up is represented by an explicit unresolved graph node
- **Input**: provisional graph + relationship assessment
- **Output**: reused or newly added explanation unknown and edges
- **Why it exists**: preserves the invariant that a question must originate from an explicit unknown
- **What breaks if removed**: relationship follow-up would revert to fallback-only question text not backed by the graph
### 13. Atomicity assessment
- **Purpose**: determine whether the selected unknown is directly investigable or too composite
- **Input**: selected unknown + graph context
- **Output**: `atomic` or `composite` decision with decomposition kind/reason
- **Why it exists**: prevents asking broad explanation unknowns directly
- **What breaks if removed**: the system asks high-level composite unknowns instead of decomposing them first
### 14. Optional decomposition
- **Purpose**: split a composite unknown into deterministic child unknowns
- **Input**: composite selected unknown + graph context
- **Output**: 25 child unknowns, edges, quality summary, rejection diagnostics
- **Why it exists**: narrows broad unknowns into explicit candidate dimensions
- **What breaks if removed**: recursive reasoning stops at broad parents and loses graph-backed substructure
### 15. Resolved-child propagation upward
- **Purpose**: move resolved child effects to parent and ancestor chain without prematurely resolving them
- **Input**: updated graph + proposal snapshot
- **Output**: parent/ancestor status and confidence updates, additional diagnostics
- **Why it exists**: decomposition requires deterministic reconstruction as well as decomposition
- **What breaks if removed**: child answers stay local and parents never become progressively better-supported
### 16. Confidence / completeness / corroboration update
- **Purpose**: derive parent-level `confidenceAssessment` from resolved children and branch interactions
- **Input**: parent child set + branch evidence/status interactions
- **Output**: `evidenceConfidence`, `completenessStatus`, `conclusionConfidence`, plus derived display `confidence`
- **Why it exists**: reasoning support must be separated from completion and contradiction state
- **What breaks if removed**: parent confidence collapses back into vague status-driven heuristics
### 17. Next active unknown + question formulation
- **Purpose**: reselect the next unresolved unknown and formulate a concrete next question
- **Input**: updated graph + selection state + graph context
- **Output**: next active unknown and question object
- **Why it exists**: closes the recursive interaction loop
- **What breaks if removed**: the system updates the graph but cannot continue investigation deterministically
## Architectural invariants
The current implementation enforces these invariants:
1. **A question must originate from an explicit unresolved unknown node.**
2. **Unknown selection is deterministic.**
3. **Alphabetical ordering is not treated as reasoning.**
4. **Relationship reasoning does not precede comparability.**
5. **The LLM never mutates the graph directly; it only proposes updates.**
6. **All graph updates are schema-validated before application.**
7. **All node/edge references must resolve to existing nodes.**
8. **Duplicate node IDs are rejected.**
9. **Duplicate added edge IDs are rejected.**
10. **A selected question cannot target a resolved unknown.**
11. **A resolved unknown updated to `resolved` must also appear in `resolvedUnknownNodeIds`.**
12. **A proposal must contain a meaningful change.**
13. **Every newly added unknown must include why-it-matters language.**
14. **Every newly added unknown must be explicitly connected to answer-derived graph structure.**
15. **Composite selected unknowns are decomposed before direct questioning when atomicity rules require it.**
16. **Parent unknowns remain unresolved until completion rules are satisfied.**
17. **Confidence must not outrun completeness.**
18. **Duplicate evidence cannot increase confidence.**
19. **Conflicting evidence caps conclusion confidence.**
20. **Cross-branch corroboration only counts for distinct branches with distinct evidence keys.**
21. **Ambiguous leading unknowns remain explicit ambiguity, not silent forced choice.**
## Recursive loops and stopping rules
### Main investigation loop
```text
Unknown
Question
Answer
Proposal
Graph update
Propagation
Next unknown
```
- **Exit condition**: no unresolved candidates remain, or no next question is justified, or proposal/application fails
- **Stopping rule**: deterministic selection returns `null` or explicit ambiguity, or update validation blocks progress
- **Completion behaviour**: continues only while the graph contains justified unresolved unknowns
### Decomposition loop
```text
Selected unknown
Atomicity assessment
If composite: decompose
Reselect child
Atomicity assessment again
```
- **Exit condition**: selected child is atomic; parent already has children; max decomposition depth reached; or decomposition quality fails
- **Stopping rule**: `MAX_DECOMPOSITION_DEPTH`, reuse instead of regeneration, or inability to produce enough valid child unknowns
- **Completion behaviour**: deterministic and bounded; no infinite recursive decomposition path is intentionally allowed
### Propagation loop
```text
Resolved child
Ancestor chain walk
Recompute parent state
Stop when no ancestor state changes
```
- **Exit condition**: no more parents in the ancestor chain or no state change
- **Stopping rule**: ancestor chain is explicit and finite; propagation does not invent new ancestors
- **Completion behaviour**: deterministic upward traversal with explicit stop on unchanged state
### Potential infinite loops reviewed
- **Unknown/question recursion**: bounded by unresolved unknown set, proposal validation, and explicit no-candidate states
- **Decomposition recursion**: bounded by max depth and child reuse rules
- **Propagation recursion**: bounded by finite ancestor chain and no-change stop condition
No intentional infinite reasoning loop is present in the implemented architecture.
## Graph lifecycle summary
### Node lifecycle
1. node created by `buildInitialGraph` or later proposal/decomposition/emergent-unknown logic
2. node validated by schema
3. node may become active unknown
4. node may be updated by proposal application
5. unknown node may become `resolved`, `provisional`, `contradicted`, or remain `unknown`
6. resolved unknown ID is tracked in `resolvedNodeIds`
### Edge lifecycle
1. edge created in initial graph or by deterministic proposal augmentation
2. edge validated against existing node IDs
3. edge may be removed only through explicit `removedEdgeIds`
4. edge relationships also update `dependsOn` / `childIds` projections during application
### Unknown lifecycle
1. initial unknown discovered from reconstruction
2. selected deterministically or left ambiguous
3. may be decomposed if composite
4. may be resolved directly by answer
5. may cause emergent reasoning unknown creation when relationship reasoning demands a new explicit question target
### Resolved lifecycle
1. proposal marks unresolved unknown resolved
2. reconciliation ensures resolution semantics are explicit
3. `resolvedUnknownNodeIds` feed graph application
4. propagation may resolve parent only when completion rule is met
### Confidence lifecycle
1. nodes begin with base `confidence`
2. parent/ancestor propagation derives `confidenceAssessment`
3. display `confidence` is derived from `conclusionConfidence`
4. completeness, duplicate evidence, contradiction, and corroboration constrain the result
### Question lifecycle
1. selected unknown becomes question target
2. `formulateQuestion` or tie-resolution logic produces question text
3. answer returns through update route
4. proposal may select a new question target or leave reselection to deterministic logic
Every major transition above is explicit in the current codebase rather than implicit in model text alone.
## Duplicated or overlapping concepts
The following concepts are intentionally close and may look duplicated:
- **status vs confidence**: status captures lifecycle/progression; confidence captures support strength
- **confidence vs confidenceAssessment**: `confidence` is now a derived display field, while `confidenceAssessment` carries separated reasoning dimensions
- **resolvedNodeIds vs node.status === resolved**: both are maintained; the first is a graph-level index, the second is node-local state
- **selectedQuestion in proposal vs selectedQuestion in final result**: proposal may omit or propose one, final result recomputes deterministic selection/questioning after graph logic
- **comparability state in reasoningState vs derived comparability from graph**: overrides may carry forward prior confirmed reasoning, but `buildReasoningState` still rebuilds from graph + override context
These are not necessarily defects, but they are the main places where future simplification pressure is likely.
## Known boundaries and deliberate exclusions
v0.6 deliberately does **not** attempt the following:
- probabilistic reasoning
- Bayesian inference
- persistence
- semantic embeddings
- fuzzy semantic similarity
- autonomous exploration outside explicit user answers
- multi-hop corroboration across unrelated subtrees without a shared direct parent
- expert-only jargon-specific reasoning modes
- UI-heavy reasoning visualisation beyond existing graph/update displays
- arbitrary non-deterministic tie breaking
## Defects found during this review
No new production defect was intentionally introduced or fixed as part of this architecture review.
## Developer notes
- `startCase` owns reconstruction → graph build → first deterministic selection.
- `updateCaseWithDependencies` owns proposal generation / parsing and delegates deterministic graph semantics to `applyValidatedProposal`.
- `applyValidatedProposal` is the main reasoning pipeline coordinator for update-time graph evolution.
- `question-formulator.js` owns comparability, relationship classification, atomicity assessment, investigation strategy selection, and question formulation.
- `utils.js` owns selection scoring, ordering, graph validation, and safe graph update application.
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# v0.6 Release Notes
## Purpose
v0.6 turns the engine into a deterministic recursive reasoning system that keeps next questions, decomposition, propagation, and confidence updates explicitly grounded in the situation graph.
## Capabilities added
- deterministic unknown selection explanations
- explicit ambiguity handling instead of silent tie-breaking
- comparability assessment before relationship reasoning
- relationship classification after comparability
- reasoning-stage progression after comparability answers
- graph-backed next questions via explicit unknown nodes
- investigation-strategy-based question formulation
- atomicity assessment for selected unknowns
- composite-unknown decomposition into child unknowns
- child-quality validation for decomposition outputs
- upward propagation from resolved children to parents and ancestors
- separation of evidence confidence, completeness, and conclusion confidence
- deterministic cross-branch corroboration, conflict, and duplicate-evidence handling
- developer-facing reasoning architecture documentation
## Reasoning pipeline summary
```text
Scenario
→ Reconstruction
→ Initial graph
→ Deterministic unknown selection
→ Question
→ Answer
→ Proposal
→ Proposal parsing / validation
→ Graph update
→ Reasoning-state rebuild
→ Comparability assessment
→ Relationship classification
→ Emergent unknown creation / reuse
→ Atomicity assessment
→ Optional decomposition
→ Propagation
→ Confidence / completeness / corroboration update
→ Next active unknown
→ Next question
```
## Core invariants
- every asked question must originate from an explicit unresolved unknown
- unknown selection is deterministic
- ambiguity is preserved explicitly when no justified distinction exists
- relationship reasoning cannot precede comparability
- parent nodes cannot resolve before completion rules are met
- confidence cannot outrun completeness
- duplicate evidence cannot increase confidence
- conflicting evidence caps conclusion confidence
- cross-branch corroboration only counts for distinct branches with distinct evidence keys
- the LLM proposes updates but does not mutate the graph directly
## What v0.6 proved
- graph-backed questioning works better when every justified next question maps to an explicit unresolved node
- broad unknowns can be decomposed deterministically before direct questioning
- resolved child evidence can be propagated upward without prematurely resolving parent reasoning
- confidence becomes easier to reason about when evidence quality, completeness, and conclusion strength are separated
- deterministic cross-branch corroboration can improve support without double-counting repeated evidence
## Known limitations
- sibling selection still depends on the existing deterministic scorer and may choose a justified next branch that is not always the intuitively expected one
- cross-branch corroboration is limited to direct child branches of the same parent
- no multi-hop corroboration exists across unrelated subtrees
- reasoning remains bounded to explicitly represented graph structure and user-provided answers
## Deliberate exclusions
- no persistence
- no autonomous exploration
- no probabilistic reasoning
- no Bayesian reasoning
- no semantic embeddings
- no expert mode
- no multi-hop corroboration across unrelated subtrees
- no heavy graph visualisation
## Next experimental question
`Can the engine preserve and reuse successful reasoning structures across separate cases without turning prior experience into unquestioned assumptions?`
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# v0.6 Selection Influence Experiment
## Hypothesis
The initial unknown selected for the revenue-versus-cash scenario may be driven more by graph structure, more by semantic keyword matches, or by both together.
## Scenario
`Revenue increased by 18%, but cash in the bank fell over the same period.`
## Actual selected node
- Node ID: `nqdzobz`
- Label: `Magnitude and nature of cash outflows (operating expenses, debt repayments, capex, or working capital shifts).`
- Deterministic investigation strategy: `definition`
- Deterministic question: `What evidence would resolve whether magnitude and nature of cash outflows (operating expenses, debt repayments, capex, or working capital shifts). is true?`
## Structural contribution
- Downstream dependency count: `0`
- Prerequisite position: no unresolved prerequisites; count `0`
- Dependency ordering / centrality: no candidate had downstream dependants or dependency depth advantage in the live graph
## Semantic contribution
- Objective: false
- Actor: false
- Criteria: false
- Measurement: false
- Terminology: false
- Constraint: false
- Pricing: false
- Implementation: false
- Optimisation: false
- Speculative: false
- Contribution list: only `downstream_dependencies` was present, with delta `0`
## Counterfactual results
- Live-shaped ordering: `nqdzobz` ranked above `niewza`, but both had score `0`, downstream `0`, and unresolved prerequisites `0`
- Links removed: ordering stayed the same, because the live graph already provided no differentiating structure between the two unknowns
- Wording neutralised: ordering flipped to the first unknown by neutral label order (`Unknown A` before `Unknown B`), showing the outcome remained tie-break-driven rather than structure-driven
## Conclusion
For this scenario, the actual winner was not selected because of graph structure and not selected because of semantic keyword weights. The live diagnostics show a complete tie on score, downstream influence, and prerequisite position, with every semantic match category false for both candidates. The winner was therefore chosen by the final tie-break rule, `label_asc`.
## Is a scoring change justified?
Not from this single experiment alone. The result shows a diagnostic gap for this scenario, but this task does not justify a scoring change by itself, and no scoring change is made.
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/**
* Core analysis pipeline — shared by API routes and evaluation harness.
* Calls the provider, parses output, validates against Zod schemas (v0.2 first, v0.1 fallback).
*/
import { getConfig } from "../lib/config.js";
import { getProvider } from "../lib/llm/provider.js";
import {
buildPrompt,
PROMPT_VERSIONS,
DEFAULT_PROMPT_VERSION,
} from "../lib/reconstruction/prompt.js";
import { normaliseAnalysisResponse } from "../lib/reconstruction/compatibility.js";
import {
reconstructionV2Schema,
reconstructionSchema as reconstructionV1Schema,
} from "../lib/reconstruction/schema.js";
const MAX_SCENARIO_LENGTH = 10000;
/**
* Analyse a scenario string through the full pipeline.
* @param {string} scenario - The scenario text to analyse
* @param {object} [opts]
* @param {"v0.1" | "v0.2"} [opts.promptVersion="v0.2"] - Prompt version to use
* @returns {Promise<object>} Analysis result with diagnostics
*/
export async function analyseScenario(scenario, opts = {}) {
const startTime = Date.now();
// ── Input validation ───────────────────────────────
if (typeof scenario !== "string") {
return buildErrorResponse("Input must be a string", startTime);
}
const trimmed = scenario.trim();
if (trimmed.length === 0) {
return buildErrorResponse("Scenario cannot be empty", startTime);
}
if (trimmed.length > MAX_SCENARIO_LENGTH) {
return buildErrorResponse(
`Scenario must be under ${MAX_SCENARIO_LENGTH} characters`,
startTime,
);
}
// ── Configuration check ────────────────────────────
const configResult = getConfig();
if (!configResult.ok) {
return buildErrorResponse("Invalid server configuration", startTime, "500");
}
const { OLLAMA_BASE_URL: _ignored, OLLAMA_MODEL } = configResult.config;
const promptVersion = opts.promptVersion || DEFAULT_PROMPT_VERSION;
// ── Build prompt ───────────────────────────────────
let promptObj;
try {
promptObj = await buildPrompt(trimmed, promptVersion);
} catch (e) {
return buildErrorResponse(
`Failed to build prompt: ${e.message}`,
startTime,
);
}
// ── Call provider ──────────────────────────────────
const provider = getProvider();
let rawResponse;
try {
rawResponse = await provider.generateReconstruction(
promptObj.prompt,
OLLAMA_MODEL,
);
} catch (e) {
return buildErrorResponse(
e.message || "Provider error during analysis",
Date.now() - startTime,
);
}
const duration = Date.now() - startTime;
// Try to capture raw response for diagnostics
let rawResponseStr;
try {
rawResponseStr = JSON.stringify(rawResponse);
} catch {
rawResponseStr = String(rawResponse).slice(0, 2000);
}
const compatibility = normaliseAnalysisResponse(rawResponse);
const candidateResponse = compatibility.normalised;
// ── Validate against v0.2 schema (preferred) ──────
const resultV2 = tryValidateAgainstSchema(
candidateResponse,
reconstructionV2Schema,
);
if (resultV2.valid) {
return buildSuccessResultV2(
resultV2.data,
OLLAMA_MODEL,
duration,
promptVersion,
compatibility,
);
}
// ── Fallback to v0.1 schema ────────────────────────
const resultV1 = tryValidateAgainstSchema(
candidateResponse,
reconstructionV1Schema,
);
if (resultV1.valid) {
return buildSuccessResultV1(
resultV1.data,
OLLAMA_MODEL,
duration,
promptVersion,
compatibility,
);
}
// ── Neither schema matched — partial failure ───────
return buildPartialResult(
rawResponseStr?.slice(0, 2000),
resultV2.error ?? resultV1.error,
OLLAMA_MODEL,
duration,
promptVersion,
compatibility,
);
}
/** Attempt validation against a Zod schema */
function tryValidateAgainstSchema(data, schema) {
if (!schema.safeParse) {
return {
valid: false,
error: new Error("Schema does not support safeParse"),
};
}
const result = schema.safeParse(data);
return result.success
? { valid: true, data: result.data }
: { valid: false, error: result.error };
}
// ── Result builders ──────────────────────────────────
function buildErrorResponse(message, elapsed, statusCode = 500) {
return {
success: false,
error: message,
modelName: null,
responseDurationMs: elapsed,
validationStatus: "invalid",
rawResponse: null,
promptVersion: null,
statusCode,
};
}
function buildCompatibilityDiagnostics(compatibility) {
return {
compatibilityApplied: compatibility.changesApplied.length > 0,
compatibilityChanges: compatibility.changesApplied,
compatibilityWarnings: compatibility.warnings,
};
}
function buildSuccessResultV2(data, model, duration, version, compatibility) {
return {
success: true,
validationStatus: "valid",
modelName: model,
responseDurationMs: duration,
rawResponse: JSON.stringify(data).slice(0, 3000),
promptVersion: version,
inputClassification: data.inputClassification,
reconstruction: data.reconstruction,
evidence: data.evidence,
nextQuestion: data.nextQuestion,
errors: undefined,
...buildCompatibilityDiagnostics(compatibility),
};
}
function buildSuccessResultV1(data, model, duration, version, compatibility) {
return {
success: true,
validationStatus: "valid",
modelName: model,
responseDurationMs: duration,
rawResponse: JSON.stringify(data).slice(0, 3000),
promptVersion: version,
inputClassification: null,
reconstruction: data,
evidence: undefined,
nextQuestion: undefined,
errors: undefined,
...buildCompatibilityDiagnostics(compatibility),
};
}
function buildPartialResult(
rawResp,
error,
model,
duration,
version,
compatibility,
) {
let errors = [];
if (error && typeof error.flatten === "function") {
errors = error.flatten().fieldErrors
? Object.entries(error.flatten().fieldErrors).flatMap(([k, v]) => [
`${k}: ${v.join(", ")}`,
])
: [String(error)];
} else if (error) {
errors = [String(error).slice(0, 500)];
}
return {
success: false,
validationStatus: "invalid",
modelName: model,
responseDurationMs: duration,
rawResponse: rawResp?.slice(0, 2000),
promptVersion: version,
inputClassification: null,
reconstruction: null,
evidence: undefined,
nextQuestion: undefined,
errors,
...buildCompatibilityDiagnostics(compatibility),
};
}
export { PROMPT_VERSIONS, DEFAULT_PROMPT_VERSION };
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/**
* Deterministic situation graph builder builds initial graph from scenario text.
* Takes v0.2/v0.3 analysis output (from analyseScenario) and constructs a SituationGraph.
*/
import {
situationNodeSchema,
situationEdgeSchema,
makeNodeId,
} from "./schema.js";
/**
* Build an initial situation graph from a v0.3 reconstruction result.
* @param {{ reconstruction: object, evidence: object[] | undefined }} analysisData
* @returns {{ nodes: import("./schema.js").SituationNode[], edges: import("./schema.js").SituationEdge[] }}
*/
export function buildInitialGraph(analysisData) {
const { reconstruction, evidence = [] } = analysisData;
if (!reconstruction || !reconstruction.summary) {
return { nodes: [], edges: [] };
}
const nodeMap = new Map(); // label -> node
// ── Helper: register or get a node by label ────────────
function ensureNode(
label,
kind,
status,
description,
value,
unit,
confidence,
) {
if (nodeMap.has(label)) return nodeMap.get(label);
const id = makeNodeId(label);
const node = situationNodeSchema.parse({
id,
label,
description: description ?? label,
kind,
status,
confidence,
value: value ?? null,
unit: unit ?? null,
evidenceIds: [],
dependsOn: [],
affects: [],
parentId: null,
childIds: [],
});
nodeMap.set(label, node);
return node;
}
// ── Evidence lookup ────────────────────────────────────
const evidenceMap = new Map();
for (const ev of evidence) {
if (ev.id) evidenceMap.set(ev.id, ev);
}
function addEvidenceToNode(nodeId, evidenceId) {
const node = Object.values(nodeMap).find((n) => n.id === nodeId);
if (node && !node.evidenceIds.includes(evidenceId)) {
node.evidenceIds.push(evidenceId);
}
}
// ── Extract observed states as nodes ────────────────────
const summaryNode = ensureNode(
reconstruction.summary || "Situation Summary",
"state",
"provisional",
"Summary of the situation from the scenario text",
null,
null,
"medium",
);
// Collect all observable quantities as metric nodes
const metrics = new Map();
if (reconstruction.observedStates) {
for (const obs of reconstruction.observedStates) {
const node = ensureNode(
obs.description || obs.label,
"observation",
"supported",
obs.description || obs.label,
null,
null,
obs.confidence || "medium",
);
if (obs.id) node.evidenceIds.push(obs.id);
}
}
// Actors as states/nodes
if (reconstruction.actors) {
for (const actor of reconstruction.actors) {
ensureNode(
actor.description || actor.label,
"observation",
"supported",
actor.description || actor.label,
null,
null,
actor.confidence || "medium",
);
}
}
if (reconstruction.systemsOrObjects) {
for (const sys of reconstruction.systemsOrObjects) {
ensureNode(
sys.description || sys.label,
"metric",
"known",
sys.description || sys.label,
null,
null,
sys.confidence || "medium",
);
}
}
// Differences as relationship nodes
if (reconstruction.differences) {
for (const diff of reconstruction.differences) {
const node = ensureNode(
diff.description || "Difference",
"relationship",
"supported",
diff.description || "Difference",
null,
null,
diff.confidence || "medium",
);
}
}
// Contradictions as nodes
if (reconstruction.contradictions) {
for (const c of reconstruction.contradictions) {
const node = ensureNode(
c.description || c.label,
"relationship",
"supported",
c.description || c.label,
null,
null,
c.confidence || "medium",
);
}
}
// Important unknowns as unknown nodes
const unknownNodes = [];
if (reconstruction.importantUnknowns) {
for (const unk of reconstruction.importantUnknowns) {
const node = ensureNode(
unk.description || unk.label,
"unknown",
"unknown",
unk.description || "Unknown factor in the situation",
null,
null,
unk.confidence || "low",
);
unknownNodes.push(node);
}
}
// Plausible interpretations
if (reconstruction.plausibleInterpretations) {
for (const interp of reconstruction.plausibleInterpretations) {
ensureNode(
interp.description || interp.label,
"assumption",
"provisional",
interp.description || "Plausible interpretation",
null,
null,
interp.confidence || "low",
);
}
}
// Known transitions
if (reconstruction.knownTransitions) {
for (const trans of reconstruction.knownTransitions) {
ensureNode(
`${trans.entity}: ${trans.previousState}${trans.currentState}`,
"transition",
trans.explanationStatus === "confirmed" ? "known" : "provisional",
trans.description ||
`Transition: ${trans.entity} from ${trans.previousState} to ${trans.currentState}`,
null,
null,
trans.confidence || "medium",
);
}
}
// ── Build edges between nodes ────────────────────────
const nodeArr = Array.from(nodeMap.values());
const edges = [];
// Link actors → observed states as measures relationships
let actorNodes = [];
let metricNodes = [];
let unknownNodeIds = [];
for (const n of nodeArr) {
if (n.kind === "observation" && n.status === "supported") {
// These are observations — link to summary
edges.push(
situationEdgeSchema.parse({
id: `e-sum-${n.id}`,
fromNodeId: n.id,
toNodeId: summaryNode.id,
relationship: "supports",
confidence: n.confidence || "medium",
description: `${n.label} supports the summary`,
}),
);
}
if (n.kind === "unknown") {
unknownNodeIds.push(n.id);
edges.push(
situationEdgeSchema.parse({
id: `e-unk-${n.id}`,
fromNodeId: n.id,
toNodeId: summaryNode.id,
relationship: "depends_on",
confidence: n.confidence || "low",
description: `${n.label} is an unresolved factor for this situation`,
}),
);
}
}
return { nodes: nodeArr, edges };
}
/**
* Build a minimal starting graph for any scenario.
* Used when analysis has no reconstruction data (e.g., error state).
*/
export function buildMinimalGraph(scenario) {
const shortLabel = scenario.slice(0, 80);
return {
nodes: [
situationNodeSchema.parse({
id: "n0",
label: shortLabel,
description: `Initial situation from: "${scenario.slice(0, 200)}"`,
kind: "state",
status: "provisional",
confidence: "low",
value: null,
unit: null,
evidenceIds: [],
dependsOn: [],
affects: [],
parentId: null,
childIds: [],
}),
],
edges: [],
};
}
/**
* Convert graph nodes/edges to a human-readable summary for display.
*/
export function describeGraph(graph) {
const parts = [];
// Count by kind
const byKind = {};
for (const n of graph.nodes) {
byKind[n.kind] = (byKind[n.kind] || 0) + 1;
}
parts.push(
`Nodes: ${Object.entries(byKind)
.map(([k, v]) => `${v} ${k}`)
.join(", ")}`,
);
parts.push(`Edges: ${graph.edges.length} total`);
parts.push(
`Unknowns: ${graph.nodes.filter((n) => n.status === "unknown").length} unresolved`,
);
return parts.join(" | ");
}
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/**
* Situation Graph Case Orchestrator manages the lifecycle of a case.
* startCase builds initial graph from analysis; updateCase applies answers.
*/
import { analyseScenario } from "../analysis.js";
import { assertConfig } from "../config.js";
import { getProvider } from "../llm/provider.js";
import {
makeGraph,
startCaseRequestSchema,
situationGraphSchema,
updateCaseRequestSchema,
} from "./schema.js";
import { buildInitialGraph, describeGraph } from "./builder.js";
import { applyValidatedProposal } from "./apply-proposal.js";
import { buildGraphUpdatePrompt } from "./prompt-builder.js";
import {
buildReasoningState,
formulateTieResolutionQuestion,
} from "./question-formulator.js";
import { parseGraphUpdateProposal } from "./update-proposal.js";
import {
explainUnknownSelection,
selectActiveUnknownCandidate,
validateGraphReferences,
} from "./utils.js";
function toValidationErrors(error) {
return (
error?.errors?.map((issue) => ({
path: issue.path,
message: issue.message,
code: issue.code,
})) ?? [{ message: "Validation failed" }]
);
}
function buildDiagnostics({
analysis,
graph,
graphReferenceValidation,
unknownSelectionExplanation,
}) {
return {
promptVersion: analysis?.promptVersion ?? null,
modelName: analysis?.modelName ?? null,
responseDurationMs: analysis?.responseDurationMs ?? null,
validationStatus: analysis?.validationStatus ?? "invalid",
nodeCount: graph?.nodes?.length ?? 0,
edgeCount: graph?.edges?.length ?? 0,
graphReferenceValidation,
compatibilityApplied: analysis?.compatibilityApplied ?? false,
compatibilityChanges: analysis?.compatibilityChanges ?? [],
compatibilityWarnings: analysis?.compatibilityWarnings ?? [],
unknownSelectionExplanation: unknownSelectionExplanation ?? null,
};
}
function buildUnknownSelectionDiagnostics(
graph,
resolvedNodeIds = [],
selectedQuestion = null,
) {
const explanation = explainUnknownSelection(graph, resolvedNodeIds);
if (explanation.status === "ambiguous") {
return {
...explanation,
tieResolutionQuestion:
selectedQuestion?.selectionStatus === "ambiguous"
? selectedQuestion.question
: formulateTieResolutionQuestion({ graph }).question,
alphabeticalUsedAsReasoning: false,
};
}
return explanation;
}
function buildUpdateDiagnostics({
promptVersion,
modelName,
responseDurationMs,
normalisationsApplied,
graph,
graphReferenceValidation,
selectedQuestion,
unknownSelectionExplanation,
previousReasoningState,
reasoningState,
resolvedReasoningNodeIds,
emergentReasoningNodeCreated,
emergentReasoningNodeId,
emergentReasoningNodeReason,
atomicityAssessment,
atomicityDecisionReason,
decompositionDepth,
decompositionAttempted,
decompositionAccepted,
decompositionStoppedReason,
proposedChildCount,
acceptedChildCount,
rejectedChildren,
selectedChildNodeId,
childQualitySummary,
propagationPerformed,
resolvedChildNodeId,
parentNodeId,
parentStatusBefore,
parentStatusAfter,
parentConfidenceBefore,
parentConfidenceAfter,
evidenceConfidenceBefore,
evidenceConfidenceAfter,
completenessBefore,
completenessAfter,
conclusionConfidenceBefore,
conclusionConfidenceAfter,
resolvedDirectChildren,
unresolvedDirectChildren,
contradictoryDirectChildren,
corroboratingBranchCount,
conflictingBranchCount,
duplicateEvidenceCount,
independentBranchCount,
interactionSummary,
confidenceCapReason,
ancestorPropagationStoppedReason,
affectedAncestorIds,
nextSelectedSibling,
parentResolved,
decompositionPerformed,
childUnknownCount,
childNodeIds,
atomicityReason,
}) {
return {
promptVersion: promptVersion ?? "v0.4",
modelName: modelName ?? null,
responseDurationMs: responseDurationMs ?? null,
validationStatus: "valid",
nodeCount: graph?.nodes?.length ?? 0,
edgeCount: graph?.edges?.length ?? 0,
graphReferenceValidation: graphReferenceValidation ?? {
valid: true,
errors: [],
},
normalisationsApplied: normalisationsApplied ?? [],
investigationStrategy:
selectedQuestion?.investigationStrategy ??
selectedQuestion?.strategy ??
null,
previousComparabilityStatus:
previousReasoningState?.comparabilityStatus ?? null,
comparabilityStatus: reasoningState?.comparabilityStatus ?? null,
relationshipStatus: reasoningState?.relationshipStatus ?? null,
relationshipAssessed: reasoningState?.relationshipAssessed ?? null,
reasoningStagesBefore: previousReasoningState?.reasoningStages ?? [],
reasoningStagesAfter: reasoningState?.reasoningStages ?? [],
resolvedReasoningNodeIds: resolvedReasoningNodeIds ?? [],
emergentReasoningNodeCreated: emergentReasoningNodeCreated ?? false,
emergentReasoningNodeId: emergentReasoningNodeId ?? null,
emergentReasoningNodeReason: emergentReasoningNodeReason ?? null,
atomicityAssessment: atomicityAssessment ?? null,
atomicityDecisionReason: atomicityDecisionReason ?? null,
decompositionDepth: decompositionDepth ?? 0,
decompositionAttempted: decompositionAttempted ?? false,
decompositionAccepted: decompositionAccepted ?? false,
decompositionStoppedReason: decompositionStoppedReason ?? null,
proposedChildCount: proposedChildCount ?? 0,
acceptedChildCount: acceptedChildCount ?? 0,
rejectedChildren: rejectedChildren ?? [],
selectedChildNodeId: selectedChildNodeId ?? null,
childQualitySummary: childQualitySummary ?? [],
propagationPerformed: propagationPerformed ?? false,
resolvedChildNodeId: resolvedChildNodeId ?? null,
parentNodeId: parentNodeId ?? null,
parentStatusBefore: parentStatusBefore ?? null,
parentStatusAfter: parentStatusAfter ?? null,
parentConfidenceBefore: parentConfidenceBefore ?? null,
parentConfidenceAfter: parentConfidenceAfter ?? null,
evidenceConfidenceBefore: evidenceConfidenceBefore ?? null,
evidenceConfidenceAfter: evidenceConfidenceAfter ?? null,
completenessBefore: completenessBefore ?? null,
completenessAfter: completenessAfter ?? null,
conclusionConfidenceBefore: conclusionConfidenceBefore ?? null,
conclusionConfidenceAfter: conclusionConfidenceAfter ?? null,
resolvedDirectChildren: resolvedDirectChildren ?? 0,
unresolvedDirectChildren: unresolvedDirectChildren ?? 0,
contradictoryDirectChildren: contradictoryDirectChildren ?? 0,
corroboratingBranchCount: corroboratingBranchCount ?? 0,
conflictingBranchCount: conflictingBranchCount ?? 0,
duplicateEvidenceCount: duplicateEvidenceCount ?? 0,
independentBranchCount: independentBranchCount ?? 0,
interactionSummary: interactionSummary ?? null,
confidenceCapReason: confidenceCapReason ?? null,
ancestorPropagationStoppedReason: ancestorPropagationStoppedReason ?? null,
affectedAncestorIds: affectedAncestorIds ?? [],
nextSelectedSibling: nextSelectedSibling ?? null,
parentResolved: parentResolved ?? false,
decompositionPerformed: decompositionPerformed ?? false,
childUnknownCount: childUnknownCount ?? 0,
childNodeIds: childNodeIds ?? [],
atomicityReason: atomicityReason ?? null,
unknownSelectionExplanation: unknownSelectionExplanation ?? null,
};
}
export async function startCase(body) {
const parsedRequest = startCaseRequestSchema.safeParse(body);
if (!parsedRequest.success) {
return {
success: false,
error: "Invalid start-case request",
validationErrors: toValidationErrors(parsedRequest.error),
statusCode: 400,
};
}
const { scenario, promptVersion } = parsedRequest.data;
const analysis = await analyseScenario(scenario, { promptVersion });
if (!analysis.success) {
return {
success: false,
error: analysis.error ?? "Scenario analysis failed",
diagnostics: buildDiagnostics({
analysis,
graph: null,
graphReferenceValidation: null,
}),
analysisErrors: analysis.errors ?? undefined,
rawResponse: analysis.rawResponse ?? undefined,
statusCode: Number(analysis.statusCode) || 502,
};
}
const initialGraph = buildInitialGraph({
reconstruction: analysis.reconstruction,
evidence: analysis.evidence,
});
const currentSummary = describeGraph(initialGraph);
const deterministicSelection = selectActiveUnknownCandidate(
{
...initialGraph,
resolvedNodeIds: [],
},
[],
);
const activeUnknownNodeId =
deterministicSelection?.status === "selected"
? deterministicSelection.nodeId
: null;
const situationGraph = makeGraph({
centralStatement: scenario,
nodes: initialGraph.nodes,
edges: initialGraph.edges,
activeUnknownNodeId,
resolvedNodeIds: [],
currentSummary,
reasoningState: buildReasoningState({
centralStatement: scenario,
nodes: initialGraph.nodes,
edges: initialGraph.edges,
resolvedNodeIds: [],
}),
});
situationGraphSchema.parse(situationGraph);
const graphReferenceValidation = validateGraphReferences(situationGraph);
const selectedQuestion =
deterministicSelection?.status === "ambiguous"
? {
id: "q_tie_resolution",
...formulateTieResolutionQuestion({ graph: situationGraph }),
tiedCandidateIds: deterministicSelection.tiedCandidateIds,
}
: (analysis.nextQuestion ?? null);
const unknownSelectionExplanation = buildUnknownSelectionDiagnostics(
situationGraph,
[],
selectedQuestion,
);
if (!graphReferenceValidation.valid) {
return {
success: false,
error: "Situation graph reference validation failed",
diagnostics: buildDiagnostics({
analysis,
graph: situationGraph,
graphReferenceValidation,
unknownSelectionExplanation,
}),
validationErrors: graphReferenceValidation.errors,
statusCode: 500,
};
}
return {
success: true,
situationGraph,
selectedQuestion,
diagnostics: buildDiagnostics({
analysis,
graph: situationGraph,
graphReferenceValidation,
unknownSelectionExplanation,
}),
};
}
export async function updateCase() {
return updateCaseWithDependencies(...arguments);
}
function sanitiseErrorMessage(error, fallbackMessage) {
if (typeof error?.message === "string" && error.message.trim().length > 0) {
return error.message;
}
return fallbackMessage;
}
async function updateCaseWithDependencies(body, dependencies = {}) {
const parsedRequest = updateCaseRequestSchema.safeParse(body);
if (!parsedRequest.success) {
return {
success: false,
stage: "request_validation",
error: "Invalid update-case request",
validationErrors: toValidationErrors(parsedRequest.error),
statusCode: 400,
};
}
const { situationGraph, previousQuestion, answer, promptVersion } =
parsedRequest.data;
const graphSchemaValidation = situationGraphSchema.safeParse(situationGraph);
const graphReferenceValidation = validateGraphReferences(situationGraph);
if (!graphSchemaValidation.success || !graphReferenceValidation.valid) {
return {
success: false,
stage: "graph_validation",
error: "Invalid situation graph",
graphValidationErrors: [
...(!graphSchemaValidation.success
? toValidationErrors(graphSchemaValidation.error)
: []),
...(!graphReferenceValidation.valid
? graphReferenceValidation.errors
: []),
],
statusCode: 400,
};
}
const buildPrompt =
dependencies.buildGraphUpdatePrompt ?? buildGraphUpdatePrompt;
const parseProposal =
dependencies.parseGraphUpdateProposal ?? parseGraphUpdateProposal;
const applyProposalUpdate =
dependencies.applyValidatedProposal ?? applyValidatedProposal;
const shouldApplyProposal = dependencies.applyProposal === true;
let modelName = null;
let rawResponse;
const startedAt = Date.now();
try {
const config = dependencies.config ?? assertConfig();
modelName = config.OLLAMA_MODEL;
const prompt = buildPrompt({
situationGraph,
previousQuestion,
answer,
promptVersion,
});
const provider = dependencies.provider ?? getProvider();
rawResponse = await provider.generateReconstruction(prompt, modelName);
} catch (error) {
return {
success: false,
stage: "provider",
error: "Graph update proposal generation failed",
providerErrors: [
sanitiseErrorMessage(
error,
"Provider failed to generate graph update proposal",
),
],
diagnostics: {
promptVersion: promptVersion ?? null,
modelName,
responseDurationMs: Date.now() - startedAt,
normalisationsApplied: [],
},
statusCode: 502,
};
}
const parsedProposal = parseProposal(rawResponse);
const responseDurationMs = Date.now() - startedAt;
if (!parsedProposal.success) {
return {
success: false,
stage: "proposal_validation",
error: "Invalid graph update proposal",
proposalErrors: parsedProposal.errors,
diagnostics: {
promptVersion: promptVersion ?? null,
modelName,
responseDurationMs,
normalisationsApplied: parsedProposal.normalisationsApplied,
},
statusCode: 502,
};
}
if (shouldApplyProposal) {
const applicationResult = applyProposalUpdate({
situationGraph,
proposal: parsedProposal.proposal,
previousQuestion,
answer,
});
if (!applicationResult.success) {
return {
success: false,
stage: applicationResult.stage,
errors: applicationResult.errors,
diagnostics: {
...buildUpdateDiagnostics({
promptVersion,
modelName,
responseDurationMs,
normalisationsApplied: parsedProposal.normalisationsApplied,
graph: situationGraph,
graphReferenceValidation: graphReferenceValidation,
selectedQuestion: null,
previousReasoningState: buildReasoningState(situationGraph),
reasoningState: buildReasoningState(situationGraph),
resolvedReasoningNodeIds: [],
emergentReasoningNodeCreated: false,
emergentReasoningNodeId: null,
emergentReasoningNodeReason: null,
atomicityAssessment: null,
atomicityDecisionReason: null,
decompositionDepth: 0,
decompositionAttempted: false,
decompositionAccepted: false,
decompositionStoppedReason: null,
proposedChildCount: 0,
acceptedChildCount: 0,
rejectedChildren: [],
selectedChildNodeId: null,
childQualitySummary: [],
propagationPerformed: false,
resolvedChildNodeId: null,
parentNodeId: null,
parentStatusBefore: null,
parentStatusAfter: null,
parentConfidenceBefore: null,
parentConfidenceAfter: null,
evidenceConfidenceBefore: null,
evidenceConfidenceAfter: null,
completenessBefore: null,
completenessAfter: null,
conclusionConfidenceBefore: null,
conclusionConfidenceAfter: null,
resolvedDirectChildren: 0,
unresolvedDirectChildren: 0,
contradictoryDirectChildren: 0,
corroboratingBranchCount: 0,
conflictingBranchCount: 0,
duplicateEvidenceCount: 0,
independentBranchCount: 0,
interactionSummary: null,
confidenceCapReason: null,
ancestorPropagationStoppedReason: null,
affectedAncestorIds: [],
nextSelectedSibling: null,
parentResolved: false,
decompositionPerformed: false,
childUnknownCount: 0,
childNodeIds: [],
atomicityReason: null,
unknownSelectionExplanation: explainUnknownSelection(
situationGraph,
situationGraph.resolvedNodeIds || [],
),
}),
},
statusCode:
applicationResult.stage === "application" ||
applicationResult.stage === "result_validation"
? 500
: 400,
};
}
return {
success: true,
stage: "update_applied",
updatedSituationGraph: applicationResult.updatedSituationGraph,
proposal: applicationResult.graphUpdate,
selectedQuestion: applicationResult.selectedQuestion,
affectedNodeIds: applicationResult.affectedNodeIds,
resolvedUnknownNodeIds: applicationResult.resolvedUnknownNodeIds,
previousActiveUnknownNodeId:
applicationResult.previousActiveUnknownNodeId,
newActiveUnknownNodeId: applicationResult.newActiveUnknownNodeId,
changesApplied: applicationResult.changesApplied,
diagnostics: buildUpdateDiagnostics({
promptVersion,
modelName,
responseDurationMs,
normalisationsApplied: parsedProposal.normalisationsApplied,
graph: applicationResult.updatedSituationGraph,
graphReferenceValidation: applicationResult.graphReferenceValidation,
selectedQuestion: applicationResult.selectedQuestion,
previousReasoningState: applicationResult.previousReasoningState,
reasoningState: applicationResult.reasoningState,
resolvedReasoningNodeIds: applicationResult.resolvedReasoningNodeIds,
emergentReasoningNodeCreated:
applicationResult.emergentReasoningNodeCreated,
emergentReasoningNodeId: applicationResult.emergentReasoningNodeId,
emergentReasoningNodeReason:
applicationResult.emergentReasoningNodeReason,
atomicityAssessment: applicationResult.atomicityAssessment,
atomicityDecisionReason: applicationResult.atomicityDecisionReason,
decompositionDepth: applicationResult.decompositionDepth,
decompositionAttempted: applicationResult.decompositionAttempted,
decompositionAccepted: applicationResult.decompositionAccepted,
decompositionStoppedReason:
applicationResult.decompositionStoppedReason,
proposedChildCount: applicationResult.proposedChildCount,
acceptedChildCount: applicationResult.acceptedChildCount,
rejectedChildren: applicationResult.rejectedChildren,
selectedChildNodeId: applicationResult.selectedChildNodeId,
childQualitySummary: applicationResult.childQualitySummary,
propagationPerformed: applicationResult.propagationPerformed,
resolvedChildNodeId: applicationResult.resolvedChildNodeId,
parentNodeId: applicationResult.parentNodeId,
parentStatusBefore: applicationResult.parentStatusBefore,
parentStatusAfter: applicationResult.parentStatusAfter,
parentConfidenceBefore: applicationResult.parentConfidenceBefore,
parentConfidenceAfter: applicationResult.parentConfidenceAfter,
evidenceConfidenceBefore: applicationResult.evidenceConfidenceBefore,
evidenceConfidenceAfter: applicationResult.evidenceConfidenceAfter,
completenessBefore: applicationResult.completenessBefore,
completenessAfter: applicationResult.completenessAfter,
conclusionConfidenceBefore:
applicationResult.conclusionConfidenceBefore,
conclusionConfidenceAfter: applicationResult.conclusionConfidenceAfter,
resolvedDirectChildren: applicationResult.resolvedDirectChildren,
unresolvedDirectChildren: applicationResult.unresolvedDirectChildren,
contradictoryDirectChildren:
applicationResult.contradictoryDirectChildren,
corroboratingBranchCount: applicationResult.corroboratingBranchCount,
conflictingBranchCount: applicationResult.conflictingBranchCount,
duplicateEvidenceCount: applicationResult.duplicateEvidenceCount,
independentBranchCount: applicationResult.independentBranchCount,
interactionSummary: applicationResult.interactionSummary,
confidenceCapReason: applicationResult.confidenceCapReason,
ancestorPropagationStoppedReason:
applicationResult.ancestorPropagationStoppedReason,
affectedAncestorIds: applicationResult.affectedAncestorIds,
nextSelectedSibling: applicationResult.nextSelectedSibling,
parentResolved: applicationResult.parentResolved,
decompositionPerformed: applicationResult.decompositionPerformed,
childUnknownCount: applicationResult.childUnknownCount,
childNodeIds: applicationResult.childNodeIds,
atomicityReason: applicationResult.atomicityReason,
unknownSelectionExplanation: buildUnknownSelectionDiagnostics(
applicationResult.updatedSituationGraph,
applicationResult.updatedSituationGraph.resolvedNodeIds || [],
applicationResult.selectedQuestion,
),
}),
};
}
return {
success: true,
stage: "proposal_ready",
proposal: parsedProposal.proposal,
diagnostics: buildUpdateDiagnostics({
promptVersion,
modelName,
responseDurationMs,
normalisationsApplied: parsedProposal.normalisationsApplied,
graph: situationGraph,
graphReferenceValidation,
selectedQuestion: null,
previousReasoningState: buildReasoningState(situationGraph),
reasoningState: buildReasoningState(situationGraph),
resolvedReasoningNodeIds: [],
emergentReasoningNodeCreated: false,
emergentReasoningNodeId: null,
emergentReasoningNodeReason: null,
atomicityAssessment: null,
atomicityDecisionReason: null,
decompositionDepth: 0,
decompositionAttempted: false,
decompositionAccepted: false,
decompositionStoppedReason: null,
proposedChildCount: 0,
acceptedChildCount: 0,
rejectedChildren: [],
selectedChildNodeId: null,
childQualitySummary: [],
propagationPerformed: false,
resolvedChildNodeId: null,
parentNodeId: null,
parentStatusBefore: null,
parentStatusAfter: null,
parentConfidenceBefore: null,
parentConfidenceAfter: null,
evidenceConfidenceBefore: null,
evidenceConfidenceAfter: null,
completenessBefore: null,
completenessAfter: null,
conclusionConfidenceBefore: null,
conclusionConfidenceAfter: null,
resolvedDirectChildren: 0,
unresolvedDirectChildren: 0,
contradictoryDirectChildren: 0,
corroboratingBranchCount: 0,
conflictingBranchCount: 0,
duplicateEvidenceCount: 0,
independentBranchCount: 0,
interactionSummary: null,
confidenceCapReason: null,
ancestorPropagationStoppedReason: null,
affectedAncestorIds: [],
nextSelectedSibling: null,
parentResolved: false,
decompositionPerformed: false,
childUnknownCount: 0,
childNodeIds: [],
atomicityReason: null,
unknownSelectionExplanation: buildUnknownSelectionDiagnostics(
situationGraph,
situationGraph.resolvedNodeIds || [],
null,
),
}),
};
}
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import {
ConfidenceLevel,
SituationKind,
SituationRelationship,
SituationStatus,
} from "./schema.js";
const DEFAULT_PROMPT_VERSION = "v0.4";
function formatEnumValues(values) {
return Object.values(values).join(" | ");
}
function formatGraph(graph) {
return JSON.stringify(graph, null, 2);
}
function formatExampleAnswerBlock() {
return [
"Example answer the model must be able to handle without hard-coding output:",
'"The complaint rate fell from 2.0 complaints per 100 units to 1.9 complaints per 100 units."',
"This may justify resolving a rate-related unknown or updating a metric node, but only if the current graph and answer support that proposal.",
].join("\n");
}
export function buildGraphUpdatePrompt({
situationGraph,
previousQuestion,
answer,
promptVersion = DEFAULT_PROMPT_VERSION,
}) {
const nodeKinds = formatEnumValues(SituationKind);
const nodeStatuses = formatEnumValues(SituationStatus);
const edgeRelationships = formatEnumValues(SituationRelationship);
const confidenceLevels = formatEnumValues(ConfidenceLevel);
return `You are proposing a graph update for Confidence Engine ${promptVersion}.
Return exactly one JSON object matching the GraphUpdate contract.
Return JSON only. Do not include markdown, explanation, or any text before or after the JSON object.
## Current Situation Graph
${formatGraph(situationGraph)}
## Previous Selected Question
${previousQuestion}
## User Answer
${answer}
## Allowed Node Kinds
${nodeKinds}
## Allowed Node Statuses
${nodeStatuses}
## Allowed Edge Relationships
${edgeRelationships}
## Allowed Confidence Values
${confidenceLevels}
## Required JSON Field Names
The JSON object must contain exactly these top-level fields:
- addedNodes
- updatedNodes
- addedEdges
- removedEdgeIds
- resolvedUnknownNodeIds
- affectedNodeIds
- selectedQuestion
## Required Shapes
- addedNodes: array of nodes using these exact keys:
id, label, description, kind, status, confidence, value, unit, evidenceIds, dependsOn, affects, parentId, childIds
- updatedNodes: array of node updates using these exact keys:
nodeId, previousStatus, newStatus, previousValue, newValue, reason
- addedEdges: array of edges using these exact keys:
id, fromNodeId, toNodeId, relationship, confidence, description
- removedEdgeIds: array of strings
- resolvedUnknownNodeIds: array of strings
- affectedNodeIds: array of strings
- selectedQuestion: either null or an object using these exact keys:
nodeId, question, reason
## Proposal Rules
1. Propose changes only. Never return a replacement graph.
2. Preserve unrelated nodes and edges by omitting them from the proposal.
3. Reference existing node IDs when updating an existing concept.
4. Use addedNodes only for genuinely new concepts.
5. Resolve the answered unknown first when the answer supports it.
6. Then inspect the answer for newly introduced consequential uncertainty.
7. Add new unknown nodes only when the answer introduces a new decision, claim, object, measure, dependency, or unresolved term directly relevant to the case.
8. Add at most 3 new unknown nodes.
9. Every new unknown must be directly traceable to the user's answer and its description must state why that uncertainty matters.
9a. In the description of every new unknown, explicitly include a short why-it-matters clause using wording such as because, so that, needed to decide, or matters because.
10. Do not add broad generic discovery questions.
11. Do not add duplicate unknowns.
12. Do not expand unrelated branches.
13. Propagate only through explicit dependencies or relationships already present in the graph, except for the minimal new edges needed to connect validated new unknowns to the relevant answer-derived decision or context node.
13a. For every new unknown node, include at least one added edge that connects it to an existing updated/resolved node or to a newly added non-unknown node introduced from the answer.
14. Do not invent evidence.
15. Do not create unsupported causal edges.
16. If consequential unresolved unknowns exist, selectedQuestion may identify one valid candidate unknown, but the engine will deterministically choose final priority after validation.
17. selectedQuestion.nodeId must reference an unresolved unknown node that exists either already in the graph or in addedNodes.
18. selectedQuestion.question must be one narrow non-compound question about that one unknown.
19. Do not prioritise downstream implementation, pricing, optimisation, or speculative branches ahead of prerequisite definitions, actors, success criteria, constraints, measures, or terminology.
20. Return selectedQuestion as null only when no consequential unresolved unknown remains.
21. Use empty arrays when there are no changes in a category.
22. Never return null array entries.
23. Never use unknown enum values.
24. Do not change existing IDs.
25. Do not replace the whole graph, and do not restate unchanged graph content inside the proposal.
## Additional Guidance
- If the answer only clarifies an existing unknown, prefer updatedNodes and resolvedUnknownNodeIds over creating duplicate nodes.
- When an answer resolves an existing unknown, include that existing node ID in resolvedUnknownNodeIds and update that node rather than creating only a parallel observation.
- If the answer creates a more specific decision situation, add the smallest set of new nodes and edges needed to represent that situation and only its most consequential unknowns.
- If you add a new unknown, do not leave it floating: connect it with an added edge to the relevant decision/context node created or updated from the answer.
- If you add a new unknown, its description must do two jobs in one sentence: what is unknown, and why resolving it matters for the case.
- Treat selectedQuestion as a candidate only; the engine will apply deterministic information-value scoring after validation.
- If the answer does not justify a change, return empty arrays for every category.
## Example Constraint Reminder
${formatExampleAnswerBlock()}
## Output Contract Reminder
Return one JSON object only, with exact field names and exact enum values.
Never include a full graph.
Never include any field other than the contract fields above.
`;
}
export const buildUpdatePrompt = buildGraphUpdatePrompt;
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/**
* Situation Graph schema v0.4 experiment.
* Defines types for an evolving multi-turn situation reconstruction graph.
* Plain TypeScript interfaces implemented as Zod schemas for runtime validation.
*/
import { z } from "zod";
// ── Enums ────────────────────────────────────────────
export const SituationKind = /** @type {const} */ ({
observation: "observation",
reported_claim: "reported_claim",
metric: "metric",
state: "state",
transition: "transition",
relationship: "relationship",
assumption: "assumption",
unknown: "unknown",
conclusion: "conclusion",
});
export const SituationStatus = /** @type {const} */ ({
known: "known",
unknown: "unknown",
provisional: "provisional",
supported: "supported",
weakened: "weakened",
contradicted: "contradicted",
resolved: "resolved",
});
export const ConfidenceLevel = /** @type {const} */ ({
low: "low",
medium: "medium",
high: "high",
});
export const CompletenessStatus = /** @type {const} */ ({
empty: "empty",
partial: "partial",
complete: "complete",
});
export const confidenceAssessmentSchema = z
.object({
evidenceConfidence: z.enum(Object.values(ConfidenceLevel)),
completenessStatus: z.enum(Object.values(CompletenessStatus)),
conclusionConfidence: z.enum(Object.values(ConfidenceLevel)),
})
.strict();
// ── SituationNode ────────────────────────────────────
export const situationNodeSchema = z.object({
id: z.string().min(1),
label: z.string().min(1),
description: z.string().min(1),
kind: z.enum(Object.values(SituationKind)),
status: z.enum(Object.values(SituationStatus)),
confidence: z.enum(Object.values(ConfidenceLevel)),
confidenceAssessment: confidenceAssessmentSchema.optional(),
value: z.union([z.string(), z.number(), z.null()]).nullable().optional(),
unit: z.string().nullable().optional(),
evidenceIds: z.array(z.string()).default([]),
dependsOn: z.array(z.string()).default([]),
affects: z.array(z.string()).default([]),
parentId: z.string().nullable().optional(),
childIds: z.array(z.string()).default([]),
});
/** @typedef {z.infer<typeof situationNodeSchema>} SituationNode */
// ── SituationEdge ────────────────────────────────────
export const SituationRelationship = /** @type {const} */ ({
supports: "supports",
weakens: "weakens",
contradicts: "contradicts",
depends_on: "depends_on",
causes: "causes",
may_cause: "may_cause",
measures: "measures",
compares_with: "compares_with",
updates: "updates",
other: "other",
});
export const situationEdgeSchema = z.object({
id: z.string().min(1),
fromNodeId: z.string().min(1),
toNodeId: z.string().min(1),
relationship: z.enum(Object.values(SituationRelationship)),
confidence: z.enum(Object.values(ConfidenceLevel)),
description: z.string().min(1),
});
/** @typedef {z.infer<typeof situationEdgeSchema>} SituationEdge */
// ── SituationGraph ───────────────────────────────────
const reasoningStageSchema = z.object({
stage: z.string().min(1),
status: z.string().min(1),
outcome: z.string().min(1),
});
export const reasoningStateSchema = z
.object({
comparabilityStatus: z.string().min(1).nullable().optional(),
comparabilityReason: z.string().min(1).nullable().optional(),
comparabilityEvidence: z.array(z.string()).default([]),
relationshipStatus: z.string().min(1).nullable().optional(),
relationshipReason: z.string().min(1).nullable().optional(),
relationshipAssessed: z.boolean().optional(),
contradictionReasoningAllowed: z.boolean().optional(),
reasoningStages: z.array(reasoningStageSchema).default([]),
})
.strict();
export const situationGraphSchema = z.object({
centralStatement: z.string().min(1),
nodes: z.array(situationNodeSchema).min(1),
edges: z.array(situationEdgeSchema).default([]),
activeUnknownNodeId: z.string().nullable(),
resolvedNodeIds: z.array(z.string()).default([]),
currentSummary: z.string().min(1),
reasoningState: reasoningStateSchema.optional(),
});
/** @typedef {z.infer<typeof situationGraphSchema>} SituationGraph */
// ── GraphUpdate (change set) ────────────────────────
const graphUpdateNodeChangeSchema = z.object({
nodeId: z.string().min(1),
previousStatus: z.enum(Object.values(SituationStatus)).nullable().optional(),
newStatus: z.enum(Object.values(SituationStatus)).nullable().optional(),
previousValue: z
.union([z.string(), z.number(), z.null()])
.nullable()
.optional(),
newValue: z.union([z.string(), z.number(), z.null()]).nullable().optional(),
reason: z.string().min(1),
});
export const selectedQuestionSchema = z
.object({
nodeId: z.string().min(1),
question: z.string().min(1),
reason: z.string().min(1),
})
.strict();
export const graphUpdateSchema = z.object({
addedNodes: z.array(situationNodeSchema).default([]),
updatedNodes: z.array(graphUpdateNodeChangeSchema).default([]),
addedEdges: z.array(situationEdgeSchema).default([]),
removedEdgeIds: z.array(z.string()).default([]),
resolvedUnknownNodeIds: z.array(z.string()).default([]),
affectedNodeIds: z.array(z.string()).default([]),
selectedQuestion: selectedQuestionSchema.nullable().default(null),
});
/** @typedef {z.infer<typeof graphUpdateSchema>} GraphUpdate */
// ── API request / response schemas ───────────────────
export const startCaseRequestSchema = z.object({
scenario: z.string().min(1).max(10000),
promptVersion: z.string().optional(),
});
export const updateCaseRequestSchema = z.object({
situationGraph: situationGraphSchema,
previousQuestion: z.string().min(1),
answer: z.string().min(1).max(5000),
promptVersion: z.string().optional(),
});
// ── Helpers ──────────────────────────────────────────
/** Generate a short deterministic ID from a label */
export function makeNodeId(label) {
return "n" + Math.abs(hashString(label)).toString(36).slice(0, 7);
}
function hashString(str) {
let h = 0;
for (let i = 0; i < str.length; i++) {
h = (Math.imul(31, h) + str.charCodeAt(i)) | 0;
}
return h;
}
/** Create a minimal valid node — used in tests and fixtures */
export function makeNode(opts) {
const id = opts.id || makeNodeId(opts.label);
return situationNodeSchema.parse({
id,
label: opts.label,
description: opts.description ?? opts.label,
kind: opts.kind ?? "observation",
status: opts.status ?? "unknown",
confidence: opts.confidence ?? "medium",
confidenceAssessment: opts.confidenceAssessment,
value: opts.value ?? null,
unit: opts.unit ?? null,
evidenceIds: opts.evidenceIds ?? [],
dependsOn: opts.dependsOn ?? [],
affects: opts.affects ?? [],
parentId: opts.parentId ?? null,
childIds: opts.childIds ?? [],
});
}
/** Create a minimal valid edge — used in tests and fixtures */
export function makeEdge(opts) {
return situationEdgeSchema.parse({
id:
opts.id ||
"e" + opts.fromNodeId.slice(0, 3) + "-" + opts.toNodeId.slice(0, 3),
fromNodeId: opts.fromNodeId,
toNodeId: opts.toNodeId,
relationship: opts.relationship ?? "supports",
confidence: opts.confidence ?? "medium",
description: opts.description ?? opts.fromNodeId + " -> " + opts.toNodeId,
});
}
/** Build a minimal valid graph structure */
export function makeGraph(opts) {
return situationGraphSchema.parse({
centralStatement: opts.centralStatement || "",
nodes: opts.nodes ?? [],
edges: opts.edges ?? [],
activeUnknownNodeId: opts.activeUnknownNodeId ?? null,
resolvedNodeIds: opts.resolvedNodeIds ?? [],
currentSummary: opts.currentSummary || "",
reasoningState: opts.reasoningState,
});
}
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import { graphUpdateSchema } from "./schema.js";
const TOP_LEVEL_ARRAY_FIELDS = [
"addedNodes",
"updatedNodes",
"addedEdges",
"removedEdgeIds",
"resolvedUnknownNodeIds",
"affectedNodeIds",
];
const TOP_LEVEL_NULLABLE_FIELDS = ["selectedQuestion"];
function cloneJsonSafe(value) {
if (value == null) return value;
return JSON.parse(JSON.stringify(value));
}
function removeNullArrayEntries(value, path = [], normalisationsApplied = []) {
if (Array.isArray(value)) {
const filtered = [];
value.forEach((item, index) => {
if (item === null) {
normalisationsApplied.push({
path: [...path, index],
change: "Removed null array entry",
});
return;
}
filtered.push(
removeNullArrayEntries(item, [...path, index], normalisationsApplied),
);
});
return filtered;
}
if (value && typeof value === "object") {
return Object.fromEntries(
Object.entries(value).map(([key, child]) => [
key,
removeNullArrayEntries(child, [...path, key], normalisationsApplied),
]),
);
}
return value;
}
function applyKnownEnumAliases(proposal, normalisationsApplied) {
if (!proposal || typeof proposal !== "object") return proposal;
if (Array.isArray(proposal.addedNodes)) {
proposal.addedNodes = proposal.addedNodes.map((node, index) => {
if (node?.kind === "reported_statement") {
normalisationsApplied.push({
path: ["addedNodes", index, "kind"],
change: "Converted reported_statement to reported_claim",
});
return { ...node, kind: "reported_claim" };
}
return node;
});
}
return proposal;
}
function fillMissingOptionalArrays(proposal, normalisationsApplied) {
if (!proposal || typeof proposal !== "object") return proposal;
for (const field of TOP_LEVEL_ARRAY_FIELDS) {
if (!(field in proposal)) {
proposal[field] = [];
normalisationsApplied.push({
path: [field],
change: "Filled missing optional array with []",
});
}
}
return proposal;
}
function fillMissingNullableFields(proposal, normalisationsApplied) {
if (!proposal || typeof proposal !== "object") return proposal;
for (const field of TOP_LEVEL_NULLABLE_FIELDS) {
if (!(field in proposal)) {
proposal[field] = null;
normalisationsApplied.push({
path: [field],
change: "Filled missing optional nullable field with null",
});
}
}
return proposal;
}
export function parseGraphUpdateProposal(rawResponse) {
const raw = rawResponse;
let parsed;
if (typeof rawResponse === "string") {
try {
parsed = JSON.parse(rawResponse);
} catch (error) {
return {
success: false,
proposal: null,
raw,
normalisationsApplied: [],
errors: [error.message || "Model response is not valid JSON"],
};
}
} else if (rawResponse && typeof rawResponse === "object") {
parsed = cloneJsonSafe(rawResponse);
} else {
return {
success: false,
proposal: null,
raw,
normalisationsApplied: [],
errors: ["Graph update proposal must be a JSON object or JSON string"],
};
}
const normalisationsApplied = [];
let normalised = removeNullArrayEntries(parsed, [], normalisationsApplied);
normalised = applyKnownEnumAliases(normalised, normalisationsApplied);
normalised = fillMissingOptionalArrays(normalised, normalisationsApplied);
normalised = fillMissingNullableFields(normalised, normalisationsApplied);
const parsedProposal = graphUpdateSchema.safeParse(normalised);
if (!parsedProposal.success) {
return {
success: false,
proposal: null,
raw,
normalisationsApplied,
errors: parsedProposal.error.issues.map((issue) => ({
path: issue.path,
message: issue.message,
code: issue.code,
})),
};
}
return {
success: true,
proposal: parsedProposal.data,
raw,
normalisationsApplied,
errors: [],
};
}
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/**
* Deterministic graph utilities for situation graph operations.
* These functions perform safe, validated operations on the graph.
* The LLM should never directly modify the graph it proposes changes,
* and these utilities apply them safely.
*/
import {
situationNodeSchema,
situationEdgeSchema,
situationGraphSchema,
} from "./schema.js";
function normaliseText(value) {
return String(value || "")
.toLowerCase()
.replace(/[^a-z0-9]+/g, " ")
.trim();
}
function collectNodeText(node) {
return `${node?.label || ""} ${node?.description || ""}`.trim();
}
function countIncomingUnknownDependencies(graph, nodeId, resolvedNodeIds) {
const resolvedSet = new Set(resolvedNodeIds || []);
const nodesById = new Map(graph.nodes.map((node) => [node.id, node]));
const incoming = new Set();
for (const dependencyId of nodesById.get(nodeId)?.dependsOn || []) {
const dependencyNode = nodesById.get(dependencyId);
if (dependencyNode?.kind === "unknown" && !resolvedSet.has(dependencyId)) {
incoming.add(dependencyId);
}
}
for (const edge of graph.edges) {
if (edge.toNodeId !== nodeId) continue;
const dependencyNode = nodesById.get(edge.fromNodeId);
if (
dependencyNode?.kind === "unknown" &&
!resolvedSet.has(edge.fromNodeId)
) {
incoming.add(edge.fromNodeId);
}
}
return incoming.size;
}
function classifyUnknownPriority(text) {
const normalised = normaliseText(text);
const matches = {
objective:
/\b(objective|goal|outcome|value|problem|job to be done|benefit|commercial value)\b/.test(
normalised,
),
actor:
/\b(customer|user|buyer|actor|stakeholder|audience|recipient)\b/.test(
normalised,
),
criteria:
/\b(success criteria|success threshold|threshold|decision criteria|criterion|justify|sufficient)\b/.test(
normalised,
),
measure:
/\b(metric|measure|measurable|roi|demand|evidence|signal|proof)\b/.test(
normalised,
),
terminology: /\b(define|definition|meaning|means|term|terminology)\b/.test(
normalised,
),
constraint:
/\b(constraint|limit|budget|deadline|requirement|regulation)\b/.test(
normalised,
),
pricing: /\b(price|pricing|price point|subscription|charge|pay for)\b/.test(
normalised,
),
implementation:
/\b(implementation|build approach|architecture|stack|feature|technical design)\b/.test(
normalised,
),
optimisation:
/\b(optimisation|optimi[sz]ation|improve|efficiency|performance|scale)\b/.test(
normalised,
),
speculative:
/\b(maybe|possible|optional|future branch|nice to have|slogan|colour|color|ui)\b/.test(
normalised,
),
};
return matches;
}
function buildScoreContributions(
matches,
downstreamCount,
unresolvedParentUnknownCount,
) {
const contributions = [
{
rule: "downstream_dependencies",
value: downstreamCount,
weight: 4,
delta: downstreamCount * 4,
},
];
if (matches.objective) {
contributions.push({
rule: "objective_match",
value: true,
weight: 12,
delta: 12,
});
}
if (matches.actor) {
contributions.push({
rule: "actor_match",
value: true,
weight: 10,
delta: 10,
});
}
if (matches.criteria) {
contributions.push({
rule: "criteria_match",
value: true,
weight: 11,
delta: 11,
});
}
if (matches.measure) {
contributions.push({
rule: "measure_match",
value: true,
weight: 8,
delta: 8,
});
}
if (matches.terminology) {
contributions.push({
rule: "terminology_match",
value: true,
weight: 7,
delta: 7,
});
}
if (matches.constraint) {
contributions.push({
rule: "constraint_match",
value: true,
weight: 9,
delta: 9,
});
}
if (matches.pricing) {
contributions.push({
rule: "pricing_penalty",
value: true,
weight: -8,
delta: -8,
});
}
if (matches.implementation) {
contributions.push({
rule: "implementation_penalty",
value: true,
weight: -10,
delta: -10,
});
}
if (matches.optimisation) {
contributions.push({
rule: "optimisation_penalty",
value: true,
weight: -9,
delta: -9,
});
}
if (matches.speculative) {
contributions.push({
rule: "speculative_penalty",
value: true,
weight: -12,
delta: -12,
});
}
if (
matches.pricing &&
!matches.objective &&
!matches.criteria &&
!matches.actor
) {
contributions.push({
rule: "isolated_pricing_penalty",
value: true,
weight: -6,
delta: -6,
});
}
if (unresolvedParentUnknownCount > 0) {
contributions.push({
rule: "unresolved_prerequisite_penalty",
value: unresolvedParentUnknownCount,
weight: -7,
delta: unresolvedParentUnknownCount * -7,
});
}
return contributions;
}
function getMeaningfulSemanticContributions(contributions = []) {
return contributions
.filter(
(contribution) =>
contribution.rule !== "downstream_dependencies" &&
contribution.rule !== "unresolved_prerequisite_penalty" &&
contribution.delta !== 0,
)
.map((contribution) => ({
rule: contribution.rule,
delta: contribution.delta,
}));
}
function buildCandidateDisplayOrder(candidates) {
return [...candidates].sort((a, b) => {
if (b.score !== a.score) return b.score - a.score;
if (b.downstreamCount !== a.downstreamCount) {
return b.downstreamCount - a.downstreamCount;
}
if (a.unresolvedParentUnknownCount !== b.unresolvedParentUnknownCount) {
return a.unresolvedParentUnknownCount - b.unresolvedParentUnknownCount;
}
return a.label.localeCompare(b.label);
});
}
function semanticSignature(candidate) {
return JSON.stringify(
getMeaningfulSemanticContributions(candidate.contributions),
);
}
function classifyCandidateOrdering(candidates) {
const displayOrder = buildCandidateDisplayOrder(candidates);
const best = displayOrder[0] ?? null;
if (!best) {
return {
displayOrder,
best: null,
leadingCandidates: [],
status: "no_candidates",
tieType: "none",
usedAlphabeticalOrdering: false,
reason: "No unresolved unknown candidates remain.",
};
}
const topScoreCandidates = displayOrder.filter(
(candidate) => candidate.score === best.score,
);
if (topScoreCandidates.length === 1) {
return {
displayOrder,
best,
leadingCandidates: [best],
status: "selected",
tieType: "none",
usedAlphabeticalOrdering: false,
reason: `Clear winner by total score (${best.score}).`,
};
}
const topStructuralCandidates = topScoreCandidates.filter(
(candidate) =>
candidate.downstreamCount === best.downstreamCount &&
candidate.unresolvedParentUnknownCount ===
best.unresolvedParentUnknownCount,
);
if (topStructuralCandidates.length === 1) {
return {
displayOrder,
best,
leadingCandidates: [best],
status: "selected",
tieType: "structural_tie",
usedAlphabeticalOrdering: false,
reason:
"Score tie was resolved by downstream dependency count or prerequisite ordering.",
};
}
const topSemanticSignature = semanticSignature(best);
const semanticPeers = topStructuralCandidates.filter(
(candidate) => semanticSignature(candidate) === topSemanticSignature,
);
if (semanticPeers.length !== topStructuralCandidates.length) {
return {
displayOrder,
best: null,
leadingCandidates: topStructuralCandidates,
status: "ambiguous",
tieType: "semantic_tie",
usedAlphabeticalOrdering: false,
reason:
"Leading candidates remain tied after score and structural checks, but differ in semantic contribution patterns.",
};
}
return {
displayOrder,
best: null,
leadingCandidates: topStructuralCandidates,
status: "ambiguous",
tieType: "complete_unresolved_tie",
usedAlphabeticalOrdering: false,
reason: "No justified distinction between leading unknowns.",
};
}
export function scoreUnknownCandidate(graph, node, resolvedNodeIds = []) {
const text = collectNodeText(node);
const matches = classifyUnknownPriority(text);
const downstreamCount = findDependentNodes(graph, node.id).length;
const unresolvedParentUnknownCount = countIncomingUnknownDependencies(
graph,
node.id,
resolvedNodeIds,
);
const contributions = buildScoreContributions(
matches,
downstreamCount,
unresolvedParentUnknownCount,
);
const score = contributions.reduce(
(total, contribution) => total + contribution.delta,
0,
);
return {
nodeId: node.id,
label: node.label,
score,
downstreamCount,
unresolvedParentUnknownCount,
matches,
contributions,
};
}
export function buildDeterministicQuestionForUnknown(node) {
const text = normaliseText(collectNodeText(node));
if (
/\b(success criteria|success threshold|threshold|decision criteria|criterion)\b/.test(
text,
)
) {
return `What outcome would define success for ${node.label}?`;
}
if (
/\b(customer|user|buyer|actor|stakeholder|audience|recipient)\b/.test(text)
) {
return `Who is the key actor or customer for ${node.label}?`;
}
if (
/\b(define|definition|meaning|means|term|terminology|value)\b/.test(text)
) {
return `How should ${node.label} be defined for this decision?`;
}
if (
/\b(metric|measure|measurable|roi|demand|evidence|signal|proof)\b/.test(
text,
)
) {
return `What evidence or measure would resolve ${node.label}?`;
}
return `What would resolve ${node.label}?`;
}
// ── Validate that all edge references point to existing nodes ──
export function validateGraphReferences(graph) {
const errors = [];
const nodeIds = new Set(graph.nodes.map((n) => n.id));
for (const node of graph.nodes) {
if (node.parentId !== null && !nodeIds.has(node.parentId)) {
errors.push(
`Node "${node.id}" references parentId "${node.parentId}" which does not exist`,
);
}
for (const cid of node.childIds) {
if (!nodeIds.has(cid)) {
errors.push(
`Node "${node.id}" references childIds "${cid}" which does not exist`,
);
}
}
for (const dep of node.dependsOn) {
if (!nodeIds.has(dep)) {
errors.push(
`Node "${node.id}" depends on "${dep}" which does not exist`,
);
}
}
for (const aff of node.affects) {
if (!nodeIds.has(aff)) {
errors.push(`Node "${node.id}" affects "${aff}" which does not exist`);
}
}
}
for (const edge of graph.edges) {
if (!nodeIds.has(edge.fromNodeId)) {
errors.push(
`Edge "${edge.id}" references non-existent fromNodeId "${edge.fromNodeId}"`,
);
}
if (!nodeIds.has(edge.toNodeId)) {
errors.push(
`Edge "${edge.id}" references non-existent toNodeId "${edge.toNodeId}"`,
);
}
}
return { valid: errors.length === 0, errors };
}
// ── Detect duplicate node IDs ──
export function detectDuplicateNodeIds(nodes) {
const countMap = new Map();
const seen = new Set();
for (const node of nodes) {
if (countMap.has(node.id)) {
countMap.set(node.id, countMap.get(node.id) + 1);
} else {
countMap.set(node.id, 1);
}
}
const duplicates = [];
for (const [id, count] of countMap.entries()) {
if (count > 1 && !seen.has(id)) {
duplicates.push({ nodeId: id, count });
seen.add(id);
}
}
return duplicates;
}
// ── Detect duplicate edges ──
export function detectDuplicateEdges(edges) {
const seen = new Set();
const duplicates = [];
for (const edge of edges) {
const key = `${edge.fromNodeId}->${edge.toNodeId}:${edge.relationship}`;
if (seen.has(key)) {
duplicates.push({
edgeId: edge.id,
fromNodeId: edge.fromNodeId,
toNodeId: edge.toNodeId,
relationship: edge.relationship,
});
}
seen.add(key);
}
return duplicates;
}
// ── Find all nodes that depend on a given node (transitive) ──
export function findDependentNodes(graph, nodeId) {
const direct = graph.nodes
.filter((n) => n.dependsOn.includes(nodeId))
.map((n) => n.id);
const affected = new Set(direct);
// Also propagate through edges where the relationship is depends_on
for (const edge of graph.edges) {
if (edge.toNodeId === nodeId && !affected.has(edge.fromNodeId)) {
direct.push(edge.fromNodeId);
affected.add(edge.fromNodeId);
}
}
// Transitive propagation — BFS
const queue = [...direct];
while (queue.length > 0) {
const current = queue.shift();
if (!current || !affected.has(current)) continue;
for (const node of graph.nodes) {
if (node.dependsOn.includes(current) && !affected.has(node.id)) {
affected.add(node.id);
queue.push(node.id);
}
}
}
return [...affected];
}
// ── Find all nodes that are directly or indirectly affected by a change in nodeId ──
export function findAffectedNodes(graph, nodeId) {
// Direct effects: two sources
// 1. Nodes that depend on this node (they list it in their dependsOn)
const directFromDepends = graph.nodes
.filter((n) => n.id !== nodeId && n.dependsOn.includes(nodeId))
.map((n) => n.id);
// 2. Targets of the node's affects relationships (this node directly affects them)
const myAffectedTargets = new Set(
graph.nodes.find((n) => n.id === nodeId)?.affects || [],
);
// Merge: also add edge targets where this node is the source
for (const edge of graph.edges) {
if (edge.fromNodeId === nodeId && !myAffectedTargets.has(edge.toNodeId)) {
myAffectedTargets.add(edge.toNodeId);
}
}
// Combine both sources
const direct = [...new Set([...directFromDepends, ...myAffectedTargets])];
// Transitive propagation — BFS through dependsOn and affects of affected nodes
const affected = new Set(direct);
const queue = [...direct];
while (queue.length > 0) {
const current = queue.shift();
if (!current || !affected.has(current)) continue;
for (const node of graph.nodes) {
if (
node.id !== nodeId &&
!affected.has(node.id) &&
(node.dependsOn.includes(current) || node.affects.includes(current))
) {
affected.add(node.id);
queue.push(node.id);
}
}
}
return [...affected];
}
// ── Resolve an unknown node ──
export function resolveUnknownNode(graph, nodeId, newStatus, newValue, reason) {
const nodeIdx = graph.nodes.findIndex((n) => n.id === nodeId);
if (nodeIdx === -1) {
return { success: false, error: `Node "${nodeId}" not found in graph` };
}
const previousStatus = graph.nodes[nodeIdx].status;
const previousValue = graph.nodes[nodeIdx].value;
return {
success: true,
previousStatus,
newStatus,
previousValue,
newValue,
reason,
affectedNodes: findAffectedNodes(graph, nodeId),
};
}
// ── Select the next highest-value active unknown candidate ──
export function selectActiveUnknownCandidate(graph, resolvedNodeIds) {
// Skip already resolved nodes
const unresolved = graph.nodes.filter(
(n) => n.kind === "unknown" && !resolvedNodeIds.includes(n.id),
);
if (unresolved.length === 0) return null;
const scoredCandidates = unresolved.map((node) => ({
node,
...scoreUnknownCandidate(graph, node, resolvedNodeIds),
}));
const selection = classifyCandidateOrdering(
scoredCandidates.map(({ node, ...candidate }) => ({
...candidate,
node,
})),
);
if (selection.status === "ambiguous") {
return {
selectedNode: null,
status: "ambiguous",
tieType: selection.tieType,
tiedCandidateIds: selection.leadingCandidates.map(
(candidate) => candidate.nodeId,
),
displayOrder: selection.displayOrder.map((candidate) => candidate.nodeId),
reason: selection.reason,
};
}
const best = selection.best;
if (!best) return null;
return {
selectedNode: {
nodeId: best.node.id,
label: best.node.label,
},
status: "selected",
tieType: selection.tieType,
nodeId: best.node.id,
label: best.node.label,
score: best.score,
question: buildDeterministicQuestionForUnknown(best.node),
reason: `Selected for highest information value (score ${best.score}) with ${best.downstreamCount} downstream dependency node(s) and ${best.unresolvedParentUnknownCount} unresolved prerequisite unknown(s).`,
};
}
export function explainUnknownSelection(graph, resolvedNodeIds = []) {
const unresolved = graph.nodes.filter(
(n) => n.kind === "unknown" && !resolvedNodeIds.includes(n.id),
);
if (unresolved.length === 0) {
return {
selectedNodeId: null,
selectedNodeLabel: null,
status: "no_candidates",
tieType: "none",
resolvedNodeIds: [...resolvedNodeIds],
tiedCandidateIds: [],
candidates: [],
competitors: [],
tieBreakOrder: [
"score_desc",
"downstreamCount_desc",
"unresolvedParentUnknownCount_asc",
"label_asc",
],
summary: {
candidateCount: 0,
},
};
}
const candidates = unresolved.map((node) => ({
nodeId: node.id,
label: node.label,
...scoreUnknownCandidate(graph, node, resolvedNodeIds),
}));
const selection = classifyCandidateOrdering(candidates);
const orderedCandidates = selection.displayOrder;
const selected = selection.best;
const competitors = orderedCandidates
.filter((candidate) => candidate.nodeId !== selected?.nodeId)
.map((candidate) => ({
nodeId: candidate.nodeId,
label: candidate.label,
score: candidate.score,
downstreamCount: candidate.downstreamCount,
unresolvedParentUnknownCount: candidate.unresolvedParentUnknownCount,
matches: candidate.matches,
contributions: candidate.contributions,
outrankedBy: {
scoreDelta: (selected?.score ?? candidate.score) - candidate.score,
downstreamDelta:
(selected?.downstreamCount ?? candidate.downstreamCount) -
candidate.downstreamCount,
unresolvedPrerequisiteDelta:
candidate.unresolvedParentUnknownCount -
(selected?.unresolvedParentUnknownCount ??
candidate.unresolvedParentUnknownCount),
labelOrderWinner:
selected &&
selected.score === candidate.score &&
selected.downstreamCount === candidate.downstreamCount &&
selected.unresolvedParentUnknownCount ===
candidate.unresolvedParentUnknownCount
? selected.label.localeCompare(candidate.label) <= 0
? selected.label
: candidate.label
: null,
},
}));
return {
selectedNodeId: selected?.nodeId ?? null,
selectedNodeLabel: selected?.label ?? null,
status: selection.status,
tieType: selection.tieType,
resolvedNodeIds: [...resolvedNodeIds],
tiedCandidateIds: selection.leadingCandidates.map(
(candidate) => candidate.nodeId,
),
tieBreakOrder: [
"score_desc",
"downstreamCount_desc",
"unresolvedParentUnknownCount_asc",
"label_asc",
],
alphabeticalUsedAsReasoning: false,
candidates: orderedCandidates,
selected: selected
? {
nodeId: selected.nodeId,
label: selected.label,
score: selected.score,
downstreamCount: selected.downstreamCount,
unresolvedParentUnknownCount: selected.unresolvedParentUnknownCount,
matches: selected.matches,
contributions: selected.contributions,
}
: null,
competitors,
summary: {
candidateCount: orderedCandidates.length,
selectedReason: selected
? `highest_score=${selected.score}; downstream=${selected.downstreamCount}; unresolved_prerequisites=${selected.unresolvedParentUnknownCount}`
: selection.reason,
},
};
}
// ── Apply a graph update deterministically ──
export function applyGraphUpdate(graph, update) {
const errors = [];
const updatedNodesMap = new Map();
// Validate that update references existing nodes or newly added ones
const allNodeIds = new Set(graph.nodes.map((n) => n.id));
for (const added of update.addedNodes) {
if (allNodeIds.has(added.id)) {
errors.push(`Cannot add node with duplicate ID: "${added.id}"`);
continue;
}
allNodeIds.add(added.id);
}
// Validate updated nodes exist
for (const upd of update.updatedNodes) {
if (!allNodeIds.has(upd.nodeId)) {
errors.push(`Cannot update non-existent node: "${upd.nodeId}"`);
}
}
// Validate added edges reference existing or new nodes
for (const edge of update.addedEdges) {
if (!allNodeIds.has(edge.fromNodeId)) {
errors.push(
`Added edge references non-existent fromNodeId: "${edge.fromNodeId}"`,
);
}
if (!allNodeIds.has(edge.toNodeId)) {
errors.push(
`Added edge references non-existent toNodeId: "${edge.toNodeId}"`,
);
}
}
if (errors.length > 0) return { success: false, errors };
// Build the new nodes list — start with a deep copy of existing
const newNodes = graph.nodes.map((n) => ({ ...n }));
// Apply updated nodes
for (const upd of update.updatedNodes) {
const idx = newNodes.findIndex((n) => n.id === upd.nodeId);
if (idx === -1) continue; // already validated above
if (upd.newStatus !== undefined && upd.newStatus !== null) {
newNodes[idx].status = upd.newStatus;
}
if (upd.newValue !== undefined) {
newNodes[idx].value = upd.newValue;
}
updatedNodesMap.set(upd.nodeId, newNodes[idx]);
}
// Add new nodes
for (const newNode of update.addedNodes) {
if (!allNodeIds.has(newNode.id)) continue;
allNodeIds.add(newNode.id);
newNodes.push({ ...newNode });
}
// Remove edges if requested
const removedEdgeSet = new Set(update.removedEdgeIds);
const newEdges = graph.edges.filter((e) => !removedEdgeSet.has(e.id));
// Add new edges
for (const newEdge of update.addedEdges) {
newEdges.push({ ...newEdge });
// Update dependsOn / affects on the nodes
const fromNode = newNodes.find((n) => n.id === newEdge.fromNodeId);
const toNode = newNodes.find((n) => n.id === newEdge.toNodeId);
if (fromNode && !fromNode.childIds.includes(newEdge.toNodeId)) {
fromNode.childIds.push(newEdge.toNodeId);
}
if (toNode && !toNode.dependsOn.includes(newEdge.fromNodeId)) {
toNode.dependsOn.push(newEdge.fromNodeId);
}
}
// Add resolved node IDs
const newResolved = [
...new Set([...graph.resolvedNodeIds, ...update.resolvedUnknownNodeIds]),
];
return {
success: true,
nodes: newNodes,
edges: newEdges,
resolvedNodeIds: newResolved,
};
}
// ── Validate a proposed graph update before application ──
export function validateGraphUpdate(graph, update) {
const errors = [];
// Check for duplicate node IDs against existing and newly added nodes
const extendedIds = new Set(graph.nodes.map((n) => n.id));
for (const newNode of update.addedNodes) {
if (extendedIds.has(newNode.id)) {
errors.push(`Cannot add node with duplicate ID: "${newNode.id}"`);
} else {
extendedIds.add(newNode.id);
}
}
// Check updated nodes exist (in original graph, not newly added ones)
const existingIds = new Set(graph.nodes.map((n) => n.id));
for (const upd of update.updatedNodes) {
if (!existingIds.has(upd.nodeId)) {
errors.push(`Cannot update non-existent node: "${upd.nodeId}"`);
}
}
// Reject updates with no meaningful change
const statusChanged = update.updatedNodes.some(
(u) => u.previousStatus !== null && u.newStatus !== u.previousStatus,
);
const valueChanged = update.updatedNodes.some(
(u) => u.previousValue !== null && u.newValue !== u.previousValue,
);
const hasMeaningfulChange =
update.addedNodes.length > 0 ||
statusChanged ||
valueChanged ||
update.addedEdges.length > 0 ||
update.removedEdgeIds.length > 0;
if (!hasMeaningfulChange) {
errors.push("Update contains no meaningful change");
}
// Reject oversized input
const totalSize = JSON.stringify(update).length;
if (totalSize > 100000) {
errors.push(`Proposed graph update exceeds 100KB (${totalSize} bytes)`);
}
return { valid: errors.length === 0, errors };
}
+3 -5
View File
@@ -93,11 +93,9 @@ async function detectChatSupport(baseUrl) {
class OllamaLlmProvider {
async generateReconstruction(scenario, modelName) {
const { buildPrompt } = await import("@/lib/reconstruction/prompt");
let rawPrompt = buildPrompt(scenario);
// Stronger JSON hint since we can't use format:json on older Ollama
const prompt = rawPrompt + `\n\nReturn ONLY a valid JSON object starting with { and ending with }. Do NOT include any text before the opening brace or after the closing brace. Do NOT wrap in markdown backticks.`;
// scenario is ALREADY a fully-built prompt text (built by analyseScenario).
// Do NOT call buildPrompt() again — that would double-wrap the prompt.
const prompt = scenario;
const baseUrl = process.env.OLLAMA_BASE_URL;
if (!baseUrl) throw new Error("OLLAMA_BASE_URL is not set");
+40
View File
@@ -0,0 +1,40 @@
function cloneJsonSafe(value) {
if (value == null) return value;
return JSON.parse(JSON.stringify(value));
}
export function normaliseAnalysisResponse(input) {
const normalised = cloneJsonSafe(input);
const changesApplied = [];
const warnings = [];
if (!normalised || typeof normalised !== "object") {
return { normalised: input, changesApplied, warnings };
}
if (Array.isArray(normalised.evidence)) {
normalised.evidence = normalised.evidence.map((record, index) => {
if (!record || typeof record !== "object") return record;
if (record.source === null) {
changesApplied.push({
path: ["evidence", index, "source"],
change: "Converted null source to undefined",
});
const { source: _removed, ...rest } = record;
return rest;
}
return record;
});
}
if (changesApplied.length > 0) {
warnings.push(
"Applied deterministic reconstruction compatibility normalisation",
);
}
return { normalised, changesApplied, warnings };
}
+73 -2
View File
@@ -1,5 +1,24 @@
export function buildPrompt(scenario) {
return `You are a neutral analyst performing an evidence-based reconstruction of the following scenario.
import { promises as fs } from "node:fs";
import { fileURLToPath } from "node:url";
import { dirname, join } from "node:path";
const __filename = fileURLToPath(import.meta.url);
const __dirname = dirname(__filename);
const PROMPTS_DIR = join(__dirname, "../../prompts");
/** Available prompt versions */
export const PROMPT_VERSIONS = ["v0.1", "v0.2", "v0.3"];
/** Default prompt version (override via RECONSTRUCTION_PROMPT_VERSION env var) */
const defaultVersionFromEnv = process.env.RECONSTRUCTION_PROMPT_VERSION;
export const DEFAULT_PROMPT_VERSION =
defaultVersionFromEnv && PROMPT_VERSIONS.includes(defaultVersionFromEnv)
? defaultVersionFromEnv
: "v0.3";
/** Build a v0.1 (extraction-only) prompt inline for backward compatibility */
function buildV1Prompt(scenario) {
return `You are a neutral analyst performing an evidence-based reconstruction of the following scenario.
Rules:
1. Do NOT invent facts. Only include information present in the scenario or clearly implied.
@@ -29,3 +48,55 @@ Return valid JSON matching this structure exactly:
Return ONLY the JSON object. No markdown, no explanation, no preamble.`;
}
/** Load a versioned prompt from disk and substitute {{SCENARIO}} */
async function buildV2Prompt(scenario) {
try {
const content = await fs.readFile(
join(PROMPTS_DIR, "reconstruct-v0.2.md"),
"utf-8",
);
return content.replace("{{SCENARIO}}", scenario);
} catch {
// Fall back to v0.1 prompt if v0.2 file is missing
return buildV1Prompt(scenario);
}
}
/** Load a versioned prompt from disk and substitute {{SCENARIO}} */
async function buildV3Prompt(scenario) {
try {
const content = await fs.readFile(
join(PROMPTS_DIR, "reconstruct-v0.3.md"),
"utf-8",
);
return content.replace("{{SCENARIO}}", scenario);
} catch {
// Fall back to v0.2 prompt if v0.3 file is missing
return buildV2Prompt(scenario);
}
}
/**
* Build an analysis prompt for the given version.
* @param {"v0.1" | "v0.2" | "v0.3"} [version="v0.3"]
* @returns {Promise<{prompt: string, version: string}>}
*/
export async function buildPrompt(scenario, version = "v0.3") {
let prompt;
switch (version) {
case "v0.1":
prompt = buildV1Prompt(scenario);
break;
case "v0.2":
prompt = await buildV2Prompt(scenario);
break;
default: // v0.3
prompt = await buildV3Prompt(scenario);
break;
}
const strongJsonHint =
"\n\nReturn ONLY a valid JSON object starting with { and ending with }. Do NOT include any text before the opening brace or after the closing brace. Do NOT wrap in markdown backticks.";
return { prompt: prompt + strongJsonHint, version };
}
+183 -16
View File
@@ -1,38 +1,59 @@
import { z } from "zod";
const confidenceEnum = z.enum(["low", "medium", "high"]);
// ──────────────────────────────────────────────
// Shared enums (v0.1 & v0.2)
// ──────────────────────────────────────────────
const itemSchema = z.object({
export const confidenceEnum = z.enum(["low", "medium", "high"]);
const importanceEnum = z.enum([
"incidental",
"supporting",
"important",
"critical",
]);
const expectedInfoValueEnum = z.enum(["low", "medium", "high"]);
// ──────────────────────────────────────────────
// v0.1 — extraction-only schema (preserved)
// ──────────────────────────────────────────────
const confidenceEnumV1 = z.enum(["low", "medium", "high"]);
const itemSchemaV1 = z.object({
id: z.string().min(1),
description: z.string().min(1),
confidence: confidenceEnum,
confidence: confidenceEnumV1,
});
export const reconstructionSchema = z.object({
observations: z.array(itemSchema),
observations: z.array(itemSchemaV1),
reportedClaims: z.array(
itemSchema.extend({
attributedTo: z.union([z.string().min(1), z.null()]).optional().nullable(),
})
itemSchemaV1.extend({
attributedTo: z
.union([z.string().min(1), z.null()])
.optional()
.nullable(),
}),
),
assumptions: z.array(itemSchema),
entities: z.array(itemSchema),
assumptions: z.array(itemSchemaV1),
entities: z.array(itemSchemaV1),
transitions: z.array(
itemSchema.extend({
itemSchemaV1.extend({
entity: z.string().min(1),
previousState: z.string().min(1),
currentState: z.string().min(1),
explanationStatus: z.string().min(1),
})
}),
),
expectedButMissing: z.array(itemSchema),
presentButUnexpected: z.array(itemSchema),
contradictions: z.array(itemSchema),
openUncertainties: z.array(itemSchema),
expectedButMissing: z.array(itemSchemaV1),
presentButUnexpected: z.array(itemSchemaV1),
contradictions: z.array(itemSchemaV1),
openUncertainties: z.array(itemSchemaV1),
});
// v0.1 analyse response (used internally)
export const analyseResponseSchema = z.object({
reconstruction: reconstructionSchema,
reconstruction: z.union([reconstructionSchema, z.null()]),
modelName: z.string(),
responseDurationMs: z.number(),
validationStatus: z.enum(["valid", "partial", "invalid"]),
@@ -48,6 +69,141 @@ export const healthResponseSchema = z.object({
error: z.string().nullable(),
});
// ──────────────────────────────────────────────
// v0.2 — reasoning classification + reconstruction
// ──────────────────────────────────────────────
export const inputTypes =
/** @type {z.ZodType<typeof import("@/lib/reconstruction/schema").INPUT_TYPE_VALUE>} */ (
z.enum([
"observed_problem",
"unexplained_change",
"contradiction",
"decision_request",
"causal_claim",
"reported_claim",
"fault_report",
"ambiguous_statement",
"question",
"desired_outcome",
"insufficient_context",
"other",
])
);
export const reasoningModes =
/** @type {z.ZodType<typeof import("@/lib/reconstruction/schema").REASONING_MODE_VALUE>} */ (
z.enum([
"establish_baseline",
"identify_difference",
"reconstruct_transition",
"decompose_aggregate",
"validate_measurement",
"validate_claim",
"investigate_contradiction",
"clarify_meaning",
"decision_support",
"fault_investigation",
"identify_missing_information",
"test_possible_explanations",
"other",
])
);
const evidenceRecordSchema = z.object({
id: z.string().min(1),
description: z.string().min(1),
evidenceType: z.enum([
"direct_observation",
"reported_statement",
"interpretation",
"assumption",
"inferred_relationship",
]),
source: z.string().optional(),
attribution: z.string().nullable().optional(),
confidence: confidenceEnum,
importance: importanceEnum,
});
const reconstructionSchemaV2 = z.object({
summary: z.string().min(1),
actors: z.array(itemSchemaV1),
systemsOrObjects: z.array(itemSchemaV1),
expectedStates: z.array(itemSchemaV1),
observedStates: z.array(itemSchemaV1),
differences: z.array(itemSchemaV1),
knownTransitions: z.array(
itemSchemaV1.extend({
entity: z.string().min(1),
previousState: z.string().min(1),
currentState: z.string().min(1),
explanationStatus: z.string().min(1),
}),
),
unexplainedTransitions: z.array(
itemSchemaV1.extend({
entity: z.string().min(1).optional(),
previousState: z.string().min(1).optional(),
currentState: z.string().min(1).optional(),
}),
),
contradictions: z.array(itemSchemaV1),
importantUnknowns: z.array(itemSchemaV1),
plausibleInterpretations: z.array(
z.object({
id: z.string().min(1),
description: z.string().min(1),
supportingEvidenceIds: z.array(z.string()),
assumptionsRequired: z.array(z.string()).optional().default([]),
confidence: confidenceEnum,
}),
),
});
const inputClassificationSchema = z.object({
primaryType: inputTypes,
secondaryTypes: z.array(inputTypes).optional().default([]),
reasoningModes: z.array(reasoningModes).optional().default([]),
classificationReason: z.string().min(1),
confidence: confidenceEnum,
});
const nextQuestionSchema = z.object({
id: z.string().min(1),
question: z.string().min(1),
targets: z.array(z.string()),
reason: z.string().min(1),
expectedInformationValue: expectedInfoValueEnum,
reasoningMode: reasoningModes.optional().default("other"),
});
// v0.2 complete analysis response (what the model produces)
export const reconstructionV2Schema = z.object({
inputClassification: inputClassificationSchema,
reconstruction: reconstructionSchemaV2,
evidence: z.array(evidenceRecordSchema),
nextQuestion: nextQuestionSchema,
});
// Outer wrapper for API return (includes diagnostics + v0.2 data)
export const analyseResponseV2Schema = z.object({
inputClassification: inputClassificationSchema.optional(),
reconstruction: reconstructionSchemaV2.optional().nullable(),
evidence: z.array(evidenceRecordSchema).optional(),
nextQuestion: nextQuestionSchema.optional(),
modelName: z.string(),
responseDurationMs: z.number(),
validationStatus: z.enum(["valid", "partial", "invalid"]),
rawResponse: z.string().optional(),
errors: z.array(z.string()).optional(),
promptVersion: z.string().optional(),
});
// ──────────────────────────────────────────────
// Parsing helpers
// ──────────────────────────────────────────────
export function parseReconstruction(raw) {
if (typeof raw === "string") {
try {
@@ -58,3 +214,14 @@ export function parseReconstruction(raw) {
}
return reconstructionSchema.parse(raw);
}
export function parseReconstructionV2(raw) {
if (typeof raw === "string") {
try {
raw = JSON.parse(raw);
} catch {
throw new SyntaxError("Model response is not valid JSON");
}
}
return reconstructionV2Schema.parse(raw);
}
+66 -2
View File
@@ -1,12 +1,12 @@
{
"name": "confidence-engine",
"version": "0.1.0",
"version": "0.2.0-experimental",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"name": "confidence-engine",
"version": "0.1.0",
"version": "0.2.0-experimental",
"dependencies": {
"next": "^14.2.0",
"react": "^18.3.0",
@@ -14,6 +14,7 @@
"zod": "^3.23.0"
},
"devDependencies": {
"@playwright/test": "^1.62.1",
"@types/node": "^20.14.0",
"@types/react": "^18.3.0",
"@types/react-dom": "^18.3.0",
@@ -888,6 +889,22 @@
"node": ">=14"
}
},
"node_modules/@playwright/test": {
"version": "1.62.1",
"resolved": "https://registry.npmjs.org/@playwright/test/-/test-1.62.1.tgz",
"integrity": "sha512-DTcUc8qii+cpHvtOwggMtBRMjKZHXYWdw8syRYu2vtzuq4Wxphqq4NfCs5Zt44L6mA8rfDfj+PHnxFc/FeK6mQ==",
"devOptional": true,
"license": "Apache-2.0",
"dependencies": {
"playwright": "1.62.1"
},
"bin": {
"playwright": "cli.js"
},
"engines": {
"node": ">=20"
}
},
"node_modules/@rollup/rollup-android-arm-eabi": {
"version": "4.62.3",
"resolved": "https://registry.npmjs.org/@rollup/rollup-android-arm-eabi/-/rollup-android-arm-eabi-4.62.3.tgz",
@@ -5601,6 +5618,53 @@
"node": ">= 6"
}
},
"node_modules/playwright": {
"version": "1.62.1",
"resolved": "https://registry.npmjs.org/playwright/-/playwright-1.62.1.tgz",
"integrity": "sha512-0M+L3LAD8/nm554LOla9Ayx0j0tmFZ0FBcoQ7F1VuVHpM/XpiC8RcDzBQB8W5+hA8L22THxELzeF+2WcUzvcLg==",
"devOptional": true,
"license": "Apache-2.0",
"dependencies": {
"playwright-core": "1.62.1"
},
"bin": {
"playwright": "cli.js"
},
"engines": {
"node": ">=20"
},
"optionalDependencies": {
"fsevents": "2.3.2"
}
},
"node_modules/playwright-core": {
"version": "1.62.1",
"resolved": "https://registry.npmjs.org/playwright-core/-/playwright-core-1.62.1.tgz",
"integrity": "sha512-wPYSwEBJY9GHraISXqyqtx0na0LpO3XEX7jNDhntbex7tzUS7kLnZsOlFruFJB4Hi/rhDMjXGqHewDZ68nYZVw==",
"devOptional": true,
"license": "Apache-2.0",
"bin": {
"playwright-core": "cli.js"
},
"engines": {
"node": ">=20"
}
},
"node_modules/playwright/node_modules/fsevents": {
"version": "2.3.2",
"resolved": "https://registry.npmjs.org/fsevents/-/fsevents-2.3.2.tgz",
"integrity": "sha512-xiqMQR4xAeHTuB9uWm+fFRcIOgKBMiOBP+eXiyT7jsgVCq1bkVygt00oASowB7EdtpOHaaPgKt812P9ab+DDKA==",
"dev": true,
"hasInstallScript": true,
"license": "MIT",
"optional": true,
"os": [
"darwin"
],
"engines": {
"node": "^8.16.0 || ^10.6.0 || >=11.0.0"
}
},
"node_modules/possible-typed-array-names": {
"version": "1.1.0",
"resolved": "https://registry.npmjs.org/possible-typed-array-names/-/possible-typed-array-names-1.1.0.tgz",
+2 -1
View File
@@ -1,7 +1,7 @@
{
"type": "module",
"name": "confidence-engine",
"version": "0.1.0",
"version": "0.2.0-experimental",
"private": true,
"description": "Experimental prototype for evidence-based situation reconstruction using local LLMs",
"scripts": {
@@ -19,6 +19,7 @@
"zod": "^3.23.0"
},
"devDependencies": {
"@playwright/test": "^1.62.1",
"@types/node": "^20.14.0",
"@types/react": "^18.3.0",
"@types/react-dom": "^18.3.0",
+5
View File
@@ -0,0 +1,5 @@
import { defineConfig } from "@playwright/test";
export default defineConfig({
use: { headless: true, screenshot: "only-on-failure", actionTimeout: 120000 },
testMatch: "**/tests/smoke.test.js",
});
+122
View File
@@ -0,0 +1,122 @@
You are a neutral analyst performing evidence-based situation reconstruction.
## Rules
1. Do NOT invent facts, context or causes. Only include information present in the scenario or clearly implied.
2. First determine what kind of input has been supplied. Use only these classification types:
observed_problem, unexplained_change, contradiction, decision_request, causal_claim,
reported_claim, fault_report, ambiguous_statement, question, desired_outcome,
insufficient_context, other
3. Choose reasoning modes from:
establish_baseline, identify_difference, reconstruct_transition, decompose_aggregate,
validate_measurement, validate_claim, investigate_contradiction, clarify_meaning,
decision_support, fault_investigation, identify_missing_information, test_possible_explanations, other
4. Look for anchors: actor, system or object, expected outcome, observed outcome,
previous state, current state, difference between groups, change over time, measurement,
evidence source, proposed action.
5. Identify meaningful differences (e.g., some succeed while others fail; revenue rises while cash falls).
6. Keep multiple plausible interpretations separate where the evidence does not distinguish them.
7. Distinguish: what was said / what it may mean / why it may have been said.
8. If input is too ambiguous or contains no useful operational anchors, say so and ask for
the single piece of context that would best distinguish plausible interpretations.
## Confidence scale
- low — weak evidence, speculation, or missing information
- medium — reasonable inference from available evidence
- high — strong evidence, direct observation, or confirmed fact
## Importance scale (evidence records)
- incidental — minor detail, unlikely to affect conclusions
- supporting — adds context but not critical
- important — materially affects understanding of the situation
- critical — essential to resolving the situation; without it conclusions cannot be drawn
## Expected information value (next question)
- low — marginally useful even if answered
- medium — meaningfully clarifies the situation
- high — would significantly distinguish between plausible explanations or fill a gap in understanding
## Next question selection criteria
Prefer questions that:
- clarify a major difference
- establish a baseline
- explain an important transition
- test an unsupported claim
- distinguish between plausible explanations
- request measurable evidence
- identify who or what is affected
- establish timing
Avoid questions that:
- have already been answered
- assume a cause
- jump to a solution
- ask about motive before the observable situation is understood
- focus on incidental wording
- are too broad to produce useful information
- combine many unrelated questions
## Output format — return this exact JSON structure
Return a JSON object with exactly these four top-level keys (use **camelCase**):
```json
{
"inputClassification": {
"primaryType": "<one of: observed_problem, unexplained_change, contradiction, decision_request, causal_claim, reported_claim, fault_report, ambiguous_statement, question, desired_outcome, insufficient_context, other>",
"secondaryTypes": ["<optional additional types from the same list>"],
"reasoningModes": ["<one or more of: establish_baseline, identify_difference, reconstruct_transition, decompose_aggregate, validate_measurement, validate_claim, investigate_contradiction, clarify_meaning, decision_support, fault_investigation, identify_missing_information, test_possible_explanations, other>"],
"classificationReason": "<brief explanation of why you chose the primary type>",
"confidence": "<low | medium | high>"
},
"reconstruction": {
"summary": "<one-sentence overview of the situation>",
"actors": [{"id": "<any unique string>", "description": "...", "confidence": "<low|medium|high>"}],
"systemsOrObjects": [{"id": "<any unique string>", "description": "...", "confidence": "<low|medium|high>"}],
"expectedStates": [{"id": "...", "description": "...", "confidence": "<low|medium|high>"}],
"observedStates": [{"id": "...", "description": "...", "confidence": "<low|medium|high>"}],
"differences": [{"id": "...", "description": "...", "confidence": "<low|medium|high>"}],
"knownTransitions": [{"id": "...", "description": "...", "confidence": "<low|medium|high>", "entity": "...", "previousState": "...", "currentState": "...", "explanationStatus": "..."}],
"unexplainedTransitions": [{"id": "...", "description": "...", "confidence": "<low|medium|high>", "entity": "...", "previousState": "...", "currentState": "..."}],
"contradictions": [{"id": "...", "description": "...", "confidence": "<low|medium|high>"}],
"importantUnknowns": [{"id": "...", "description": "...", "confidence": "<low|medium|high>"}],
"plausibleInterpretations": [{"id": "...", "description": "...", "supportingEvidenceIds": ["<ids that support this interpretation>"], "assumptionsRequired": [], "confidence": "<low|medium|high>"}]
},
"evidence": [
{
"id": "<any unique string>",
"description": "...",
"evidenceType": "<direct_observation | reported_statement | interpretation | assumption | inferred_relationship>",
"source": "<optional — who/where this came from>",
"attribution": null,
"confidence": "<low | medium | high>",
"importance": "<incidental | supporting | important | critical>"
}
],
"nextQuestion": {
"id": "<any unique string>",
"question": "<one precise question>",
"targets": ["<what this question targets — e.g. 'actor', 'system', 'expectedOutcome'>"],
"reason": "<why answering this is important>",
"expectedInformationValue": "<low | medium | high>",
"reasoningMode": "<optional reasoning mode from the list above>"
}
}
```
CRITICAL RULES for JSON output:
1. Use **exactly** the key names shown above (camelCase, no snake_case).
2. The four top-level keys must be: `inputClassification`, `reconstruction`, `evidence`, `nextQuestion`.
3. Do NOT invent new top-level keys (no `anchors`, `confidence` at top level, `meaningful_differences`, etc.).
4. Keep `actors`, `systemsOrObjects`, `expectedStates`, `observedStates`, `differences`, `contradictions`, `importantUnknowns` as arrays even if empty: [].
5. Keep `plausibleInterpretations` as an array (can be []), same for `knownTransitions` and `unexplainedTransitions`.
6. Each object in arrays must have at least `id`, `description`, `confidence`.
Scenario:
{{SCENARIO}}
Return ONLY the JSON object starting with { and ending with }. Do NOT include any text before the opening brace or after the closing brace. Do NOT wrap in markdown backticks.
+160
View File
@@ -0,0 +1,160 @@
You are a neutral analyst performing evidence-based situation reconstruction.
## Rules
1. Do NOT invent facts, context or causes. Only include information present in the scenario or clearly implied.
2. First determine what kind of input has been supplied. Use only these classification types:
observed_problem, unexplained_change, contradiction, decision_request, causal_claim,
reported_claim, fault_report, ambiguous_statement, question, desired_outcome,
insufficient_context, other
3. Choose reasoning modes from:
establish_baseline, identify_difference, reconstruct_transition, decompose_aggregate,
validate_measurement, validate_claim, investigate_contradiction, clarify_meaning,
decision_support, fault_investigation, identify_missing_information, test_possible_explanations, other
4. Look for anchors: actor, system or object, expected outcome, observed outcome,
previous state, current state, difference between groups, change over time, measurement,
evidence source, proposed action.
5. Identify meaningful differences (e.g., some succeed while others fail; revenue rises while cash falls).
6. Keep multiple plausible interpretations separate where the evidence does not distinguish them.
7. Distinguish: what was said / what it may mean / why it may have been said.
8. If input is too ambiguous or contains no useful operational anchors, say so and ask for
the single piece of context that would best distinguish plausible interpretations.
## Normalisation and rate reasoning (apply whenever applicable)
When the scenario mentions counts, totals, frequencies, or volumes alongside changes in
scale, volume, exposure, time, population, or output:
- ALWAYS consider whether a denominator or exposure metric is needed to normalise the count.
- Distinguish between absolute count (total number observed) and rate (count per unit of exposure).
- Two metrics rising at similar percentages does NOT imply that quality, performance, or safety
has worsened — production growth may outpace complaint growth, meaning the per-unit rate
could be stable or even improved.
- Identify the possible denominator explicitly (e.g., "per unit produced", "per customer served",
"per hour of operation").
- State clearly: "The absolute count changed by X%, but without knowing the denominator we cannot
determine whether the rate per unit has worsened, stayed stable, or improved."
- Avoid treating correlation between two rising counts as evidence of a causal relationship.
## Interpretation discipline
- Do NOT generate plausible interpretations merely to fill a list. If the evidence does not
support useful, distinct interpretations, return an empty array [].
- Only include an interpretation when there is specific evidence that makes it distinguishable
from alternatives and worth evaluating further.
- Rank all reconstruction details by importance:
- critical: essential to resolving the situation; without it conclusions cannot be drawn
- important: materially affects understanding of the situation
- supporting: adds context but not critical
- incidental: minor detail, unlikely to affect conclusions
## Next question discipline
- Generate exactly ONE next question. Do NOT combine multiple questions.
- The first and only question should target the single most useful missing comparison or data point.
- Prefer narrow, specific questions over broad compound questions.
- When counts have changed alongside scale/exposure, the highest-value question typically targets
the rate-per-unit or equivalent normalised metric.
- Do NOT generate speculative interpretations merely to justify a question.
## Confidence scale
- low — weak evidence, speculation, or missing information
- medium — reasonable inference from available evidence
- high — strong evidence, direct observation, or confirmed fact
## Importance scale (evidence records)
- incidental — minor detail, unlikely to affect conclusions
- supporting — adds context but not critical
- important — materially affects understanding of the situation
- critical — essential to resolving the situation; without it conclusions cannot be drawn
## Expected information value (next question)
- low — marginally useful even if answered
- medium — meaningfully clarifies the situation
- high — would significantly distinguish between plausible explanations or fill a gap in understanding
## Next question selection criteria
Prefer questions that:
- clarify a major difference
- establish a baseline
- explain an important transition
- test an unsupported claim
- distinguish between plausible explanations
- request measurable evidence
- identify who or what is affected
- establish timing
Avoid questions that:
- have already been answered
- assume a cause
- jump to a solution
- ask about motive before the observable situation is understood
- focus on incidental wording
- are too broad to produce useful information
- combine many unrelated questions
## Output format — return this exact JSON structure
Return a JSON object with exactly these four top-level keys (use **camelCase**):
```json
{
"inputClassification": {
"primaryType": "<one of: observed_problem, unexplained_change, contradiction, decision_request, causal_claim, reported_claim, fault_report, ambiguous_statement, question, desired_outcome, insufficient_context, other>",
"secondaryTypes": ["<optional additional types from the same list>"],
"reasoningModes": ["<one or more of: establish_baseline, identify_difference, reconstruct_transition, decompose_aggregate, validate_measurement, validate_claim, investigate_contradiction, clarify_meaning, decision_support, fault_investigation, identify_missing_information, test_possible_explanations, other>"],
"classificationReason": "<brief explanation of why you chose the primary type>",
"confidence": "<low | medium | high>"
},
"reconstruction": {
"summary": "<one-sentence overview of the situation>",
"actors": [{"id": "<any unique string>", "description": "...", "confidence": "<low|medium|high>"}],
"systemsOrObjects": [{"id": "<any unique string>", "description": "...", "confidence": "<low|medium|high>"}],
"expectedStates": [{"id": "...", "description": "...", "confidence": "<low|medium|high>"}],
"observedStates": [{"id": "...", "description": "...", "confidence": "<low|medium|high>"}],
"differences": [{"id": "...", "description": "...", "confidence": "<low|medium|high>"}],
"knownTransitions": [{"id": "...", "description": "...", "confidence": "<low|medium|high>", "entity": "...", "previousState": "...", "currentState": "...", "explanationStatus": "..."}],
"unexplainedTransitions": [{"id": "...", "description": "...", "confidence": "<low|medium|high>", "entity": "...", "previousState": "...", "currentState": "..."}],
"contradictions": [{"id": "...", "description": "...", "confidence": "<low|medium|high>"}],
"importantUnknowns": [{"id": "...", "description": "...", "confidence": "<low|medium|high>"}],
"plausibleInterpretations": [{"id": "...", "description": "...", "supportingEvidenceIds": ["<ids that support this interpretation>"], "assumptionsRequired": [], "confidence": "<low|medium|high>"}]
},
"evidence": [
{
"id": "<any unique string>",
"description": "...",
"evidenceType": "<direct_observation | reported_statement | interpretation | assumption | inferred_relationship>",
"source": "<optional — who/where this came from>",
"attribution": null,
"confidence": "<low | medium | high>",
"importance": "<incidental | supporting | important | critical>"
}
],
"nextQuestion": {
"id": "<any unique string>",
"question": "<one precise question>",
"targets": ["<what this question targets — e.g. 'actor', 'system', 'expectedOutcome'>"],
"reason": "<why answering this is important>",
"expectedInformationValue": "<low | medium | high>",
"reasoningMode": "<optional reasoning mode from the list above>"
}
}
```
CRITICAL RULES for JSON output:
1. Use **exactly** the key names shown above (camelCase, no snake_case).
2. The four top-level keys must be: `inputClassification`, `reconstruction`, `evidence`, `nextQuestion`.
3. Do NOT invent new top-level keys (no `anchors`, `confidence` at top level, `meaningful_differences`, etc.).
4. Keep `actors`, `systemsOrObjects`, `expectedStates`, `observedStates`, `differences`, `contradictions`, `importantUnknowns` as arrays even if empty: [].
5. Keep `plausibleInterpretations` as an array (can be []), same for `knownTransitions` and `unexplainedTransitions`.
6. Each object in arrays must have at least `id`, `description`, `confidence`.
7. **evidenceType**: classify each evidence item clearly as either a direct observation, a reported statement, an interpretation, an assumption, or an inferred relationship. Do not treat raw counts as proof of causal relationships — they may be inferred relationships only when supported by explicit reasoning about denominators or rates.
Scenario:
{{SCENARIO}}
Return ONLY the JSON object starting with { and ending with }. Do NOT include any text before the opening brace or after the closing brace. Do NOT wrap in markdown backticks.
@@ -0,0 +1,97 @@
import { mkdir, writeFile } from "node:fs/promises";
const BASE_URL =
process.env.CONFIDENCE_ENGINE_BASE_URL || "http://127.0.0.1:3000";
const OUTPUT_DIR = "tests-results/commercial-value-update";
const scenario = "I think therefore I am";
const answer =
"Deciding whether to build the Confidence Engine due to uncertainty about its commercial value.";
async function postJson(path, body) {
const response = await fetch(`${BASE_URL}${path}`, {
method: "POST",
headers: {
"content-type": "application/json",
},
body: JSON.stringify(body),
});
const json = await response.json();
return { status: response.status, json };
}
function printLine(label, value) {
const rendered = value === undefined ? null : value;
console.log(`${label}: ${JSON.stringify(rendered)}`);
}
async function main() {
await mkdir(OUTPUT_DIR, { recursive: true });
const startResult = await postJson("/api/cases/start", { scenario });
await writeFile(
`${OUTPUT_DIR}/start-response.json`,
JSON.stringify(startResult, null, 2),
);
const selectedQuestion = startResult.json?.selectedQuestion?.question || null;
let updateResult = {
status: null,
json: {
success: false,
stage: "request_construction",
errors: ["Missing selected question from start response"],
},
};
if (startResult.json?.success && selectedQuestion) {
updateResult = await postJson("/api/cases/update", {
situationGraph: startResult.json.situationGraph,
previousQuestion: selectedQuestion,
answer,
});
}
await writeFile(
`${OUTPUT_DIR}/update-response.json`,
JSON.stringify(updateResult, null, 2),
);
printLine("start success", startResult.json?.success ?? false);
printLine("update success", updateResult.json?.success ?? false);
printLine("update stage", updateResult.json?.stage ?? null);
printLine(
"proposal added nodes",
updateResult.json?.proposal?.addedNodes?.map((node) => node.id) ?? null,
);
printLine(
"proposal added edges",
updateResult.json?.proposal?.addedEdges?.map((edge) => ({
id: edge.id,
fromNodeId: edge.fromNodeId,
toNodeId: edge.toNodeId,
relationship: edge.relationship,
})) ?? null,
);
printLine(
"proposal resolved unknown IDs",
updateResult.json?.proposal?.resolvedUnknownNodeIds ??
updateResult.json?.resolvedUnknownNodeIds ??
null,
);
printLine(
"errors",
updateResult.json?.errors ??
updateResult.json?.proposalErrors ??
updateResult.json?.graphValidationErrors ??
updateResult.json?.validationErrors ??
null,
);
}
main().catch((error) => {
console.error(error instanceof Error ? error.message : String(error));
process.exitCode = 1;
});
+114
View File
@@ -0,0 +1,114 @@
import { beforeEach, describe, expect, it, vi } from "vitest";
const mockStartCase = vi.fn();
vi.mock("@/lib/graph/orchestrator.js", () => ({
startCase: (...args) => mockStartCase(...args),
}));
describe("app/api/cases/start route", () => {
beforeEach(() => {
vi.resetModules();
vi.clearAllMocks();
});
it("delegates request body to the orchestrator", async () => {
mockStartCase.mockResolvedValue({
success: true,
situationGraph: { nodes: [{ id: "n1" }], edges: [] },
selectedQuestion: null,
diagnostics: {},
});
const { POST } = await import("@/app/api/cases/start/route.js");
const request = new Request("http://localhost/api/cases/start", {
method: "POST",
body: JSON.stringify({ scenario: "Scenario text" }),
headers: { "content-type": "application/json" },
});
await POST(request);
expect(mockStartCase).toHaveBeenCalledWith({ scenario: "Scenario text" });
});
it("returns 200 on success", async () => {
mockStartCase.mockResolvedValue({
success: true,
situationGraph: { nodes: [{ id: "n1" }], edges: [] },
selectedQuestion: null,
diagnostics: {},
});
const { POST } = await import("@/app/api/cases/start/route.js");
const response = await POST(
new Request("http://localhost/api/cases/start", {
method: "POST",
body: JSON.stringify({ scenario: "Scenario text" }),
headers: { "content-type": "application/json" },
}),
);
expect(response.status).toBe(200);
});
it("returns 400 for invalid request input", async () => {
mockStartCase.mockResolvedValue({
success: false,
error: "Invalid start-case request",
validationErrors: [{ message: "Required" }],
statusCode: 400,
});
const { POST } = await import("@/app/api/cases/start/route.js");
const response = await POST(
new Request("http://localhost/api/cases/start", {
method: "POST",
body: JSON.stringify({}),
headers: { "content-type": "application/json" },
}),
);
expect(response.status).toBe(400);
await expect(response.json()).resolves.toMatchObject({
success: false,
error: "Invalid start-case request",
});
});
it("returns provider/internal failures as 5xx without stack traces", async () => {
mockStartCase.mockResolvedValue({
success: false,
error: "Provider unavailable",
diagnostics: { modelName: "llama3" },
statusCode: 502,
});
const { POST } = await import("@/app/api/cases/start/route.js");
const response = await POST(
new Request("http://localhost/api/cases/start", {
method: "POST",
body: JSON.stringify({ scenario: "Scenario text" }),
headers: { "content-type": "application/json" },
}),
);
expect(response.status).toBe(502);
await expect(response.json()).resolves.not.toHaveProperty("stack");
});
it("returns structured 500 on malformed JSON", async () => {
const { POST } = await import("@/app/api/cases/start/route.js");
const request = {
json: vi.fn().mockRejectedValue(new Error("Unexpected token")),
};
const response = await POST(request);
expect(response.status).toBe(500);
await expect(response.json()).resolves.toMatchObject({
success: false,
error: "Internal server error",
});
});
});
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import { beforeEach, describe, expect, it, vi } from "vitest";
const mockUpdateCase = vi.fn();
vi.mock("@/lib/graph/orchestrator.js", () => ({
updateCase: (...args) => mockUpdateCase(...args),
}));
function makeSuccessResult() {
return {
success: true,
stage: "update_applied",
updatedSituationGraph: {
centralStatement: "Scenario",
nodes: [{ id: "n1" }],
edges: [],
activeUnknownNodeId: null,
resolvedNodeIds: ["n1"],
currentSummary: "Updated summary",
},
proposal: {
addedNodes: [],
updatedNodes: [],
addedEdges: [],
removedEdgeIds: [],
resolvedUnknownNodeIds: ["n1"],
affectedNodeIds: ["n1"],
selectedQuestion: null,
},
selectedQuestion: null,
affectedNodeIds: ["n1"],
resolvedUnknownNodeIds: ["n1"],
previousActiveUnknownNodeId: "n0",
newActiveUnknownNodeId: null,
changesApplied: { updatedNodeCount: 1 },
diagnostics: { promptVersion: "v0.4" },
};
}
describe("app/api/cases/update route", () => {
beforeEach(() => {
vi.resetModules();
vi.clearAllMocks();
});
it("valid update returns HTTP 200", async () => {
mockUpdateCase.mockResolvedValue(makeSuccessResult());
const { POST } = await import("@/app/api/cases/update/route.js");
const response = await POST(
new Request("http://localhost/api/cases/update", {
method: "POST",
body: JSON.stringify({ answer: "A" }),
headers: { "content-type": "application/json" },
}),
);
expect(response.status).toBe(200);
});
it("route calls updateCase with applyProposal: true", async () => {
mockUpdateCase.mockResolvedValue(makeSuccessResult());
const { POST } = await import("@/app/api/cases/update/route.js");
const body = { situationGraph: {}, previousQuestion: "Q", answer: "A" };
await POST(
new Request("http://localhost/api/cases/update", {
method: "POST",
body: JSON.stringify(body),
headers: { "content-type": "application/json" },
}),
);
expect(mockUpdateCase).toHaveBeenCalledWith(body, { applyProposal: true });
});
it("invalid JSON returns 400", async () => {
const { POST } = await import("@/app/api/cases/update/route.js");
const request = {
json: vi.fn().mockRejectedValue(new SyntaxError("Unexpected token")),
};
const response = await POST(request);
expect(response.status).toBe(400);
await expect(response.json()).resolves.toMatchObject({
success: false,
stage: "request_validation",
error: "Invalid JSON request body",
});
});
it("request validation failure returns 400", async () => {
mockUpdateCase.mockResolvedValue({
success: false,
stage: "request_validation",
error: "Invalid update-case request",
validationErrors: [{ message: "Required" }],
});
const { POST } = await import("@/app/api/cases/update/route.js");
const response = await POST(
new Request("http://localhost/api/cases/update", {
method: "POST",
body: JSON.stringify({}),
headers: { "content-type": "application/json" },
}),
);
expect(response.status).toBe(400);
});
it("graph validation failure returns 400", async () => {
mockUpdateCase.mockResolvedValue({
success: false,
stage: "graph_validation",
error: "Invalid situation graph",
graphValidationErrors: ["bad graph"],
});
const { POST } = await import("@/app/api/cases/update/route.js");
const response = await POST(
new Request("http://localhost/api/cases/update", {
method: "POST",
body: JSON.stringify({ answer: "A" }),
headers: { "content-type": "application/json" },
}),
);
expect(response.status).toBe(400);
});
it("provider failure returns 502", async () => {
mockUpdateCase.mockResolvedValue({
success: false,
stage: "provider",
error: "Graph update proposal generation failed",
providerErrors: ["provider offline"],
});
const { POST } = await import("@/app/api/cases/update/route.js");
const response = await POST(
new Request("http://localhost/api/cases/update", {
method: "POST",
body: JSON.stringify({ answer: "A" }),
headers: { "content-type": "application/json" },
}),
);
expect(response.status).toBe(502);
});
it("proposal validation failure returns 422", async () => {
mockUpdateCase.mockResolvedValue({
success: false,
stage: "proposal_validation",
error: "Invalid graph update proposal",
proposalErrors: [{ message: "bad proposal" }],
});
const { POST } = await import("@/app/api/cases/update/route.js");
const response = await POST(
new Request("http://localhost/api/cases/update", {
method: "POST",
body: JSON.stringify({ answer: "A" }),
headers: { "content-type": "application/json" },
}),
);
expect(response.status).toBe(422);
});
it("proposal compatibility failure returns 422", async () => {
mockUpdateCase.mockResolvedValue({
success: false,
stage: "proposal_compatibility",
error: "Update case failed",
errors: ["incompatible proposal"],
});
const { POST } = await import("@/app/api/cases/update/route.js");
const response = await POST(
new Request("http://localhost/api/cases/update", {
method: "POST",
body: JSON.stringify({ answer: "A" }),
headers: { "content-type": "application/json" },
}),
);
expect(response.status).toBe(422);
});
it("application failure returns 422", async () => {
mockUpdateCase.mockResolvedValue({
success: false,
stage: "application",
error: "Update case failed",
errors: ["could not apply"],
});
const { POST } = await import("@/app/api/cases/update/route.js");
const response = await POST(
new Request("http://localhost/api/cases/update", {
method: "POST",
body: JSON.stringify({ answer: "A" }),
headers: { "content-type": "application/json" },
}),
);
expect(response.status).toBe(422);
});
it("result validation failure returns 500", async () => {
mockUpdateCase.mockResolvedValue({
success: false,
stage: "result_validation",
error: "Update case failed",
errors: ["invalid result"],
});
const { POST } = await import("@/app/api/cases/update/route.js");
const response = await POST(
new Request("http://localhost/api/cases/update", {
method: "POST",
body: JSON.stringify({ answer: "A" }),
headers: { "content-type": "application/json" },
}),
);
expect(response.status).toBe(500);
});
it("unknown failure returns 500", async () => {
mockUpdateCase.mockRejectedValue(new Error("boom"));
const { POST } = await import("@/app/api/cases/update/route.js");
const response = await POST(
new Request("http://localhost/api/cases/update", {
method: "POST",
body: JSON.stringify({ answer: "A" }),
headers: { "content-type": "application/json" },
}),
);
expect(response.status).toBe(500);
await expect(response.json()).resolves.toMatchObject({
success: false,
stage: "internal",
error: "Internal server error",
});
});
it("success response preserves updated graph fields", async () => {
const success = makeSuccessResult();
mockUpdateCase.mockResolvedValue(success);
const { POST } = await import("@/app/api/cases/update/route.js");
const response = await POST(
new Request("http://localhost/api/cases/update", {
method: "POST",
body: JSON.stringify({ answer: "A" }),
headers: { "content-type": "application/json" },
}),
);
await expect(response.json()).resolves.toMatchObject({
updatedSituationGraph: success.updatedSituationGraph,
proposal: success.proposal,
affectedNodeIds: success.affectedNodeIds,
resolvedUnknownNodeIds: success.resolvedUnknownNodeIds,
previousActiveUnknownNodeId: success.previousActiveUnknownNodeId,
newActiveUnknownNodeId: success.newActiveUnknownNodeId,
selectedQuestion: success.selectedQuestion,
changesApplied: success.changesApplied,
diagnostics: success.diagnostics,
});
});
it("stack traces and raw provider output are not exposed", async () => {
mockUpdateCase.mockResolvedValue({
success: false,
stage: "provider",
error: "Graph update proposal generation failed",
providerErrors: ["provider offline"],
rawResponse: "secret",
stack: "trace",
diagnostics: {},
});
const { POST } = await import("@/app/api/cases/update/route.js");
const response = await POST(
new Request("http://localhost/api/cases/update", {
method: "POST",
body: JSON.stringify({ answer: "A" }),
headers: { "content-type": "application/json" },
}),
);
const payload = await response.json();
expect(payload).not.toHaveProperty("stack");
expect(payload).not.toHaveProperty("rawResponse");
});
});
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import { makeEdge, makeGraph, makeNode } from "@/lib/graph/schema.js";
function buildAmbiguityFixture({
key,
scenario,
summaryLabel,
contradictionLabel,
observationLabels,
unknownLabels,
disallowedQuestionTerms,
}) {
const summary = makeNode({
id: `${key}-summary`,
label: summaryLabel,
description: "Summary of the situation from the scenario text",
kind: "state",
status: "provisional",
confidence: "medium",
});
const contradiction = makeNode({
id: `${key}-contradiction`,
label: contradictionLabel,
description: contradictionLabel,
kind: "relationship",
status: "supported",
confidence: "medium",
});
const observations = observationLabels.map((label, index) =>
makeNode({
id: `${key}-obs-${index + 1}`,
label,
description: label,
kind: "observation",
status: "supported",
confidence: "high",
}),
);
const unknowns = unknownLabels.map((label, index) =>
makeNode({
id: `${key}-unknown-${index + 1}`,
label,
description: label,
kind: "unknown",
status: "unknown",
confidence: "high",
}),
);
const edges = [
...observations.map((node) =>
makeEdge({
id: `${node.id}-supports-summary`,
fromNodeId: node.id,
toNodeId: summary.id,
relationship: "supports",
description: `${node.label} supports the summary.`,
}),
),
...unknowns.map((node) =>
makeEdge({
id: `${node.id}-depends-summary`,
fromNodeId: node.id,
toNodeId: summary.id,
relationship: "depends_on",
description: `${node.label} is an unresolved factor for this situation.`,
}),
),
];
return {
key,
scenario,
disallowedQuestionTerms,
graph: makeGraph({
centralStatement: scenario,
nodes: [summary, contradiction, ...observations, ...unknowns],
edges,
activeUnknownNodeId: null,
resolvedNodeIds: [],
currentSummary: `Ambiguity fixture for ${key}`,
}),
};
}
export const ambiguityGeneralisationFixtures = [
buildAmbiguityFixture({
key: "revenue-cash",
scenario:
"Revenue increased by 18%, but cash in the bank fell over the same period.",
summaryLabel: "Revenue rose while cash fell",
contradictionLabel:
"Contradiction between revenue improvement and lower cash reserves.",
observationLabels: [
"Revenue increased by 18%.",
"Cash in the bank decreased over the same period.",
],
unknownLabels: [
"Possible explanation for the contradiction from one side of the situation.",
"Possible explanation for the contradiction from another side of the situation.",
],
disallowedQuestionTerms: [
"accounts receivable",
"capex",
"debt repayments",
"working capital",
],
}),
buildAmbiguityFixture({
key: "satisfaction-complaints",
scenario:
"Customer satisfaction scores increased, but complaints also increased.",
summaryLabel: "Satisfaction scores rose while complaints also rose",
contradictionLabel:
"Contradiction between higher satisfaction scores and higher complaint volume.",
observationLabels: [
"Customer satisfaction scores increased.",
"Complaints increased.",
],
unknownLabels: [
"Possible explanation for why the positive signal and negative signal moved together.",
"Another possible explanation for why the positive signal and negative signal moved together.",
],
disallowedQuestionTerms: [
"net promoter",
"ticket backlog",
"call deflection",
"support queue",
],
}),
buildAmbiguityFixture({
key: "delivery-cancellations",
scenario:
"Average delivery time decreased by 25%, but order cancellations increased.",
summaryLabel: "Delivery became faster while cancellations increased",
contradictionLabel:
"Contradiction between faster delivery and more order cancellations.",
observationLabels: [
"Average delivery time decreased by 25%.",
"Order cancellations increased.",
],
unknownLabels: [
"Possible explanation for why the faster result did not reduce the negative result.",
"Another possible explanation for why the faster result did not reduce the negative result.",
],
disallowedQuestionTerms: [
"fulfilment",
"last mile",
"warehouse",
"routing",
],
}),
buildAmbiguityFixture({
key: "traffic-sales",
scenario: "Website traffic doubled, but sales remained unchanged.",
summaryLabel: "Website traffic doubled while sales stayed flat",
contradictionLabel:
"Contradiction between much higher traffic and unchanged sales.",
observationLabels: [
"Website traffic doubled.",
"Sales remained unchanged.",
],
unknownLabels: [
"Possible explanation for why the stronger signal did not change the outcome.",
"Another possible explanation for why the stronger signal did not change the outcome.",
],
disallowedQuestionTerms: [
"conversion funnel",
"campaign attribution",
"landing page",
"checkout flow",
],
}),
buildAmbiguityFixture({
key: "output-defects",
scenario:
"Production output increased by 30%, but quality defects also increased.",
summaryLabel: "Production output rose while defects also rose",
contradictionLabel:
"Contradiction between higher output and more quality defects.",
observationLabels: [
"Production output increased by 30%.",
"Quality defects increased.",
],
unknownLabels: [
"Possible explanation for why the gain came with a worsening result.",
"Another possible explanation for why the gain came with a worsening result.",
],
disallowedQuestionTerms: [
"scrap rate",
"throughput",
"yield",
"root cause",
],
}),
];
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import { makeEdge, makeGraph, makeNode } from "@/lib/graph/schema.js";
function buildComparabilityFixture({
key,
scenario,
observationLabels,
contradictionLabel,
expectedComparabilityStatus,
expectsComparisonQuestion,
}) {
const summary = makeNode({
id: `${key}-summary`,
label: scenario,
description: "Summary of the situation from the scenario text",
kind: "state",
status: "provisional",
confidence: "medium",
});
const observations = observationLabels.map((label, index) =>
makeNode({
id: `${key}-obs-${index + 1}`,
label,
description: label,
kind: "observation",
status: "supported",
confidence: "high",
}),
);
const contradiction = contradictionLabel
? [
makeNode({
id: `${key}-contradiction`,
label: contradictionLabel,
description: contradictionLabel,
kind: "relationship",
status: "supported",
confidence: "medium",
}),
]
: [];
const unknowns = [
makeNode({
id: `${key}-unknown-a`,
label: "Possible explanation from one side of the situation.",
description: "Possible explanation from one side of the situation.",
kind: "unknown",
status: "unknown",
confidence: "high",
}),
makeNode({
id: `${key}-unknown-b`,
label: "Possible explanation from another side of the situation.",
description: "Possible explanation from another side of the situation.",
kind: "unknown",
status: "unknown",
confidence: "high",
}),
];
const edges = [
...observations.map((node) =>
makeEdge({
id: `${node.id}-supports-summary`,
fromNodeId: node.id,
toNodeId: summary.id,
relationship: "supports",
description: `${node.label} supports the summary.`,
}),
),
...unknowns.map((node) =>
makeEdge({
id: `${node.id}-depends-summary`,
fromNodeId: node.id,
toNodeId: summary.id,
relationship: "depends_on",
description: `${node.label} is an unresolved factor for this situation.`,
}),
),
];
return {
key,
scenario,
expectedComparabilityStatus,
expectsComparisonQuestion,
graph: makeGraph({
centralStatement: scenario,
nodes: [summary, ...observations, ...contradiction, ...unknowns],
edges,
activeUnknownNodeId: null,
resolvedNodeIds: [],
currentSummary: `Comparability fixture for ${key}`,
}),
};
}
export const comparabilityAssessmentFixtures = [
buildComparabilityFixture({
key: "revenue-cash",
scenario:
"Revenue increased by 18%, but cash in the bank fell over the same period.",
observationLabels: [
"Revenue increased by 18%.",
"Cash in the bank decreased over the same period.",
],
contradictionLabel:
"Contradiction between revenue improvement and lower cash reserves.",
expectedComparabilityStatus: "uncertain",
expectsComparisonQuestion: true,
}),
buildComparabilityFixture({
key: "complaints-production",
scenario: "Complaints increased. Production increased.",
observationLabels: ["Complaints increased.", "Production increased."],
contradictionLabel:
"Possible contradiction between complaints and production movement.",
expectedComparabilityStatus: "uncertain",
expectsComparisonQuestion: true,
}),
buildComparabilityFixture({
key: "delivery-cancellations",
scenario:
"Average delivery time decreased by 25%, but order cancellations increased.",
observationLabels: [
"Average delivery time decreased by 25%.",
"Order cancellations increased.",
],
contradictionLabel:
"Contradiction between faster delivery and more cancellations.",
expectedComparabilityStatus: "uncertain",
expectsComparisonQuestion: true,
}),
buildComparabilityFixture({
key: "satisfaction-complaints",
scenario: "Customer satisfaction increased, but complaints increased.",
observationLabels: [
"Customer satisfaction increased.",
"Complaints increased.",
],
contradictionLabel:
"Contradiction between satisfaction improvement and more complaints.",
expectedComparabilityStatus: "uncertain",
expectsComparisonQuestion: true,
}),
buildComparabilityFixture({
key: "temperature-ice",
scenario: "Temperature increased. Ice melted.",
observationLabels: ["Temperature increased.", "Ice melted."],
contradictionLabel: null,
expectedComparabilityStatus: "confirmed",
expectsComparisonQuestion: false,
}),
buildComparabilityFixture({
key: "sales-same",
scenario: "Sales doubled. Sales doubled.",
observationLabels: ["Sales doubled.", "Sales doubled."],
contradictionLabel: null,
expectedComparabilityStatus: "confirmed",
expectsComparisonQuestion: false,
}),
];
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import { makeEdge, makeGraph, makeNode } from "@/lib/graph/schema.js";
function makeScenarioGraph({
scenario,
decisionNode,
answeredContextUnknown,
foundationalUnknown,
consequentialUnknown,
downstreamLeaf,
}) {
const nodes = [
decisionNode,
answeredContextUnknown,
foundationalUnknown,
consequentialUnknown,
downstreamLeaf,
];
const edges = [
makeEdge({
id: `${decisionNode.id}-to-${foundationalUnknown.id}`,
fromNodeId: decisionNode.id,
toNodeId: foundationalUnknown.id,
relationship: "depends_on",
description: `${decisionNode.label} depends on ${foundationalUnknown.label}.`,
}),
makeEdge({
id: `${answeredContextUnknown.id}-to-${consequentialUnknown.id}`,
fromNodeId: answeredContextUnknown.id,
toNodeId: consequentialUnknown.id,
relationship: "depends_on",
description: `${consequentialUnknown.label} was surfaced from resolved context.`,
}),
makeEdge({
id: `${foundationalUnknown.id}-to-${consequentialUnknown.id}`,
fromNodeId: foundationalUnknown.id,
toNodeId: consequentialUnknown.id,
relationship: "depends_on",
description: `${consequentialUnknown.label} depends on ${foundationalUnknown.label}.`,
}),
makeEdge({
id: `${consequentialUnknown.id}-to-${downstreamLeaf.id}`,
fromNodeId: consequentialUnknown.id,
toNodeId: downstreamLeaf.id,
relationship: "depends_on",
description: `${downstreamLeaf.label} depends on ${consequentialUnknown.label}.`,
}),
];
return makeGraph({
centralStatement: scenario,
nodes,
edges,
activeUnknownNodeId: answeredContextUnknown.id,
resolvedNodeIds: [],
currentSummary: "Generalisation fixture graph",
});
}
export const questionPriorityGeneralisationFixtures = [
{
key: "hire-engineer",
scenario: "Should we hire another engineer?",
decisionType: "resourcing decision",
acceptableFoundationalUnknownNodeIds: [
"hire-success-criteria",
"hire-bottleneck",
],
prohibitedFirstTopics: ["salary", "job advert", "programming language"],
acceptableQuestionStrategies: [
"decision_threshold",
"evidence_gathering",
"definition",
],
notes:
"The first question should establish whether more engineering capacity is justified before compensation or implementation details.",
graph: makeScenarioGraph({
scenario: "Should we hire another engineer?",
decisionNode: makeNode({
id: "hire-decision",
label: "Hiring another engineer decision",
description: "Decision about increasing engineering capacity.",
kind: "state",
status: "known",
confidence: "medium",
value: "Deciding whether to hire another engineer",
childIds: ["hire-success-criteria"],
}),
answeredContextUnknown: makeNode({
id: "hire-delays-known",
label: "Delivery delays established",
description:
"Need to confirm whether recent delivery delays are real because this context determines whether a capacity decision is even relevant.",
kind: "unknown",
status: "unknown",
confidence: "high",
value:
"The roadmap is slipping because the current team cannot clear the queue.",
childIds: ["hire-bottleneck"],
}),
foundationalUnknown: makeNode({
id: "hire-success-criteria",
label: "Hiring success threshold",
description:
"Need the success threshold because the hiring decision depends on what improvement would justify adding headcount.",
kind: "unknown",
status: "unknown",
confidence: "high",
parentId: "hire-decision",
childIds: ["hire-bottleneck"],
}),
consequentialUnknown: makeNode({
id: "hire-bottleneck",
label: "Primary delivery bottleneck",
description:
"Need the main bottleneck because the team must know whether another engineer would relieve the limiting constraint.",
kind: "unknown",
status: "unknown",
confidence: "high",
dependsOn: ["hire-success-criteria"],
parentId: "hire-success-criteria",
childIds: ["hire-salary"],
}),
downstreamLeaf: makeNode({
id: "hire-salary",
label: "Engineer salary budget",
description:
"Need the salary range because compensation planning comes after the hiring case is established.",
kind: "unknown",
status: "unknown",
confidence: "medium",
dependsOn: ["hire-bottleneck"],
parentId: "hire-bottleneck",
}),
}),
},
{
key: "replace-vans",
scenario: "Should we replace the delivery vans?",
decisionType: "asset replacement decision",
acceptableFoundationalUnknownNodeIds: [
"van-reliability-threshold",
"van-service-constraint",
],
prohibitedFirstTopics: [
"purchase price",
"paint colour",
"finance provider",
],
acceptableQuestionStrategies: [
"decision_threshold",
"evidence_gathering",
"definition",
],
notes:
"The first question should establish whether the fleet is failing a threshold that justifies replacement.",
graph: makeScenarioGraph({
scenario: "Should we replace the delivery vans?",
decisionNode: makeNode({
id: "van-decision",
label: "Replace delivery vans decision",
description: "Decision about replacing the current delivery fleet.",
kind: "state",
status: "known",
confidence: "medium",
value: "Deciding whether to replace the delivery vans",
childIds: ["van-reliability-threshold"],
}),
answeredContextUnknown: makeNode({
id: "van-breakdowns-known",
label: "Breakdown trend confirmed",
description:
"Need to confirm whether the recent rise in breakdowns is real because that context determines whether fleet replacement is relevant.",
kind: "unknown",
status: "unknown",
confidence: "high",
value:
"Breakdowns and missed deliveries have increased over the last quarter.",
childIds: ["van-service-constraint"],
}),
foundationalUnknown: makeNode({
id: "van-reliability-threshold",
label: "Replacement justification threshold",
description:
"Need the threshold because the replacement decision depends on what level of reliability loss is enough to justify replacing the fleet.",
kind: "unknown",
status: "unknown",
confidence: "high",
parentId: "van-decision",
childIds: ["van-service-constraint"],
}),
consequentialUnknown: makeNode({
id: "van-service-constraint",
label: "Operational service constraint",
description:
"Need the limiting service constraint because the team must know how vehicle unreliability is affecting deliveries before comparing purchasing options.",
kind: "unknown",
status: "unknown",
confidence: "high",
dependsOn: ["van-reliability-threshold"],
parentId: "van-reliability-threshold",
childIds: ["van-price"],
}),
downstreamLeaf: makeNode({
id: "van-price",
label: "Exact replacement purchase price",
description:
"Need the exact purchase price because financing analysis comes after replacement is justified.",
kind: "unknown",
status: "unknown",
confidence: "medium",
dependsOn: ["van-service-constraint"],
parentId: "van-service-constraint",
}),
}),
},
{
key: "launch-country",
scenario: "Should we launch in another country?",
decisionType: "market expansion decision",
acceptableFoundationalUnknownNodeIds: [
"country-customer",
"country-value-threshold",
],
prohibitedFirstTopics: [
"launch date",
"office location",
"advertising channel",
],
acceptableQuestionStrategies: ["definition", "decision_threshold"],
notes:
"The first question should clarify the customer or value case for expansion before rollout logistics.",
graph: makeScenarioGraph({
scenario: "Should we launch in another country?",
decisionNode: makeNode({
id: "country-decision",
label: "Launch in another country decision",
description: "Decision about entering a new national market.",
kind: "state",
status: "known",
confidence: "medium",
value: "Deciding whether to launch in another country",
childIds: ["country-customer"],
}),
answeredContextUnknown: makeNode({
id: "country-interest-known",
label: "Inbound interest confirmed",
description:
"Need to confirm whether inbound interest from another country is real because that context determines whether expansion is relevant.",
kind: "unknown",
status: "unknown",
confidence: "medium",
value:
"Prospective customers from another country are asking for access.",
childIds: ["country-value-threshold"],
}),
foundationalUnknown: makeNode({
id: "country-customer",
label: "Relevant customer in the new country",
description:
"Need the relevant customer because the expansion decision depends on who experiences the problem or receives the value in that market.",
kind: "unknown",
status: "unknown",
confidence: "high",
parentId: "country-decision",
childIds: ["country-value-threshold"],
}),
consequentialUnknown: makeNode({
id: "country-value-threshold",
label: "Expansion value threshold",
description:
"Need the value threshold because the team must know what evidence of demand or value would justify entering the new country.",
kind: "unknown",
status: "unknown",
confidence: "high",
dependsOn: ["country-customer"],
parentId: "country-customer",
childIds: ["country-launch-date"],
}),
downstreamLeaf: makeNode({
id: "country-launch-date",
label: "Country launch date",
description:
"Need the launch date because rollout planning follows once the expansion case is established.",
kind: "unknown",
status: "unknown",
confidence: "medium",
dependsOn: ["country-value-threshold"],
parentId: "country-value-threshold",
}),
}),
},
{
key: "over-budget-project",
scenario: "Should we continue a project that is over budget?",
decisionType: "continuation decision",
acceptableFoundationalUnknownNodeIds: [
"project-benefit-threshold",
"project-remaining-benefit",
],
prohibitedFirstTopics: ["sunk cost", "project logo", "final launch date"],
acceptableQuestionStrategies: ["decision_threshold", "definition"],
notes:
"The first question should establish remaining value or success threshold before sunk-cost framing or launch timing.",
graph: makeScenarioGraph({
scenario: "Should we continue a project that is over budget?",
decisionNode: makeNode({
id: "project-decision",
label: "Continue over-budget project decision",
description:
"Decision about continuing a project that has exceeded budget.",
kind: "state",
status: "known",
confidence: "medium",
value: "Deciding whether to continue the over-budget project",
childIds: ["project-benefit-threshold"],
}),
answeredContextUnknown: makeNode({
id: "project-overrun-known",
label: "Budget overrun confirmed",
description:
"Need to confirm whether the project is materially over budget because that context determines whether a continuation decision is relevant.",
kind: "unknown",
status: "unknown",
confidence: "high",
value: "The project has exceeded its approved budget by 35 percent.",
childIds: ["project-remaining-benefit"],
}),
foundationalUnknown: makeNode({
id: "project-benefit-threshold",
label: "Continuation success threshold",
description:
"Need the threshold because the continuation decision depends on what remaining benefit would still justify completing the project.",
kind: "unknown",
status: "unknown",
confidence: "high",
parentId: "project-decision",
childIds: ["project-remaining-benefit"],
}),
consequentialUnknown: makeNode({
id: "project-remaining-benefit",
label: "Remaining project benefit",
description:
"Need the remaining benefit because the team must know what value is still achievable before deciding whether to continue.",
kind: "unknown",
status: "unknown",
confidence: "high",
dependsOn: ["project-benefit-threshold"],
parentId: "project-benefit-threshold",
childIds: ["project-launch-date"],
}),
downstreamLeaf: makeNode({
id: "project-launch-date",
label: "Final launch date",
description:
"Need the final launch date because scheduling details only matter after remaining value is established.",
kind: "unknown",
status: "unknown",
confidence: "medium",
dependsOn: ["project-remaining-benefit"],
parentId: "project-remaining-benefit",
}),
}),
},
{
key: "paid-support-tier",
scenario: "Should we introduce a paid support tier?",
decisionType: "commercial packaging decision",
acceptableFoundationalUnknownNodeIds: [
"support-customer",
"support-value-threshold",
],
prohibitedFirstTopics: [
"subscription price",
"payment provider",
"tier name",
],
acceptableQuestionStrategies: [
"definition",
"decision_threshold",
"baseline_reconstruction",
],
notes:
"The first question should establish who values paid support or what outcome would justify offering it before pricing details.",
graph: makeScenarioGraph({
scenario: "Should we introduce a paid support tier?",
decisionNode: makeNode({
id: "support-decision",
label: "Introduce paid support tier decision",
description: "Decision about adding a paid support offering.",
kind: "state",
status: "known",
confidence: "medium",
value: "Deciding whether to introduce a paid support tier",
childIds: ["support-customer"],
}),
answeredContextUnknown: makeNode({
id: "support-requests-known",
label: "Support request pattern confirmed",
description:
"Need to confirm whether repeated requests for faster support responses are real because that context determines whether a paid tier is relevant.",
kind: "unknown",
status: "unknown",
confidence: "high",
value:
"Some users are asking for guaranteed response times and escalation help.",
childIds: ["support-value-threshold"],
}),
foundationalUnknown: makeNode({
id: "support-customer",
label: "Customer willing to pay for support",
description:
"Need the customer because the decision depends on who experiences enough support pain or receives enough value to pay for a support tier.",
kind: "unknown",
status: "unknown",
confidence: "high",
parentId: "support-decision",
childIds: ["support-value-threshold"],
}),
consequentialUnknown: makeNode({
id: "support-value-threshold",
label: "Paid support value threshold",
description:
"Need the value threshold because the team must know what outcome would justify introducing paid support before setting packaging details.",
kind: "unknown",
status: "unknown",
confidence: "high",
dependsOn: ["support-customer"],
parentId: "support-customer",
childIds: ["support-price"],
}),
downstreamLeaf: makeNode({
id: "support-price",
label: "Support subscription price",
description:
"Need the subscription price because pricing and payment setup come after the support value case is established.",
kind: "unknown",
status: "unknown",
confidence: "medium",
dependsOn: ["support-value-threshold"],
parentId: "support-value-threshold",
}),
}),
},
];
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import { describe, expect, it } from "vitest";
import {
formulateQuestion,
formulateTieResolutionQuestion,
} from "@/lib/graph/question-formulator.js";
import {
explainUnknownSelection,
selectActiveUnknownCandidate,
} from "@/lib/graph/utils.js";
import { ambiguityGeneralisationFixtures } from "@/tests/fixtures/ambiguity-generalisation.js";
function neutraliseUnknownLabels(graph) {
let counter = 0;
return {
...graph,
nodes: graph.nodes.map((node) => {
if (node.kind !== "unknown") return { ...node };
counter += 1;
return {
...node,
label: `Unknown ${String.fromCharCode(64 + counter)}`,
description: `Unknown factor ${counter}.`,
};
}),
};
}
function isSingleQuestion(question) {
return (question.match(/\?/g) || []).length === 1;
}
describe("ambiguity generalisation", () => {
it("preserves ambiguity across contradiction scenarios without favouring one explanation", () => {
const summary = ambiguityGeneralisationFixtures.map((fixture) => {
const explanation = explainUnknownSelection(fixture.graph, []);
const selection = selectActiveUnknownCandidate(fixture.graph, []);
const neutralExplanation = explainUnknownSelection(
neutraliseUnknownLabels(fixture.graph),
[],
);
const tieQuestion = formulateTieResolutionQuestion({
graph: fixture.graph,
});
const representativeUnknown = fixture.graph.nodes.find(
(node) => node.kind === "unknown",
);
const fallbackQuestion = formulateQuestion({
node: representativeUnknown,
graph: fixture.graph,
});
const lowerQuestion = tieQuestion.question.toLowerCase();
for (const term of fixture.disallowedQuestionTerms) {
expect(lowerQuestion).not.toContain(term.toLowerCase());
}
expect(explanation.status).toBe("ambiguous");
expect(selection.status).toBe("ambiguous");
expect(selection.selectedNode).toBeNull();
expect(explanation.selectedNodeId).toBeNull();
expect(explanation.candidates).toHaveLength(2);
expect(explanation.summary.selectedReason).toBe(
"No justified distinction between leading unknowns.",
);
expect(explanation.alphabeticalUsedAsReasoning).toBe(false);
expect(neutralExplanation.status).toBe("ambiguous");
expect(isSingleQuestion(tieQuestion.question)).toBe(true);
expect(tieQuestion.question.toLowerCase()).not.toContain(" or ");
return {
scenario: fixture.scenario,
candidateCount: explanation.candidates.length,
ambiguityStatus: explanation.status,
tieReason: explanation.summary.selectedReason,
investigationStrategy: tieQuestion.strategy,
question: tieQuestion.question,
explanationFavoured: explanation.selectedNodeId !== null,
};
});
expect(summary).toMatchInlineSnapshot(`
[
{
"ambiguityStatus": "ambiguous",
"candidateCount": 2,
"explanationFavoured": false,
"investigationStrategy": null,
"question": "Were these figures measured on the same basis and at the same scale?",
"scenario": "Revenue increased by 18%, but cash in the bank fell over the same period.",
"tieReason": "No justified distinction between leading unknowns.",
},
{
"ambiguityStatus": "ambiguous",
"candidateCount": 2,
"explanationFavoured": false,
"investigationStrategy": null,
"question": "Were these figures measured over the same period and at the same scale?",
"scenario": "Customer satisfaction scores increased, but complaints also increased.",
"tieReason": "No justified distinction between leading unknowns.",
},
{
"ambiguityStatus": "ambiguous",
"candidateCount": 2,
"explanationFavoured": false,
"investigationStrategy": null,
"question": "Were these figures measured over the same period and at the same scale?",
"scenario": "Average delivery time decreased by 25%, but order cancellations increased.",
"tieReason": "No justified distinction between leading unknowns.",
},
{
"ambiguityStatus": "ambiguous",
"candidateCount": 2,
"explanationFavoured": false,
"investigationStrategy": null,
"question": "Were these figures measured over the same period and at the same scale?",
"scenario": "Website traffic doubled, but sales remained unchanged.",
"tieReason": "No justified distinction between leading unknowns.",
},
{
"ambiguityStatus": "ambiguous",
"candidateCount": 2,
"explanationFavoured": false,
"investigationStrategy": null,
"question": "Were these figures measured over the same period and at the same scale?",
"scenario": "Production output increased by 30%, but quality defects also increased.",
"tieReason": "No justified distinction between leading unknowns.",
},
]
`);
});
});
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import { describe, expect, it } from "vitest";
import { assessUnknownAtomicity } from "@/lib/graph/question-formulator.js";
import { makeGraph, makeNode } from "@/lib/graph/schema.js";
function makeGraphWithUnknown(centralStatement, unknown, observations = []) {
return makeGraph({
centralStatement,
nodes: [unknown, ...observations],
edges: [],
activeUnknownNodeId: unknown.id,
resolvedNodeIds: [],
currentSummary: "Atomicity test graph",
});
}
describe("assessUnknownAtomicity", () => {
it("classifies denominator-style unknowns as atomic", () => {
const unknown = makeNode({
id: "n-denominator",
label: "Complaint rate denominator",
description:
"Need the denominator because it directly determines the complaint rate.",
kind: "unknown",
status: "unknown",
confidence: "high",
});
const result = assessUnknownAtomicity({
node: unknown,
graph: makeGraphWithUnknown(
"Production increased while complaints increased.",
unknown,
),
});
expect(result.atomicity).toBe("atomic");
expect(result.reason.toLowerCase()).toContain("directly");
});
it("classifies relationship explanation unknowns as composite", () => {
const unknown = makeNode({
id: "n-explanation",
label:
"Explanation for why revenue increased by 18%, but cash in the bank fell over the same period",
description:
"Need to understand what change or event could explain why these observations differ, because that is needed to investigate their relationship.",
kind: "unknown",
status: "unknown",
confidence: "medium",
});
const graph = makeGraphWithUnknown(
"Revenue increased by 18%, but cash in the bank fell over the same period.",
unknown,
[
makeNode({
id: "n-revenue",
label: "Revenue increased by 18%.",
description: "Revenue increased by 18%.",
kind: "observation",
status: "supported",
confidence: "high",
}),
makeNode({
id: "n-cash",
label: "Cash in the bank decreased over the same period.",
description: "Cash in the bank decreased over the same period.",
kind: "observation",
status: "supported",
confidence: "high",
}),
],
);
const result = assessUnknownAtomicity({ node: unknown, graph });
expect(result.atomicity).toBe("composite");
expect(result.decompositionKind).toBe("relationship_explanation");
});
it.each([
[
"Customer satisfaction rose, but complaints also rose.",
"Explanation for why customer satisfaction rose, but complaints also rose",
],
[
"Delivery time fell, but cancellations increased.",
"Possible causes of why delivery time fell, but cancellations increased",
],
[
"Traffic increased, but sales stayed flat.",
"Broad explanation for why traffic increased, but sales stayed flat",
],
[
"Production increased, but defects also increased.",
"Factors behind why production increased, but defects also increased",
],
])(
"classifies broad divergence unknowns as composite: %s",
(scenario, label) => {
const unknown = makeNode({
id: `n-${label.length}`,
label,
description: `${label} because the current unknown is too broad to ask directly.`,
kind: "unknown",
status: "unknown",
confidence: "medium",
});
const result = assessUnknownAtomicity({
node: unknown,
graph: makeGraphWithUnknown(scenario, unknown),
});
expect(result.atomicity).toBe("composite");
},
);
});
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import { describe, it, expect } from "vitest";
import {
buildInitialGraph,
buildMinimalGraph,
describeGraph,
} from "@/lib/graph/builder.js";
import {
makeNode,
situationEdgeSchema,
situationGraphSchema,
situationNodeSchema,
} from "@/lib/graph/schema.js";
// ── Helper: create a v0.3-style reconstruction fixture ───────────
function makeReconstructionFixture() {
return {
summary: "Company X reports revenue growth but increasing complaints",
actors: [
{ id: "actor-1", description: "Customer Base", confidence: "high" },
{
id: "actor-2",
description: "Product Engineering Team",
confidence: "high",
},
],
systemsOrObjects: [
{ id: "sys-1", description: "Production Line A", confidence: "high" },
{
id: "sys-2",
description: "Quality Control System",
confidence: "medium",
},
],
expectedStates: [],
observedStates: [
{
id: "obs-1",
description: "Revenue up 15% year-over-year",
confidence: "high",
},
{
id: "obs-2",
description: "Customer complaints up 40% year-over-year",
confidence: "high",
},
],
differences: [
{
id: "diff-1",
description: "Complaint count grew faster than revenue",
confidence: "medium",
},
],
knownTransitions: [],
unexplainedTransitions: [],
contradictions: [
{
id: "con-1",
description: "Revenue growth vs complaint growth inconsistency",
confidence: "high",
},
],
importantUnknowns: [
{
id: "unk-1",
description: "Denominator for complaint rate (customers served)",
confidence: "high",
},
{
id: "unk-2",
description: "Root cause of complaint increase",
confidence: "medium",
},
],
plausibleInterpretations: [],
};
}
function makeEvidenceFixture() {
return [
{
id: "ev-1",
description: "Annual report data",
evidenceType: "direct_observation",
confidence: "high",
importance: "critical",
},
{
id: "ev-2",
description: "Customer survey results",
evidenceType: "reported_statement",
confidence: "medium",
importance: "important",
},
];
}
describe("buildInitialGraph", () => {
it("builds nodes from reconstruction data", () => {
const result = buildInitialGraph({
reconstruction: makeReconstructionFixture(),
evidence: makeEvidenceFixture(),
});
expect(result.nodes.length).toBeGreaterThan(0);
expect(result.edges.length).toBeGreaterThan(0);
});
it("creates a summary node", () => {
const result = buildInitialGraph({
reconstruction: makeReconstructionFixture(),
evidence: [],
});
const summaryNode = result.nodes.find((n) => n.kind === "state");
expect(summaryNode).toBeDefined();
expect(summaryNode.label).toBe(
"Company X reports revenue growth but increasing complaints",
);
});
it("creates observation nodes from observedStates", () => {
const result = buildInitialGraph({
reconstruction: makeReconstructionFixture(),
evidence: [],
});
const observations = result.nodes.filter((n) => n.kind === "observation");
expect(observations.length).toBeGreaterThan(0);
});
it("creates unknown nodes from importantUnknowns", () => {
const result = buildInitialGraph({
reconstruction: makeReconstructionFixture(),
evidence: [],
});
const unknowns = result.nodes.filter((n) => n.kind === "unknown");
expect(unknowns.length).toBe(2); // unk-1 and unk-2
});
it("creates metric nodes from systemsOrObjects", () => {
const result = buildInitialGraph({
reconstruction: makeReconstructionFixture(),
evidence: [],
});
const metrics = result.nodes.filter((n) => n.kind === "metric");
expect(metrics.length).toBe(2); // sys-1 and sys-2
});
it("creates actor nodes as observations", () => {
const result = buildInitialGraph({
reconstruction: makeReconstructionFixture(),
evidence: [],
});
const actors = result.nodes.filter(
(n) =>
n.label.includes("Customer Base") ||
n.label.includes("Product Engineering"),
);
expect(actors.length).toBe(2);
});
it("creates difference nodes", () => {
const result = buildInitialGraph({
reconstruction: makeReconstructionFixture(),
evidence: [],
});
const differenceNode = result.nodes.find((n) =>
n.label.includes("Complaint count grew faster than revenue"),
);
expect(differenceNode).toBeDefined();
expect(differenceNode.kind).toBe("relationship");
});
it("creates contradiction nodes", () => {
const result = buildInitialGraph({
reconstruction: makeReconstructionFixture(),
evidence: [],
});
const contradictionNode = result.nodes.find((n) =>
n.label.includes("inconsistency"),
);
expect(contradictionNode).toBeDefined();
expect(contradictionNode.kind).toBe("relationship");
});
it("creates edges linking observations to summary", () => {
const result = buildInitialGraph({
reconstruction: makeReconstructionFixture(),
evidence: [],
});
const supportEdges = result.edges.filter(
(e) => e.relationship === "supports",
);
expect(supportEdges.length).toBeGreaterThan(0);
});
it("creates edges linking unknowns to summary as depends_on", () => {
const result = buildInitialGraph({
reconstruction: makeReconstructionFixture(),
evidence: [],
});
const depEdges = result.edges.filter(
(e) => e.relationship === "depends_on",
);
expect(depEdges.length).toBe(2); // Two unknown nodes
});
it("handles empty observedStates gracefully", () => {
const reconstruction = {
...makeReconstructionFixture(),
observedStates: [],
};
const result = buildInitialGraph({ reconstruction, evidence: [] });
expect(result.nodes.length).toBeGreaterThan(0); // Summary + actors + systems still created
});
it("handles missing reconstruction fields gracefully", () => {
const result = buildInitialGraph({
reconstruction: { summary: "Minimal" },
evidence: [],
});
expect(result.nodes.length).toBeGreaterThan(0);
});
it("handles null/undefined reconstruction", () => {
const result = buildInitialGraph({ reconstruction: null, evidence: [] });
expect(result.nodes.length).toBe(0);
expect(result.edges.length).toBe(0);
});
it("handles missing evidence array", () => {
const result = buildInitialGraph({
reconstruction: makeReconstructionFixture(),
});
expect(result.nodes.length).toBeGreaterThan(0);
expect(result.edges.length).toBeGreaterThan(0);
});
it("generates deterministic node IDs for same labels", () => {
const r1 = buildInitialGraph({
reconstruction: makeReconstructionFixture(),
evidence: [],
});
const r2 = buildInitialGraph({
reconstruction: makeReconstructionFixture(),
evidence: [],
});
const ids1 = r1.nodes.map((n) => n.id).sort();
const ids2 = r2.nodes.map((n) => n.id).sort();
expect(ids1).toEqual(ids2);
});
it("produces valid schema output (no parse errors)", () => {
const result = buildInitialGraph({
reconstruction: makeReconstructionFixture(),
evidence: makeEvidenceFixture(),
});
for (const node of result.nodes) {
const parsed = situationNodeSchema.safeParse(node);
if (!parsed.success) {
console.error(`Invalid node: ${node.id}`, node, parsed.error.message);
}
expect(parsed.success).toBe(true);
}
for (const edge of result.edges) {
const parsed = situationEdgeSchema.safeParse(edge);
if (!parsed.success) {
console.error(`Invalid edge: ${edge.id}`, edge, parsed.error.message);
}
expect(parsed.success).toBe(true);
}
});
it("creates edges for knownTransitions as transition nodes", () => {
const reconstruction = {
...makeReconstructionFixture(),
knownTransitions: [
{
id: "trans-1",
description: "Product shipped v2.0",
entity: "Product",
previousState: "v1.x",
currentState: "v2.0",
explanationStatus: "confirmed",
confidence: "high",
},
],
};
const result = buildInitialGraph({ reconstruction, evidence: [] });
const transitions = result.nodes.filter((n) => n.kind === "transition");
expect(transitions.length).toBe(1);
});
it("creates nodes for unexplainedTransitions", () => {
const reconstruction = {
...makeReconstructionFixture(),
unexplainedTransitions: [
{
id: "ut-1",
description: "Support wait time increased",
entity: "Support",
previousState: "2hr",
currentState: "8hr",
confidence: "medium",
},
],
};
const result = buildInitialGraph({ reconstruction, evidence: [] });
expect(result.nodes.length).toBeGreaterThan(0);
});
it("creates nodes for plausibleInterpretations as assumptions", () => {
const reconstruction = {
...makeReconstructionFixture(),
plausibleInterpretations: [
{
id: "interp-1",
description: "Quality degradation hypothesis",
supportingEvidenceIds: ["ev-2"],
assumptionsRequired: [],
confidence: "medium",
},
],
};
const result = buildInitialGraph({ reconstruction, evidence: [] });
const assumptions = result.nodes.filter((n) => n.kind === "assumption");
expect(assumptions.length).toBe(1);
});
it("links evidence to observation nodes", () => {
const reconstruction = makeReconstructionFixture();
const evidence = [{ id: "ev-1", description: "Test evidence" }];
// Add a mapping from observed states to evidence IDs would require modification
// For now, just verify the nodes have empty evidenceIds (as per current implementation)
const result = buildInitialGraph({ reconstruction, evidence });
for (const node of result.nodes) {
expect(Array.isArray(node.evidenceIds)).toBe(true);
}
});
it("handles very large reconstruction without errors", () => {
const actors = Array.from({ length: 20 }, (_, i) => ({
id: `actor-${i}`,
description: `Actor ${i}`,
confidence: "high",
}));
const result = buildInitialGraph({
reconstruction: { ...makeReconstructionFixture(), actors },
evidence: [],
});
expect(result.nodes.length).toBeGreaterThan(10);
});
it("handles transition with confirmed explanation", () => {
const reconstruction = {
...makeReconstructionFixture(),
knownTransitions: [
{
id: "t-confirmed",
description: "Confirmed event",
entity: "E1",
previousState: "s1",
currentState: "s2",
explanationStatus: "confirmed",
confidence: "high",
},
],
};
const result = buildInitialGraph({ reconstruction, evidence: [] });
const confirmedTransitions = result.nodes.filter(
(n) => n.kind === "transition" && n.status === "known",
);
expect(confirmedTransitions.length).toBe(1);
});
});
describe("buildMinimalGraph", () => {
it("creates a single node with scenario text as label", () => {
const graph = buildMinimalGraph(
"This is a test scenario for minimal graph creation",
);
expect(graph.nodes.length).toBe(1);
expect(graph.edges.length).toBe(0);
});
it("truncates label to 80 chars", () => {
const longScenario = "a".repeat(200);
const graph = buildMinimalGraph(longScenario);
expect(graph.nodes[0].label.length).toBeLessThanOrEqual(80);
});
it("creates provisional state node", () => {
const graph = buildMinimalGraph("Test scenario");
expect(graph.nodes[0].kind).toBe("state");
expect(graph.nodes[0].status).toBe("provisional");
expect(graph.nodes[0].confidence).toBe("low");
});
it("uses first 200 chars of scenario for description", () => {
const graph = buildMinimalGraph(
"This is a test scenario for minimal graph creation",
);
expect(graph.nodes[0].description).toContain("Initial situation from:");
});
it("creates deterministic ID via situationNodeSchema.parse", () => {
const graph = buildMinimalGraph("Test scenario");
// Node has explicit id "n0" from the builder, not makeNodeId
expect(graph.nodes[0].id).toBe("n0");
});
it("creates minimal valid structure", () => {
const graph = buildMinimalGraph("Test");
expect(graph.nodes).toHaveLength(1);
expect(graph.edges).toHaveLength(0);
expect(graph.nodes[0].evidenceIds).toEqual([]);
expect(graph.nodes[0].dependsOn).toEqual([]);
expect(graph.nodes[0].affects).toEqual([]);
});
});
describe("describeGraph", () => {
it("returns summary string with node count by kind", () => {
const graph = buildMinimalGraph("Test");
const description = describeGraph(graph);
expect(description).toContain("Nodes:");
expect(description).toContain("Edges:");
expect(description).toContain("Unknowns:");
});
it("shows correct edge count", () => {
const graph = buildMinimalGraph("Test");
const description = describeGraph(graph);
expect(description).toContain("Edges: 0 total");
});
it("counts unresolved unknowns", () => {
const n1 = makeNode({
id: "n-unk",
label: "Unknown",
kind: "unknown",
status: "unknown",
});
const graph = situationGraphSchema.parse({
centralStatement: "Test",
nodes: [n1],
edges: [],
activeUnknownNodeId: n1.id,
resolvedNodeIds: [],
currentSummary: "Test",
});
const description = describeGraph(graph);
expect(description).toContain("1"); // One unresolved unknown
});
it("groups nodes by kind in output", () => {
const graph = buildMinimalGraph("Test");
const description = describeGraph(graph);
expect(description).toContain("1 state");
});
});
@@ -0,0 +1,180 @@
import { describe, expect, it } from "vitest";
import {
assessComparability,
classifyObservationRelationship,
formulateTieResolutionQuestion,
} from "@/lib/graph/question-formulator.js";
import { explainUnknownSelection } from "@/lib/graph/utils.js";
import { comparabilityAssessmentFixtures } from "@/tests/fixtures/comparability-assessment.js";
describe("comparability assessment", () => {
it("generates comparison or relationship questions only when warranted", () => {
const summary = comparabilityAssessmentFixtures.map((fixture) => {
const assessment = assessComparability(fixture.graph);
const relationship = classifyObservationRelationship(fixture.graph);
const question = formulateTieResolutionQuestion({ graph: fixture.graph });
const ambiguity = explainUnknownSelection(fixture.graph, []);
expect(assessment.comparabilityStatus).toBe(
fixture.expectedComparabilityStatus,
);
expect(question.comparabilityStatus).toBe(
fixture.expectedComparabilityStatus,
);
if (fixture.expectsComparisonQuestion) {
expect(question.question.toLowerCase()).toContain("same");
expect(question.contradictionReasoningAllowed).toBe(false);
} else {
expect(question.question?.toLowerCase() || "").not.toContain(
"same period and at the same scale",
);
}
if (fixture.key !== "sales-same") {
expect(ambiguity.status).toBe("ambiguous");
}
return {
scenario: fixture.scenario,
comparabilityStatus: assessment.comparabilityStatus,
relationshipStatus: relationship.relationshipStatus,
relationshipAssessed: relationship.relationshipAssessed,
contradictionReasoningAllowed: question.contradictionReasoningAllowed,
question: question.question,
};
});
expect(summary).toEqual([
{
scenario:
"Revenue increased by 18%, but cash in the bank fell over the same period.",
comparabilityStatus: "uncertain",
relationshipStatus: "insufficient_information",
relationshipAssessed: false,
contradictionReasoningAllowed: false,
question:
"Were these figures measured on the same basis and at the same scale?",
},
{
scenario: "Complaints increased. Production increased.",
comparabilityStatus: "uncertain",
relationshipStatus: "insufficient_information",
relationshipAssessed: false,
contradictionReasoningAllowed: false,
question:
"Were these figures measured over the same period and at the same scale?",
},
{
scenario:
"Average delivery time decreased by 25%, but order cancellations increased.",
comparabilityStatus: "uncertain",
relationshipStatus: "insufficient_information",
relationshipAssessed: false,
contradictionReasoningAllowed: false,
question:
"Were these figures measured over the same period and at the same scale?",
},
{
scenario: "Customer satisfaction increased, but complaints increased.",
comparabilityStatus: "uncertain",
relationshipStatus: "insufficient_information",
relationshipAssessed: false,
contradictionReasoningAllowed: false,
question:
"Were these figures measured over the same period and at the same scale?",
},
{
scenario: "Temperature increased. Ice melted.",
comparabilityStatus: "confirmed",
relationshipStatus: "compatible",
relationshipAssessed: true,
contradictionReasoningAllowed: false,
question: null,
},
{
scenario: "Sales doubled. Sales doubled.",
comparabilityStatus: "confirmed",
relationshipStatus: "duplicate",
relationshipAssessed: true,
contradictionReasoningAllowed: false,
question: null,
},
]);
});
it("defers relationship classification while comparability is uncertain", () => {
const fixture = comparabilityAssessmentFixtures[0];
const relationship = classifyObservationRelationship(fixture.graph);
expect(relationship).toMatchObject({
relationshipStatus: "insufficient_information",
relationshipAssessed: false,
contradictionReasoningAllowed: false,
questionRequired: true,
});
expect(relationship.reasoningStages).toEqual([
{
stage: "comparability",
status: "uncertain",
outcome:
"Comparability between the observations is not yet established across period, scale, or measurement basis.",
},
{
stage: "relationship",
status: "insufficient_information",
outcome: "not assessed until comparability is established",
},
]);
});
it("allows contradiction reasoning only for genuine contradictions", () => {
const serviceGraph = {
centralStatement:
"The service was reported as available throughout the hour and unavailable throughout the same hour.",
nodes: [
{
id: "service-available",
label: "The service was available throughout the hour.",
description: "The service was available throughout the hour.",
kind: "observation",
status: "supported",
confidence: "high",
value: null,
unit: null,
evidenceIds: [],
dependsOn: [],
affects: [],
parentId: null,
childIds: [],
},
{
id: "service-unavailable",
label: "The service was unavailable throughout the same hour.",
description: "The service was unavailable throughout the same hour.",
kind: "observation",
status: "supported",
confidence: "high",
value: null,
unit: null,
evidenceIds: [],
dependsOn: [],
affects: [],
parentId: null,
childIds: [],
},
],
edges: [],
activeUnknownNodeId: null,
resolvedNodeIds: [],
currentSummary: "Service contradiction fixture",
};
const relationship = classifyObservationRelationship(serviceGraph);
expect(relationship).toMatchObject({
relationshipStatus: "contradictory",
contradictionReasoningAllowed: true,
questionRequired: true,
});
});
});
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import { describe, expect, it } from "vitest";
import { applyValidatedProposal } from "@/lib/graph/apply-proposal.js";
import { makeEdge, makeGraph, makeNode } from "@/lib/graph/schema.js";
function makeFixture() {
const parent = makeNode({
id: "n-parent",
label: "Explanation for why revenue increased while cash fell",
description:
"Need to understand what change or event could explain why these observations differ, because that is needed to investigate their relationship.",
kind: "unknown",
status: "unknown",
confidence: "medium",
});
const children = [
makeNode({
id: "n-child-1",
label: "How the two observations were measured",
description: "Need evidence about the measure used for each observation.",
kind: "unknown",
status: "unknown",
confidence: "medium",
parentId: parent.id,
}),
makeNode({
id: "n-child-2",
label: "Whether the two observations reflect different timing",
description:
"Need to know whether the two observations reflect different timing.",
kind: "unknown",
status: "unknown",
confidence: "medium",
parentId: parent.id,
}),
makeNode({
id: "n-child-3",
label: "Possible change mainly affecting revenue",
description:
"Need to know whether a possible change mainly affected revenue.",
kind: "unknown",
status: "unknown",
confidence: "medium",
parentId: parent.id,
}),
makeNode({
id: "n-child-4",
label: "Possible one-off event during the period",
description:
"Need to know whether a possible one-off event happened during the period.",
kind: "unknown",
status: "unknown",
confidence: "medium",
parentId: parent.id,
}),
];
return makeGraph({
centralStatement: "Revenue increased while cash fell.",
nodes: [parent, ...children],
edges: children.map((child, index) =>
makeEdge({
id: `e-${index + 1}`,
fromNodeId: child.id,
toNodeId: parent.id,
relationship: "depends_on",
description: `${child.label} feeds the parent.`,
}),
),
activeUnknownNodeId: "n-child-1",
resolvedNodeIds: [],
currentSummary: "confidence propagation fixture",
});
}
function makeProposal({ resolvedIds, contradictedIds = [] }) {
return {
addedNodes: [
makeNode({
id: "n-anchor",
label: "Update anchor",
description:
"Anchor state introduced by the answer because the update must contain a meaningful change.",
kind: "state",
status: "known",
confidence: "low",
}),
],
updatedNodes: [
...resolvedIds.map((id) => ({
nodeId: id,
previousStatus: "unknown",
newStatus: "resolved",
previousValue: null,
newValue: `answer:${id}`,
reason: "resolved child",
})),
...contradictedIds.map((id) => ({
nodeId: id,
previousStatus: "unknown",
newStatus: "contradicted",
previousValue: null,
newValue: `contradiction:${id}`,
reason: "contradictory child evidence",
})),
],
addedEdges: [],
removedEdgeIds: [],
resolvedUnknownNodeIds: resolvedIds,
affectedNodeIds: [],
selectedQuestion: null,
};
}
describe("confidence propagation", () => {
it("one of four children resolved does not yield high conclusion confidence", () => {
const result = applyValidatedProposal({
situationGraph: makeFixture(),
proposal: makeProposal({ resolvedIds: ["n-child-1"] }),
previousQuestion:
"What evidence would clarify how the two observations were measured?",
answer: "Same accounting period and same management accounts.",
});
const parent = result.updatedSituationGraph.nodes.find(
(n) => n.id === "n-parent",
);
expect(parent.status).toBe("provisional");
expect(parent.confidence).toBe("medium");
expect(parent.confidenceAssessment).toEqual({
evidenceConfidence: "medium",
completenessStatus: "partial",
conclusionConfidence: "medium",
});
expect(result.confidenceCapReason).toBe(
"unresolved_direct_children_cap_conclusion",
);
});
it("all children resolved with coherent evidence may yield high confidence", () => {
const result = applyValidatedProposal({
situationGraph: makeFixture(),
proposal: makeProposal({
resolvedIds: ["n-child-1", "n-child-2", "n-child-3", "n-child-4"],
}),
previousQuestion:
"What evidence would clarify how the two observations were measured?",
answer: "All direct child questions are answered.",
});
const parent = result.updatedSituationGraph.nodes.find(
(n) => n.id === "n-parent",
);
expect(parent.status).toBe("resolved");
expect(parent.confidenceAssessment).toEqual({
evidenceConfidence: "high",
completenessStatus: "complete",
conclusionConfidence: "high",
});
});
it("contradictory child evidence prevents high confidence", () => {
const result = applyValidatedProposal({
situationGraph: makeFixture(),
proposal: makeProposal({
resolvedIds: ["n-child-1"],
contradictedIds: ["n-child-2"],
}),
previousQuestion:
"What evidence would clarify how the two observations were measured?",
answer: "One child resolved, another contradicted.",
});
const parent = result.updatedSituationGraph.nodes.find(
(n) => n.id === "n-parent",
);
expect(parent.confidenceAssessment.conclusionConfidence).toBe("low");
expect(result.confidenceCapReason).toBe("contradictory_direct_children");
});
});
@@ -0,0 +1,306 @@
import { describe, expect, it } from "vitest";
import {
applyValidatedProposal,
evaluateBranchInteractions,
} from "@/lib/graph/apply-proposal.js";
import { makeEdge, makeGraph, makeNode } from "@/lib/graph/schema.js";
function makeParentWithBranches(children) {
const parent = makeNode({
id: "n-parent",
label: "Explanation for why revenue increased while cash fell",
description:
"Need to understand what change or event could explain why these observations differ, because that is needed to investigate their relationship.",
kind: "unknown",
status: "unknown",
confidence: "medium",
});
return makeGraph({
centralStatement: "Revenue increased while cash fell.",
nodes: [
parent,
...children.map((child) => ({ ...child, parentId: parent.id })),
],
edges: children.map((child, index) =>
makeEdge({
id: `e-${index + 1}`,
fromNodeId: child.id,
toNodeId: parent.id,
relationship: "depends_on",
description: `${child.label} feeds the parent.`,
}),
),
activeUnknownNodeId: children[0]?.id ?? null,
resolvedNodeIds: [],
currentSummary: "cross-branch corroboration fixture",
});
}
function makeResolvedChild(id, label, value, extra = {}) {
return makeNode({
id,
label,
description: label,
kind: "unknown",
status: "resolved",
confidence: "medium",
value,
evidenceIds: extra.evidenceIds ?? [],
});
}
function makeUnknownBranch(id, label, description, extra = {}) {
return makeNode({
id,
label,
description,
kind: "unknown",
status: extra.status ?? "unknown",
confidence: extra.confidence ?? "medium",
evidenceIds: extra.evidenceIds ?? [],
value: extra.value ?? null,
});
}
describe("evaluateBranchInteractions", () => {
it("detects corroborating independent branches", () => {
const graph = makeParentWithBranches([
makeResolvedChild("n-a", "Debtor balance increased", "bank-statement-a", {
evidenceIds: ["bank-statement-a"],
}),
makeResolvedChild(
"n-b",
"Cash receipts were delayed",
"receipts-ledger-b",
{ evidenceIds: ["receipts-ledger-b"] },
),
]);
const parentNode = graph.nodes.find((node) => node.id === "n-parent");
const result = evaluateBranchInteractions({ parentNode, graph });
expect(result.interactionSummary.corroboratingBranchCount).toBe(1);
expect(result.interactionSummary.duplicateEvidenceCount).toBe(0);
expect(result.interactionSummary.conflictingBranchCount).toBe(0);
});
it("detects duplicate evidence instead of corroboration", () => {
const graph = makeParentWithBranches([
makeResolvedChild(
"n-a",
"Bank statement shows increased debtor balance",
"same-bank",
{
evidenceIds: ["same-bank"],
},
),
makeResolvedChild(
"n-b",
"Delayed receipts also cite the bank statement",
"same-bank",
{
evidenceIds: ["same-bank"],
},
),
]);
const parentNode = graph.nodes.find((node) => node.id === "n-parent");
const result = evaluateBranchInteractions({ parentNode, graph });
expect(result.interactionSummary.duplicateEvidenceCount).toBe(1);
expect(result.interactionSummary.corroboratingBranchCount).toBe(0);
});
it("detects conflicting branches", () => {
const graph = makeParentWithBranches([
makeResolvedChild("n-a", "Revenue recognised correctly", "correctly"),
makeNode({
id: "n-b",
label: "Revenue recognised incorrectly",
description: "Revenue recognised incorrectly",
kind: "unknown",
status: "contradicted",
confidence: "medium",
value: "incorrectly",
}),
]);
const parentNode = graph.nodes.find((node) => node.id === "n-parent");
const result = evaluateBranchInteractions({ parentNode, graph });
expect(result.interactionSummary.conflictingBranchCount).toBe(1);
});
});
describe("cross-branch corroboration effects", () => {
function applyToGraph(children, resolvedIds, contradictedIds = []) {
const graph = makeParentWithBranches(children);
return applyValidatedProposal({
situationGraph: graph,
proposal: {
addedNodes: [
makeNode({
id: "n-anchor",
label: "Update anchor",
description:
"Anchor state introduced by the answer because the update must contain a meaningful change.",
kind: "state",
status: "known",
confidence: "low",
}),
],
updatedNodes: [
...resolvedIds.map((id) => ({
nodeId: id,
previousStatus: "unknown",
newStatus: "resolved",
previousValue: null,
newValue: `answer:${id}`,
reason: "resolved child",
})),
...contradictedIds.map((id) => ({
nodeId: id,
previousStatus: "unknown",
newStatus: "contradicted",
previousValue: null,
newValue: `contradiction:${id}`,
reason: "contradicted child",
})),
],
addedEdges: [],
removedEdgeIds: [],
resolvedUnknownNodeIds: resolvedIds,
affectedNodeIds: [],
selectedQuestion: null,
},
previousQuestion: "What evidence would clarify this branch?",
answer: "deterministic branch update",
});
}
it("independent corroboration increases justified confidence without reaching high on incomplete parent", () => {
const result = applyToGraph(
[
makeUnknownBranch(
"n-a",
"Debtor balance increased",
"Debtor balance increased",
),
makeUnknownBranch(
"n-b",
"Cash receipts delayed",
"Cash receipts delayed",
),
makeUnknownBranch(
"n-c",
"Possible one-off event during the period",
"Possible one-off event during the period",
),
makeUnknownBranch(
"n-d",
"Whether the two observations reflect different timing",
"Whether the two observations reflect different timing",
),
],
["n-a", "n-b"],
);
expect(result.success).toBe(true);
expect(result.interactionSummary?.corroboratingBranchCount).toBeGreaterThan(
0,
);
expect(result.interactionSummary?.duplicateEvidenceCount).toBe(0);
expect(result.parentConfidenceAfter).toBe("medium");
expect(result.confidenceCapReason).toBe(
"independent_corroboration_with_incomplete_parent",
);
});
it("duplicate evidence does not increase confidence", () => {
const result = applyToGraph(
[
makeUnknownBranch(
"n-a",
"Bank statement shows increased debtor balance",
"Bank statement shows increased debtor balance",
{ evidenceIds: ["same-bank"] },
),
makeUnknownBranch(
"n-b",
"Delayed receipts also cite the bank statement",
"Delayed receipts also cite the bank statement",
{ evidenceIds: ["same-bank"] },
),
makeUnknownBranch(
"n-c",
"Possible one-off event during the period",
"Possible one-off event during the period",
),
],
["n-a", "n-b"],
);
expect(result.success).toBe(true);
expect(result.interactionSummary?.duplicateEvidenceCount).toBeGreaterThan(
0,
);
expect(result.interactionSummary?.corroboratingBranchCount).toBe(0);
expect(result.confidenceCapReason).toBe(
"duplicate_evidence_no_extra_confidence",
);
});
it("conflicting evidence caps confidence", () => {
const result = applyToGraph(
[
makeUnknownBranch(
"n-a",
"Revenue recognised correctly",
"Revenue recognised correctly",
),
makeUnknownBranch(
"n-b",
"Revenue recognised incorrectly",
"Revenue recognised incorrectly",
),
makeUnknownBranch(
"n-c",
"Possible one-off event during the period",
"Possible one-off event during the period",
),
],
["n-a"],
["n-b"],
);
expect(result.success).toBe(true);
expect(result.interactionSummary?.conflictingBranchCount).toBeGreaterThan(
0,
);
expect(result.conclusionConfidenceAfter).toBe("low");
});
it("independent branches stay interaction-neutral", () => {
const result = applyToGraph(
[
makeUnknownBranch(
"n-a",
"Marketing campaign changed traffic",
"Marketing campaign changed traffic",
),
makeUnknownBranch(
"n-b",
"Equipment maintenance occurred",
"Equipment maintenance occurred",
),
],
["n-a"],
);
expect(result.success).toBe(true);
expect(result.interactionSummary?.independentBranchCount).toBeGreaterThan(
0,
);
});
});
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import { describe, expect, it } from "vitest";
import {
assessChildUnknownQuality,
applyValidatedProposal,
MAX_DECOMPOSITION_DEPTH,
} from "@/lib/graph/apply-proposal.js";
import { makeGraph, makeNode } from "@/lib/graph/schema.js";
function makeParentGraph({
centralStatement,
parentLabel,
parentDescription,
observations = [],
}) {
const parent = makeNode({
id: "n-parent",
label: parentLabel,
description: parentDescription,
kind: "unknown",
status: "unknown",
confidence: "medium",
});
return {
parent,
graph: makeGraph({
centralStatement,
nodes: [parent, ...observations],
edges: [],
activeUnknownNodeId: parent.id,
resolvedNodeIds: [],
currentSummary: "Decomposition quality graph",
}),
};
}
describe("assessChildUnknownQuality", () => {
it("rejects 'Timing or measurement basis' as compound", () => {
const { parent, graph } = makeParentGraph({
centralStatement:
"Revenue increased by 18%, but cash in the bank fell over the same period.",
parentLabel:
"Explanation for why revenue increased by 18%, but cash in the bank fell over the same period",
parentDescription:
"Need to understand what change or event could explain why these observations differ, because that is needed to investigate their relationship.",
});
const child = makeNode({
id: "n-child",
label: "Timing or measurement basis",
description:
"Need evidence about whether a timing or measurement-basis difference could explain the observations, because that would change how they should be interpreted.",
kind: "unknown",
status: "unknown",
confidence: "medium",
parentId: parent.id,
});
const result = assessChildUnknownQuality({
parentNode: parent,
childNode: child,
siblingNodes: [child],
graph,
});
expect(result.valid).toBe(false);
expect(result.compoundSignals).toContain("timing_or_measurement_basis");
expect(result.reasons).toContain("compound_child");
});
it("accepts a child with one directly answerable uncertainty", () => {
const { parent, graph } = makeParentGraph({
centralStatement: "Traffic increased, but sales stayed flat.",
parentLabel:
"What explains why more website traffic did not produce more sales?",
parentDescription:
"Need an explanation because the observations moved differently.",
});
const child = makeNode({
id: "n-child",
label: "Different measurement basis between the two observations",
description:
"Need evidence about whether the two observations use different measurement bases, because that could help explain the difference.",
kind: "unknown",
status: "unknown",
confidence: "medium",
parentId: parent.id,
});
const result = assessChildUnknownQuality({
parentNode: parent,
childNode: child,
siblingNodes: [child],
graph,
});
expect(result.valid).toBe(true);
expect(result.atomic).toBe(true);
expect(result.directlyAnswerable).toBe(true);
expect(result.narrowerThanParent).toBe(true);
});
it("rejects sibling duplicates", () => {
const { parent, graph } = makeParentGraph({
centralStatement: "Production increased, but defects also increased.",
parentLabel: "What explains why output and defects both increased?",
parentDescription:
"Need an explanation because both observations increased.",
});
const childA = makeNode({
id: "n-child-a",
label: "Different timing between the two observations",
description:
"Need evidence about whether the two observations reflect different timing, because that could help explain the difference.",
kind: "unknown",
status: "unknown",
confidence: "medium",
parentId: parent.id,
});
const childB = makeNode({
id: "n-child-b",
label: "Different timing between the two observations",
description:
"Need evidence about whether the two observations reflect different timing, because that could help explain the difference.",
kind: "unknown",
status: "unknown",
confidence: "medium",
parentId: parent.id,
});
const result = assessChildUnknownQuality({
parentNode: parent,
childNode: childA,
siblingNodes: [childA, childB],
graph,
});
expect(result.valid).toBe(false);
expect(result.duplicateSiblingIds).toContain("n-child-b");
});
it("rejects parent paraphrases", () => {
const { parent, graph } = makeParentGraph({
centralStatement:
"Customer satisfaction scores increased, but complaints also increased.",
parentLabel:
"What explains why satisfaction and complaints both increased?",
parentDescription:
"Need a broad explanation because the observations moved differently.",
});
const child = makeNode({
id: "n-child",
label: "What explains why satisfaction and complaints both increased?",
description:
"Need a broad explanation because the observations moved differently.",
kind: "unknown",
status: "unknown",
confidence: "medium",
parentId: parent.id,
});
const result = assessChildUnknownQuality({
parentNode: parent,
childNode: child,
siblingNodes: [child],
graph,
});
expect(result.valid).toBe(false);
expect(result.reasons).toContain("not_narrower_than_parent");
});
});
describe("decomposition stopping conditions", () => {
function makeMeaningfulNoOpProposal() {
return {
addedNodes: [
makeNode({
id: "n-anchor",
label: "Update anchor",
description:
"Anchor state introduced by the answer because the update must contain a meaningful change.",
kind: "state",
status: "known",
confidence: "low",
}),
],
updatedNodes: [],
addedEdges: [],
removedEdgeIds: [],
resolvedUnknownNodeIds: [],
affectedNodeIds: [],
selectedQuestion: null,
};
}
it("does not decompose an atomic selected unknown", () => {
const atomic = makeNode({
id: "n-atomic",
label: "Were both figures measured over the same accounting period?",
description:
"Need to know whether both figures cover the same accounting period because that determines whether they are directly comparable.",
kind: "unknown",
status: "unknown",
confidence: "high",
});
const graph = makeGraph({
centralStatement:
"Revenue increased by 18%, but cash in the bank fell over the same period.",
nodes: [atomic],
edges: [],
activeUnknownNodeId: atomic.id,
resolvedNodeIds: [],
currentSummary: "Atomic selected node graph",
});
const result = applyValidatedProposal({
situationGraph: graph,
proposal: makeMeaningfulNoOpProposal(),
});
expect(result.success).toBe(true);
expect(result.decompositionAttempted).toBe(false);
expect(result.decompositionStoppedReason).toBe(
"Selected unknown is already atomic.",
);
});
it("stops once a directly answerable child is selected", () => {
const { parent, graph } = makeParentGraph({
centralStatement: "Traffic increased, but sales stayed flat.",
parentLabel:
"What explains why more website traffic did not produce more sales?",
parentDescription:
"Need an explanation because the observations moved differently.",
observations: [
makeNode({
id: "n-traffic",
label: "Website traffic increased.",
description: "Website traffic increased.",
kind: "observation",
status: "supported",
confidence: "high",
}),
makeNode({
id: "n-sales",
label: "Sales stayed flat.",
description: "Sales stayed flat.",
kind: "observation",
status: "supported",
confidence: "high",
}),
],
});
const result = applyValidatedProposal({
situationGraph: graph,
proposal: makeMeaningfulNoOpProposal(),
});
expect(result.success).toBe(true);
expect(result.decompositionAttempted).toBe(true);
expect(result.decompositionAccepted).toBe(true);
expect(result.selectedQuestion).toMatchObject({
nodeId: expect.any(String),
question:
"What evidence would clarify how the two observations were measured?",
});
expect(result.selectedChildNodeId).toBe(result.selectedQuestion?.nodeId);
expect(result.decompositionStoppedReason).toBe(
"Selected child is atomic and directly answerable.",
);
});
it("exposes the configured maximum decomposition depth", () => {
expect(MAX_DECOMPOSITION_DEPTH).toBeGreaterThanOrEqual(2);
expect(MAX_DECOMPOSITION_DEPTH).toBeLessThanOrEqual(3);
});
});
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import { describe, expect, it } from "vitest";
import { buildGraphUpdatePrompt } from "@/lib/graph/prompt-builder.js";
import { makeEdge, makeGraph, makeNode } from "@/lib/graph/schema.js";
function makeContext() {
const unknown = makeNode({
id: "n-unknown",
label: "Complaint rate denominator",
description: "Need the denominator to compare complaint rates",
kind: "unknown",
status: "unknown",
confidence: "high",
});
const observation = makeNode({
id: "n-obs",
label: "Complaints up 35%",
description: "Complaints increased by 35%",
kind: "observation",
status: "supported",
confidence: "high",
});
return {
situationGraph: makeGraph({
centralStatement: "Complaints increased while production increased.",
nodes: [unknown, observation],
edges: [
makeEdge({
id: "e1",
fromNodeId: observation.id,
toNodeId: unknown.id,
relationship: "supports",
confidence: "high",
description: "Observation informs the unknown",
}),
],
activeUnknownNodeId: unknown.id,
resolvedNodeIds: [],
currentSummary:
"Nodes: 1 observation, 1 unknown | Edges: 1 total | Unknowns: 1 unresolved",
}),
previousQuestion: "What denominator is being used for the complaint rate?",
answer:
"The complaint rate fell from 2.0 complaints per 100 units to 1.9 complaints per 100 units.",
};
}
describe("buildGraphUpdatePrompt", () => {
it("includes the current graph", () => {
const prompt = buildGraphUpdatePrompt(makeContext());
expect(prompt).toContain(
"Complaints increased while production increased.",
);
expect(prompt).toContain("Complaint rate denominator");
});
it("includes previous question and answer", () => {
const prompt = buildGraphUpdatePrompt(makeContext());
expect(prompt).toContain(
"What denominator is being used for the complaint rate?",
);
expect(prompt).toContain(
"The complaint rate fell from 2.0 complaints per 100 units to 1.9 complaints per 100 units.",
);
});
it("contains exact schema keys", () => {
const prompt = buildGraphUpdatePrompt(makeContext());
expect(prompt).toContain("addedNodes");
expect(prompt).toContain("updatedNodes");
expect(prompt).toContain("addedEdges");
expect(prompt).toContain("removedEdgeIds");
expect(prompt).toContain("resolvedUnknownNodeIds");
expect(prompt).toContain("affectedNodeIds");
expect(prompt).toContain("selectedQuestion");
});
it("lists enum values", () => {
const prompt = buildGraphUpdatePrompt(makeContext());
expect(prompt).toContain(
"observation | reported_claim | metric | state | transition | relationship | assumption | unknown | conclusion",
);
expect(prompt).toContain(
"known | unknown | provisional | supported | weakened | contradicted | resolved",
);
expect(prompt).toContain(
"supports | weakens | contradicts | depends_on | causes | may_cause | measures | compares_with | updates | other",
);
});
it("forbids full-graph replacement", () => {
const prompt = buildGraphUpdatePrompt(makeContext());
expect(prompt).toContain("Never return a replacement graph");
expect(prompt).toContain("Propose changes only");
});
it("requires JSON only", () => {
const prompt = buildGraphUpdatePrompt(makeContext());
expect(prompt).toContain("Return JSON only");
expect(prompt).toContain("Return one JSON object only");
});
it("describes controlled emergent unknown rules", () => {
const prompt = buildGraphUpdatePrompt(makeContext());
expect(prompt).toContain("Add at most 3 new unknown nodes");
expect(prompt).toContain("Resolve the answered unknown first");
expect(prompt).toContain(
"selectedQuestion.question must be one narrow non-compound question",
);
expect(prompt).toContain(
"the engine will deterministically choose final priority after validation",
);
});
});
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import { describe, expect, it } from "vitest";
import {
assessUnknownAtomicity,
formulateQuestion,
formulateTieResolutionQuestion,
selectInvestigationStrategy,
} from "@/lib/graph/question-formulator.js";
import { makeEdge, makeGraph, makeNode } from "@/lib/graph/schema.js";
function makeGraphFor(node, extra = {}) {
return makeGraph({
centralStatement: extra.centralStatement || "Decision context",
nodes: [node, ...(extra.nodes || [])],
edges: extra.edges || [],
activeUnknownNodeId: node.id,
resolvedNodeIds: extra.resolvedNodeIds || [],
currentSummary: "Test summary",
});
}
describe("formulateQuestion", () => {
it("atomicity assessment leaves focused unknowns direct and marks broad explanation unknowns composite", () => {
const atomicUnknown = makeNode({
id: "n-atomic",
label: "Complaint rate denominator",
description:
"Need the denominator because it directly determines the complaint rate.",
kind: "unknown",
status: "unknown",
confidence: "high",
});
const compositeUnknown = makeNode({
id: "n-composite",
label:
"Explanation for why revenue increased by 18%, but cash in the bank fell over the same period",
description:
"Need to understand what change or event could explain why these observations differ, because that is needed to investigate their relationship.",
kind: "unknown",
status: "unknown",
confidence: "medium",
});
const compositeGraph = makeGraphFor(compositeUnknown, {
centralStatement:
"Revenue increased by 18%, but cash in the bank fell over the same period.",
nodes: [
makeNode({
id: "n-revenue-observation",
label: "Revenue increased by 18%.",
description: "Revenue increased by 18%.",
kind: "observation",
status: "supported",
confidence: "high",
}),
makeNode({
id: "n-cash-observation",
label: "Cash in the bank decreased over the same period.",
description: "Cash in the bank decreased over the same period.",
kind: "observation",
status: "supported",
confidence: "high",
}),
],
});
expect(
assessUnknownAtomicity({
node: atomicUnknown,
graph: makeGraphFor(atomicUnknown),
}).atomicity,
).toBe("atomic");
expect(
assessUnknownAtomicity({
node: compositeUnknown,
graph: compositeGraph,
}).atomicity,
).toBe("composite");
});
it("commercial viability plus build decision produces a decision-threshold question", () => {
const unknown = makeNode({
id: "n-commercial",
label: "Uncertainty regarding the commercial value of the product",
description:
"Commercial justification remains unclear because the decision depends on it.",
kind: "unknown",
status: "unknown",
confidence: "high",
parentId: "n-decision",
});
const decision = makeNode({
id: "n-decision",
label: "Build decision",
description: "Decision introduced by the answer.",
kind: "state",
status: "known",
confidence: "medium",
childIds: [unknown.id],
value: "Deciding whether to build the product",
});
const graph = makeGraphFor(unknown, {
nodes: [decision],
resolvedNodeIds: [decision.id],
});
const result = formulateQuestion({ node: unknown, graph });
expect(result.strategy).toBe("decision_threshold");
expect(result.question).toContain("What outcome");
expect(result.question.toLowerCase()).toContain("justify");
});
it("commercial viability does not produce a pricing-first question", () => {
const unknown = makeNode({
id: "n-commercial",
label: "Commercial viability",
description:
"Commercial viability remains unresolved because the decision depends on it.",
kind: "unknown",
status: "unknown",
confidence: "high",
});
const graph = makeGraphFor(unknown);
const result = formulateQuestion({ node: unknown, graph });
expect(result.question.toLowerCase()).not.toContain("price");
expect(result.question.toLowerCase()).not.toContain("pricing");
});
it("undefined term produces a definition question", () => {
const unknown = makeNode({
id: "n-term",
label: "Success criteria definition",
description:
"Need a definition of the term because the team uses it inconsistently.",
kind: "unknown",
status: "unknown",
confidence: "medium",
});
const result = formulateQuestion({
node: unknown,
graph: makeGraphFor(unknown),
});
expect(result.strategy).toBe("definition");
expect(result.question).toMatch(/^What does /);
});
it("unsupported claim produces an evidence question", () => {
const unknown = makeNode({
id: "n-claim",
label: "Demand claim",
description: "Need evidence because the claim has not been validated.",
kind: "reported_claim",
status: "provisional",
confidence: "low",
});
const result = formulateQuestion({
node: unknown,
graph: makeGraphFor(unknown),
});
expect(result.strategy).toBe("evidence_gathering");
expect(result.question).toContain("What evidence");
});
it("missing previous state produces a baseline question", () => {
const unknown = makeNode({
id: "n-baseline",
label: "Baseline conversion rate",
description:
"Need the previous baseline because the change cannot be assessed without it.",
kind: "unknown",
status: "unknown",
confidence: "medium",
});
const result = formulateQuestion({
node: unknown,
graph: makeGraphFor(unknown),
});
expect(result.strategy).toBe("baseline_reconstruction");
expect(result.question).toContain("What was the comparable state before");
});
it("conflicting claim produces a contradiction-resolution question", () => {
const unknown = makeNode({
id: "n-conflict",
label: "Conflicting churn claim",
description:
"Need to resolve the inconsistency because the current figures contradict each other.",
kind: "unknown",
status: "unknown",
confidence: "medium",
});
const contradiction = makeNode({
id: "n-contradiction",
label: "Contradicted report",
description: "Two sources disagree about churn.",
kind: "conclusion",
status: "contradicted",
confidence: "low",
childIds: [unknown.id],
});
const result = formulateQuestion({
node: unknown,
graph: makeGraphFor(unknown, { nodes: [contradiction] }),
});
expect(result.strategy).toBe("contradiction_resolution");
expect(result.question).toContain("resolve the contradiction");
});
it("constraint unknown uses evidence-gathering within the fixed strategy set", () => {
const unknown = makeNode({
id: "n-constraint",
label: "Budget constraint",
description:
"Need the main budget constraint because it limits the available options.",
kind: "unknown",
status: "unknown",
confidence: "medium",
});
const result = formulateQuestion({
node: unknown,
graph: makeGraphFor(unknown),
});
expect(result.strategy).toBe("evidence_gathering");
expect(result.question).toContain("What evidence");
});
it("the same unknown can produce different questions when paired with different strategies", () => {
const thresholdUnknown = makeNode({
id: "n-threshold-unknown",
label: "Value threshold",
description: "Need to resolve the value threshold.",
kind: "unknown",
status: "unknown",
confidence: "high",
});
const definitionUnknown = makeNode({
id: "n-definition-unknown",
label: "Value term",
description: "Need to resolve what value term refers to in this context.",
kind: "unknown",
status: "unknown",
confidence: "high",
});
const decisionGraph = makeGraphFor(thresholdUnknown, {
centralStatement: "We are deciding whether to launch this product.",
nodes: [
makeNode({
id: "n-decision",
label: "Launch decision",
description: "Decision depends on the value threshold.",
kind: "state",
status: "known",
confidence: "medium",
childIds: [thresholdUnknown.id],
value: "Deciding whether to launch the product",
}),
],
});
const definitionGraph = makeGraphFor(definitionUnknown, {
centralStatement:
"The team uses the term value threshold inconsistently.",
nodes: [
makeNode({
id: "n-definition",
label: "Definition disagreement about value threshold",
description:
"Need a definition of value threshold because the term is used inconsistently before comparing options.",
kind: "state",
status: "known",
confidence: "medium",
childIds: [definitionUnknown.id],
}),
],
});
const decisionResult = formulateQuestion({
node: thresholdUnknown,
graph: decisionGraph,
});
const definitionResult = formulateQuestion({
node: definitionUnknown,
graph: definitionGraph,
});
expect(decisionResult.strategy).toBe("decision_threshold");
expect(definitionResult.strategy).toBe("definition");
expect(decisionResult.question).not.toBe(definitionResult.question);
});
it("strategy selection is deterministic and explainable", () => {
const unknown = makeNode({
id: "n-threshold",
label: "Success threshold",
description:
"Need the success threshold because the decision depends on it.",
kind: "unknown",
status: "unknown",
confidence: "high",
});
const graph = makeGraphFor(unknown, {
centralStatement: "We need to decide whether to continue investing.",
});
const first = selectInvestigationStrategy({ node: unknown, graph });
const second = selectInvestigationStrategy({ node: unknown, graph });
expect(first).toEqual(second);
expect(first.key).toBe("decision_threshold");
expect(first.reason).toContain("threshold");
});
it("question is singular and answerable", () => {
const unknown = makeNode({
id: "n-evidence",
label: "Evidence of demand",
description:
"Need evidence of demand because the decision depends on it.",
kind: "unknown",
status: "unknown",
confidence: "medium",
});
const result = formulateQuestion({
node: unknown,
graph: makeGraphFor(unknown),
});
expect(result.question.match(/\?/g) || []).toHaveLength(1);
expect(result.question.toLowerCase()).not.toContain(" and ");
});
it("awkward uncertainty phrasing is rejected via fallback", () => {
const unknown = makeNode({
id: "n-weird",
label: "Uncertainty regarding service reliability",
description: "Unknown service reliability.",
kind: "unknown",
status: "unknown",
confidence: "medium",
});
const result = formulateQuestion({
node: unknown,
graph: makeGraphFor(unknown),
});
expect(result.question).not.toContain("How should uncertainty regarding");
expect(result.question).not.toContain(
"What would resolve uncertainty regarding",
);
});
it("ambiguous contradiction produces a broad distinguishing question without accounting jargon", () => {
const unknown = makeNode({
id: "n-cause-a",
label: "Cash outflow cause",
description: "Unclear explanation for the contradiction.",
kind: "unknown",
status: "unknown",
confidence: "high",
});
const contradiction = makeNode({
id: "n-contradiction",
label: "Divergent movement between revenue and cash",
description: "Two signals moved in opposite directions.",
kind: "relationship",
status: "supported",
confidence: "medium",
});
const revenueObservation = makeNode({
id: "n-revenue-observation",
label: "Revenue increased by 18%.",
description: "Revenue increased by 18%.",
kind: "observation",
status: "supported",
confidence: "high",
});
const cashObservation = makeNode({
id: "n-cash-observation",
label: "Cash in the bank decreased over the same period.",
description: "Cash in the bank decreased over the same period.",
kind: "observation",
status: "supported",
confidence: "high",
});
const graph = makeGraphFor(unknown, {
centralStatement:
"Revenue increased by 18%, but cash in the bank fell over the same period.",
nodes: [contradiction, revenueObservation, cashObservation],
});
const result = formulateTieResolutionQuestion({ graph });
expect(result.question).toBe(
"Were these figures measured on the same basis and at the same scale?",
);
expect(result.comparabilityStatus).toBe("uncertain");
expect(result.question.toLowerCase()).not.toMatch(
/accounts receivable|capex|debt repayments|working capital/,
);
});
it("definition is selected only for genuine definition unknowns", () => {
const unknown = makeNode({
id: "n-definition-only",
label: "Definition of success criteria",
description: "The term is used inconsistently and needs a definition.",
kind: "unknown",
status: "unknown",
confidence: "medium",
});
const result = formulateQuestion({
node: unknown,
graph: makeGraphFor(unknown),
});
expect(result.strategy).toBe("definition");
});
it("an unknown about possible causes does not become a definition question", () => {
const unknown = makeNode({
id: "n-causes",
label: "Possible causes of the divergence",
description: "Several causes may explain the divergence.",
kind: "unknown",
status: "unknown",
confidence: "medium",
});
const result = formulateQuestion({
node: unknown,
graph: makeGraphFor(unknown),
});
expect(result.strategy).toBeNull();
expect(result.question).toBe(
"What would clarify possible causes of the divergence in this situation?",
);
});
it("malformed punctuation is rejected", () => {
const unknown = makeNode({
id: "n-punct",
label: "Magnitude and nature of cash outflows (operating expenses).",
description:
"Magnitude and nature of cash outflows (operating expenses).",
kind: "unknown",
status: "unknown",
confidence: "medium",
});
const result = formulateQuestion({
node: unknown,
graph: makeGraphFor(unknown),
});
expect(result.question).not.toContain("). is true?");
expect(result.question).toBe(
"What would clarify magnitude and nature of cash outflows (operating expenses) in this situation?",
);
});
});
@@ -0,0 +1,155 @@
import { describe, expect, it } from "vitest";
import { applyValidatedProposal } from "@/lib/graph/apply-proposal.js";
import { formulateQuestion } from "@/lib/graph/question-formulator.js";
import { selectActiveUnknownCandidate } from "@/lib/graph/utils.js";
import { questionPriorityGeneralisationFixtures } from "@/tests/fixtures/question-priority-generalisation.js";
function clone(value) {
return JSON.parse(JSON.stringify(value));
}
function buildResolutionProposal(graph) {
const activeNode = graph.nodes.find(
(node) => node.id === graph.activeUnknownNodeId,
);
const placeholderCandidate = graph.nodes.find(
(node) => node.kind === "unknown" && node.id !== activeNode.id,
);
return {
addedNodes: [],
updatedNodes: [
{
nodeId: activeNode.id,
previousStatus: activeNode.status,
newStatus: "resolved",
previousValue: activeNode.value ?? null,
newValue: activeNode.value ?? "Resolved context answer",
reason:
"The resolved context unknown is treated as answered for fixture progression.",
},
],
addedEdges: [],
removedEdgeIds: [],
resolvedUnknownNodeIds: [activeNode.id],
affectedNodeIds: [],
selectedQuestion: {
nodeId: placeholderCandidate?.id,
question: "Placeholder candidate question?",
reason: "Candidate only; deterministic selector should override it.",
},
};
}
function assertQuestionStructure(question) {
expect(question.match(/\?/g) || []).toHaveLength(1);
expect(question).not.toMatch(/\?\s*(and|or)\b/i);
expect(question).not.toMatch(/^What is\s+/i);
expect(question).toMatch(/^(What|Who|When)\b/);
}
describe("question priority generalisation", () => {
for (const fixture of questionPriorityGeneralisationFixtures) {
it(`${fixture.scenario} selects a foundational unknown and singular answerable strategy`, () => {
const originalGraph = clone(fixture.graph);
const deterministicSelection = selectActiveUnknownCandidate(
fixture.graph,
[fixture.graph.activeUnknownNodeId],
);
const result = applyValidatedProposal({
situationGraph: fixture.graph,
proposal: buildResolutionProposal(fixture.graph),
});
expect(result.success).toBe(true);
expect(fixture.graph).toEqual(originalGraph);
expect(result.graphUpdate.selectedQuestion?.question).toBe(
"Placeholder candidate question?",
);
expect(deterministicSelection.nodeId).toBe(
result.selectedQuestion.nodeId,
);
expect(fixture.acceptableFoundationalUnknownNodeIds).toContain(
result.selectedQuestion.nodeId,
);
expect(result.selectedQuestion.nodeId).not.toBe(
fixture.graph.nodes[fixture.graph.nodes.length - 1].id,
);
expect(fixture.acceptableQuestionStrategies).toContain(
result.selectedQuestion.strategy,
);
assertQuestionStructure(result.selectedQuestion.question);
const lowerQuestion = result.selectedQuestion.question.toLowerCase();
for (const topic of fixture.prohibitedFirstTopics) {
expect(lowerQuestion).not.toContain(topic.toLowerCase());
}
const selectedNode = result.updatedSituationGraph.nodes.find(
(node) => node.id === result.selectedQuestion.nodeId,
);
const reformulated = formulateQuestion({
node: selectedNode,
graph: result.updatedSituationGraph,
context: {
resolvedValues: ["Resolved context answer"],
},
});
expect(reformulated.question).toBe(result.selectedQuestion.question);
expect(clone(result.updatedSituationGraph)).toEqual(
result.updatedSituationGraph,
);
});
}
it("reports all five selected unknowns and strategies", () => {
const summary = questionPriorityGeneralisationFixtures.map((fixture) => {
const result = applyValidatedProposal({
situationGraph: fixture.graph,
proposal: buildResolutionProposal(fixture.graph),
});
expect(result.success).toBe(true);
return {
scenario: fixture.scenario,
nodeId: result.selectedQuestion.nodeId,
strategy: result.selectedQuestion.strategy,
};
});
expect(summary).toMatchInlineSnapshot(`
[
{
"nodeId": "hire-success-criteria",
"scenario": "Should we hire another engineer?",
"strategy": "decision_threshold",
},
{
"nodeId": "van-reliability-threshold",
"scenario": "Should we replace the delivery vans?",
"strategy": "decision_threshold",
},
{
"nodeId": "country-value-threshold",
"scenario": "Should we launch in another country?",
"strategy": "decision_threshold",
},
{
"nodeId": "project-benefit-threshold",
"scenario": "Should we continue a project that is over budget?",
"strategy": "decision_threshold",
},
{
"nodeId": "support-value-threshold",
"scenario": "Should we introduce a paid support tier?",
"strategy": "baseline_reconstruction",
},
]
`);
});
});
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@@ -0,0 +1,442 @@
import { describe, it, expect } from "vitest";
import {
SituationKind,
SituationStatus,
ConfidenceLevel,
SituationRelationship,
situationNodeSchema,
situationEdgeSchema,
situationGraphSchema,
graphUpdateSchema,
startCaseRequestSchema,
updateCaseRequestSchema,
makeNodeId,
makeNode,
makeEdge,
makeGraph,
} from "@/lib/graph/schema.js";
describe("situationNodeSchema", () => {
const validNode = {
id: "n1",
label: "Test Node",
description: "A test node",
kind: "observation",
status: "known",
confidence: "high",
value: null,
unit: null,
evidenceIds: [],
dependsOn: [],
affects: [],
parentId: null,
childIds: [],
};
it("validates a complete valid node", () => {
const result = situationNodeSchema.safeParse(validNode);
expect(result.success).toBe(true);
});
it("requires id", () => {
const invalid = { ...validNode, id: "" };
const result = situationNodeSchema.safeParse(invalid);
expect(result.success).toBe(false);
});
it("requires label", () => {
const invalid = { ...validNode, label: "" };
const result = situationNodeSchema.safeParse(invalid);
expect(result.success).toBe(false);
});
it("rejects invalid kind", () => {
const invalid = { ...validNode, kind: "nonexistent" };
const result = situationNodeSchema.safeParse(invalid);
expect(result.success).toBe(false);
});
it("rejects invalid status", () => {
const invalid = { ...validNode, status: "unknown_status" };
const result = situationNodeSchema.safeParse(invalid);
expect(result.success).toBe(false);
});
it("rejects invalid confidence", () => {
const invalid = { ...validNode, confidence: "extreme" };
const result = situationNodeSchema.safeParse(invalid);
expect(result.success).toBe(false);
});
it("allows numeric value", () => {
const node = { ...validNode, value: 42 };
const result = situationNodeSchema.safeParse(node);
expect(result.success).toBe(true);
});
it("allows string value", () => {
const node = { ...validNode, value: "active" };
const result = situationNodeSchema.safeParse(node);
expect(result.success).toBe(true);
});
});
describe("situationEdgeSchema", () => {
const validEdge = {
id: "e1",
fromNodeId: "n1",
toNodeId: "n2",
relationship: "supports",
confidence: "medium",
description: "Edge between nodes",
};
it("validates a complete valid edge", () => {
const result = situationEdgeSchema.safeParse(validEdge);
expect(result.success).toBe(true);
});
it("rejects invalid relationship type", () => {
const invalid = { ...validEdge, relationship: "invalid_rel" };
const result = situationEdgeSchema.safeParse(invalid);
expect(result.success).toBe(false);
});
it("validates all relationship types", () => {
for (const rel of Object.values(SituationRelationship)) {
const edge = { ...validEdge, relationship: rel };
const result = situationEdgeSchema.safeParse(edge);
expect(result.success).toBe(true);
}
});
it("rejects self-referencing edges", () => {
// Self-refs are structurally valid but semantically questionable
const edge = { ...validEdge, fromNodeId: "n1", toNodeId: "n1" };
const result = situationEdgeSchema.safeParse(edge);
expect(result.success).toBe(true); // Structure is valid; semantics checked elsewhere
});
});
describe("situationGraphSchema", () => {
const validGraph = {
centralStatement: "Test graph summary",
nodes: [makeNode({ id: "n1", label: "Node 1" })],
edges: [],
activeUnknownNodeId: null,
resolvedNodeIds: [],
currentSummary: "Initial summary",
};
it("validates a complete valid graph", () => {
const result = situationGraphSchema.safeParse(validGraph);
expect(result.success).toBe(true);
});
it("requires at least one node", () => {
const invalid = { ...validGraph, nodes: [] };
const result = situationGraphSchema.safeParse(invalid);
expect(result.success).toBe(false);
});
it("allows empty edges array", () => {
const graph = { ...validGraph, edges: [] };
const result = situationGraphSchema.safeParse(graph);
expect(result.success).toBe(true);
});
it("rejects missing centralStatement", () => {
const invalid = { ...validGraph, centralStatement: "" };
const result = situationGraphSchema.safeParse(invalid);
expect(result.success).toBe(false);
});
});
describe("graphUpdateSchema", () => {
it("validates empty update (no-op proposal)", () => {
const result = graphUpdateSchema.safeParse({});
expect(result.success).toBe(true);
});
it("validates a complete update", () => {
const node = makeNode({ id: "n2", label: "New Node" });
const edge = makeEdge({ fromNodeId: "n1", toNodeId: "n2" });
const result = graphUpdateSchema.safeParse({
addedNodes: [node],
updatedNodes: [
{
nodeId: "n1",
newStatus: "resolved",
previousStatus: "unknown",
reason: "Question answered",
},
],
addedEdges: [edge],
removedEdgeIds: ["e-old"],
resolvedUnknownNodeIds: ["n2"],
affectedNodeIds: ["n3"],
selectedQuestion: {
nodeId: "n2",
question: "What does this new node mean?",
reason: "A follow-up unknown remains.",
},
});
expect(result.success).toBe(true);
});
it("allows null selectedQuestion", () => {
const result = graphUpdateSchema.safeParse({
selectedQuestion: null,
});
expect(result.success).toBe(true);
});
it("rejects update with invalid node kind in addedNodes", () => {
const invalid = graphUpdateSchema.safeParse({
addedNodes: [
{
id: "x",
label: "Test",
kind: "invalid_kind",
description: "test",
status: "unknown",
confidence: "medium",
value: null,
unit: null,
evidenceIds: [],
dependsOn: [],
affects: [],
parentId: null,
childIds: [],
},
],
});
expect(invalid.success).toBe(false);
});
});
describe("API request schemas", () => {
describe("startCaseRequestSchema", () => {
it("validates scenario field", () => {
const result = startCaseRequestSchema.safeParse({
scenario: "Test scenario",
});
expect(result.success).toBe(true);
});
it("rejects empty scenario", () => {
const result = startCaseRequestSchema.safeParse({ scenario: "" });
expect(result.success).toBe(false);
});
it("rejects scenario over 10000 chars", () => {
const longScenario = "a".repeat(10001);
const result = startCaseRequestSchema.safeParse({
scenario: longScenario,
});
expect(result.success).toBe(false);
});
it("accepts optional promptVersion", () => {
const result = startCaseRequestSchema.safeParse({
scenario: "Test",
promptVersion: "v0.3",
});
expect(result.success).toBe(true);
});
});
describe("updateCaseRequestSchema", () => {
it("validates complete update request", () => {
const graph = makeGraph({
centralStatement: "Test scenario",
nodes: [makeNode({ id: "n1", label: "N" })],
currentSummary: "Current state of situation",
});
const result = updateCaseRequestSchema.safeParse({
situationGraph: graph,
previousQuestion: "What happened?",
answer: "This is the answer",
});
expect(result.success).toBe(true);
});
it("rejects missing situationGraph", () => {
const result = updateCaseRequestSchema.safeParse({
previousQuestion: "Q?",
answer: "A",
});
expect(result.success).toBe(false);
});
it("rejects answer over 5000 chars", () => {
const graph = makeGraph({
centralStatement: "Test",
nodes: [makeNode({ id: "n1", label: "N" })],
currentSummary: "Test summary",
});
const result = updateCaseRequestSchema.safeParse({
situationGraph: graph,
previousQuestion: "Q?",
answer: "x".repeat(5001),
});
expect(result.success).toBe(false);
});
});
});
describe("deterministic ID generation", () => {
it("generate consistent IDs for same label", () => {
const id1 = makeNodeId("Same Label");
const id2 = makeNodeId("Same Label");
expect(id1).toBe(id2);
});
it("generates different IDs for different labels", () => {
const id1 = makeNodeId("Label A");
const id2 = makeNodeId("Label B");
expect(id1).not.toBe(id2);
});
it("IDs are prefixed with 'n' and short", () => {
const id = makeNodeId(
"A very long label that would produce a longer hash if not truncated",
);
expect(id.startsWith("n")).toBe(true);
expect(id.length).toBeLessThan(15);
});
it("same kind of nodes get deterministic IDs", () => {
for (let i = 0; i < 10; i++) {
expect(makeNodeId("Test Node")).toBe(makeNodeId("Test Node"));
}
});
});
describe("helper functions", () => {
describe("makeNode", () => {
it("creates a minimal node with defaults", () => {
const node = makeNode({ label: "Minimal" });
const result = situationNodeSchema.safeParse(node);
expect(result.success).toBe(true);
expect(node.kind).toBe("observation");
expect(node.status).toBe("unknown");
expect(node.confidence).toBe("medium");
});
it("creates a node with custom kind/status", () => {
const node = makeNode({
label: "Custom",
kind: "metric",
status: "known",
confidence: "high",
value: 42,
unit: "count",
});
expect(node.kind).toBe("metric");
expect(node.status).toBe("known");
expect(node.confidence).toBe("high");
expect(node.value).toBe(42);
expect(node.unit).toBe("count");
});
it("generates ID from label if none provided", () => {
const node = makeNode({ label: "Auto-ID" });
expect(node.id.startsWith("n")).toBe(true);
});
});
describe("makeEdge", () => {
it("creates a minimal edge with defaults", () => {
const edge = makeEdge({ fromNodeId: "n1", toNodeId: "n2" });
const result = situationEdgeSchema.safeParse(edge);
expect(result.success).toBe(true);
});
it("generates description from node ids if not provided", () => {
const edge = makeEdge({ fromNodeId: "n-alpha", toNodeId: "n-beta" });
expect(edge.description).toContain("alpha");
expect(edge.description).toContain("beta");
});
});
describe("makeGraph", () => {
it("creates a minimal graph with defaults", () => {
const graph = makeGraph({
centralStatement: "Test",
currentSummary: "Default summary",
nodes: [makeNode({ id: "n1", label: "Placeholder" })],
});
const result = situationGraphSchema.safeParse(graph);
expect(result.success).toBe(true);
});
it("allows specifying nodes and edges", () => {
const graph = makeGraph({
centralStatement: "Full Graph",
currentSummary: "Full summary",
nodes: [makeNode({ id: "n1", label: "N1" })],
edges: [makeEdge({ fromNodeId: "n1", toNodeId: "n2" })],
});
expect(graph.nodes.length).toBe(1);
expect(graph.edges.length).toBe(1);
});
});
});
describe("enum values completeness", () => {
it("SituationKind has all expected values", () => {
const expected = [
"observation",
"reported_claim",
"metric",
"state",
"transition",
"relationship",
"assumption",
"unknown",
"conclusion",
];
const actual = Object.values(SituationKind);
expect(actual).toEqual(expect.arrayContaining(expected));
});
it("SituationStatus has all expected values", () => {
const expected = [
"known",
"unknown",
"provisional",
"supported",
"weakened",
"contradicted",
"resolved",
];
const actual = Object.values(SituationStatus);
expect(actual).toEqual(expect.arrayContaining(expected));
});
it("SituationRelationship has all expected values", () => {
const expected = [
"supports",
"weakens",
"contradicts",
"depends_on",
"causes",
"may_cause",
"measures",
"compares_with",
"updates",
"other",
];
const actual = Object.values(SituationRelationship);
expect(actual).toEqual(expect.arrayContaining(expected));
});
it("ConfidenceLevel has all expected values", () => {
const actual = Object.values(ConfidenceLevel);
expect(actual).toContain("low");
expect(actual).toContain("medium");
expect(actual).toContain("high");
});
});
@@ -0,0 +1,303 @@
import { describe, expect, it } from "vitest";
import {
formulateQuestion,
formulateTieResolutionQuestion,
} from "@/lib/graph/question-formulator.js";
import { makeEdge, makeGraph, makeNode } from "@/lib/graph/schema.js";
import {
explainUnknownSelection,
selectActiveUnknownCandidate,
} from "@/lib/graph/utils.js";
function buildLiveShapedGraph() {
const summary = makeNode({
id: "nnvog0y",
label:
"Revenue grew by 18% while corporate cash reserves declined over an identical time frame.",
description: "Summary of the situation from the scenario text",
kind: "state",
status: "provisional",
confidence: "medium",
});
const revenueObservation = makeNode({
id: "nri36w9",
label: "Revenue increased by 18%.",
description: "Revenue increased by 18%.",
kind: "observation",
status: "supported",
confidence: "high",
evidenceIds: ["obs_rev"],
});
const cashObservation = makeNode({
id: "nnfc48j",
label: "Cash in the bank decreased over the same period.",
description: "Cash in the bank decreased over the same period.",
kind: "observation",
status: "supported",
confidence: "high",
evidenceIds: ["obs_cash"],
});
const revenueMetric = makeNode({
id: "nhsd6d5",
label: "Revenue metric (typically accrual-based income statement figure)",
description:
"Revenue metric (typically accrual-based income statement figure)",
kind: "metric",
status: "known",
confidence: "high",
});
const cashMetric = makeNode({
id: "neh5m6m",
label:
"Cash balance (liquidity measure on the balance sheet or cash flow statement)",
description:
"Cash balance (liquidity measure on the balance sheet or cash flow statement)",
kind: "metric",
status: "known",
confidence: "high",
});
const directionalRelationship = makeNode({
id: "nwo6070",
label:
"Divergent directional movement between top-line revenue growth and net cash position contraction.",
description:
"Divergent directional movement between top-line revenue growth and net cash position contraction.",
kind: "relationship",
status: "supported",
confidence: "high",
});
const contradictionRelationship = makeNode({
id: "nuiab02",
label:
"Apparent contradiction between profitability/revenue expansion and liquidity reduction.",
description:
"Apparent contradiction between profitability/revenue expansion and liquidity reduction.",
kind: "relationship",
status: "supported",
confidence: "medium",
});
const cashTiming = makeNode({
id: "niewza",
label:
"Whether revenue recognition timing differs from cash collection timing.",
description:
"Whether revenue recognition timing differs from cash collection timing.",
kind: "unknown",
status: "unknown",
confidence: "high",
});
const cashOutflows = makeNode({
id: "nqdzobz",
label:
"Magnitude and nature of cash outflows (operating expenses, debt repayments, capex, or working capital shifts).",
description:
"Magnitude and nature of cash outflows (operating expenses, debt repayments, capex, or working capital shifts).",
kind: "unknown",
status: "unknown",
confidence: "high",
});
const edges = [
makeEdge({
id: "e-revenue-summary",
fromNodeId: revenueObservation.id,
toNodeId: summary.id,
relationship: "supports",
description: "Revenue increase supports the scenario summary.",
}),
makeEdge({
id: "e-cash-summary",
fromNodeId: cashObservation.id,
toNodeId: summary.id,
relationship: "supports",
description: "Cash decline supports the scenario summary.",
}),
makeEdge({
id: "e-unk-niewza",
fromNodeId: cashTiming.id,
toNodeId: summary.id,
relationship: "depends_on",
description:
"Whether revenue recognition timing differs from cash collection timing. is an unresolved factor for this situation",
}),
makeEdge({
id: "e-unk-nqdzobz",
fromNodeId: cashOutflows.id,
toNodeId: summary.id,
relationship: "depends_on",
description:
"Magnitude and nature of cash outflows (operating expenses, debt repayments, capex, or working capital shifts). is an unresolved factor for this situation",
}),
];
return makeGraph({
centralStatement:
"Revenue increased by 18%, but cash in the bank fell over the same period.",
nodes: [
summary,
revenueObservation,
cashObservation,
revenueMetric,
cashMetric,
directionalRelationship,
contradictionRelationship,
cashTiming,
cashOutflows,
],
edges,
activeUnknownNodeId: null,
resolvedNodeIds: [],
currentSummary: "Diagnostic selection influence fixture",
});
}
function orderCandidates(explanation) {
return explanation.candidates.map((candidate) => ({
nodeId: candidate.nodeId,
label: candidate.label,
score: candidate.score,
downstreamCount: candidate.downstreamCount,
unresolvedParentUnknownCount: candidate.unresolvedParentUnknownCount,
}));
}
function removeDependencyLinks(graph) {
const nodes = graph.nodes.map((node) => ({
...node,
dependsOn: [],
affects: [],
parentId: null,
childIds: [],
}));
const edges = (graph.edges || []).filter(
(edge) => edge.relationship !== "depends_on",
);
return makeGraph({ ...graph, nodes, edges, activeUnknownNodeId: null });
}
function neutraliseUnknownWording(graph) {
let counter = 0;
const nodes = graph.nodes.map((node) => {
if (node.kind !== "unknown") return { ...node };
counter += 1;
return {
...node,
label: `Unknown ${String.fromCharCode(64 + counter)}`,
description: `Unknown factor ${counter} relevant to the scenario.`,
};
});
return makeGraph({ ...graph, nodes, activeUnknownNodeId: null });
}
describe("selection influence diagnostic", () => {
it("records ambiguous ordering changes for live-shaped, structure-only, and wording-neutralised fixtures", () => {
const liveGraph = buildLiveShapedGraph();
const liveExplanation = explainUnknownSelection(liveGraph, []);
const liveSelection = selectActiveUnknownCandidate(liveGraph, []);
const tieQuestion = formulateTieResolutionQuestion({ graph: liveGraph });
const noLinksExplanation = explainUnknownSelection(
removeDependencyLinks(liveGraph),
[],
);
const noLinksSelection = selectActiveUnknownCandidate(
removeDependencyLinks(liveGraph),
[],
);
const neutralWordingExplanation = explainUnknownSelection(
neutraliseUnknownWording(liveGraph),
[],
);
const neutralSelection = selectActiveUnknownCandidate(
neutraliseUnknownWording(liveGraph),
[],
);
const fallbackQuestion = formulateQuestion({
node: liveGraph.nodes.find((node) => node.id === "nqdzobz"),
graph: liveGraph,
});
const diagnosticRecord = {
liveStatus: liveExplanation.status,
liveShapedCandidateOrdering: orderCandidates(liveExplanation),
liveTiedCandidateIds: liveExplanation.tiedCandidateIds,
noLinksCandidateOrdering: orderCandidates(noLinksExplanation),
noLinksStatus: noLinksExplanation.status,
neutralWordingCandidateOrdering: orderCandidates(
neutralWordingExplanation,
),
neutralStatus: neutralWordingExplanation.status,
selectedExplanationContributions: liveExplanation.selected?.contributions,
tieQuestion: tieQuestion.question,
liveSelection,
noLinksSelection,
neutralSelection,
fallbackQuestion,
};
expect(diagnosticRecord.liveStatus).toBe("ambiguous");
expect(diagnosticRecord.liveTiedCandidateIds).toEqual([
"nqdzobz",
"niewza",
]);
expect(diagnosticRecord.liveShapedCandidateOrdering).toEqual([
{
nodeId: "nqdzobz",
label:
"Magnitude and nature of cash outflows (operating expenses, debt repayments, capex, or working capital shifts).",
score: 0,
downstreamCount: 0,
unresolvedParentUnknownCount: 0,
},
{
nodeId: "niewza",
label:
"Whether revenue recognition timing differs from cash collection timing.",
score: 0,
downstreamCount: 0,
unresolvedParentUnknownCount: 0,
},
]);
expect(diagnosticRecord.liveSelection).toMatchObject({
selectedNode: null,
status: "ambiguous",
tieType: "complete_unresolved_tie",
tiedCandidateIds: ["nqdzobz", "niewza"],
});
expect(diagnosticRecord.noLinksCandidateOrdering).toEqual(
diagnosticRecord.liveShapedCandidateOrdering,
);
expect(diagnosticRecord.noLinksStatus).toBe("ambiguous");
expect(diagnosticRecord.noLinksSelection.status).toBe("ambiguous");
expect(diagnosticRecord.neutralWordingCandidateOrdering).toEqual([
{
nodeId: "niewza",
label: "Unknown A",
score: 0,
downstreamCount: 0,
unresolvedParentUnknownCount: 0,
},
{
nodeId: "nqdzobz",
label: "Unknown B",
score: 0,
downstreamCount: 0,
unresolvedParentUnknownCount: 0,
},
]);
expect(diagnosticRecord.neutralStatus).toBe("ambiguous");
expect(diagnosticRecord.neutralSelection.status).toBe("ambiguous");
expect(diagnosticRecord.selectedExplanationContributions).toBeUndefined();
expect(diagnosticRecord.tieQuestion).toBe(
"Were these figures measured on the same basis and at the same scale?",
);
expect(diagnosticRecord.tieQuestion.toLowerCase()).not.toMatch(
/accounts receivable|capex|debt repayments|working capital/,
);
expect(diagnosticRecord.fallbackQuestion.strategy).toBeNull();
expect(diagnosticRecord.fallbackQuestion.question).toBe(
"What would clarify magnitude and nature of cash outflows (operating expenses, debt repayments, capex, or working capital shifts) in this situation?",
);
});
});
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import { describe, expect, it } from "vitest";
import { parseGraphUpdateProposal } from "@/lib/graph/update-proposal.js";
function makeValidProposal(overrides = {}) {
return {
addedNodes: [],
updatedNodes: [
{
nodeId: "n-unknown",
previousStatus: "unknown",
newStatus: "resolved",
previousValue: null,
newValue: "1.9 complaints per 100 units",
reason: "The answer directly provides the normalized complaint rate.",
},
],
addedEdges: [],
removedEdgeIds: [],
resolvedUnknownNodeIds: ["n-unknown"],
affectedNodeIds: [],
selectedQuestion: null,
...overrides,
};
}
describe("parseGraphUpdateProposal", () => {
it("parses a valid proposal", () => {
const result = parseGraphUpdateProposal(makeValidProposal());
expect(result.success).toBe(true);
expect(result.proposal.updatedNodes).toHaveLength(1);
});
it("fails on malformed JSON", () => {
const result = parseGraphUpdateProposal("{not json");
expect(result.success).toBe(false);
});
it("fails when required update content is invalid", () => {
const result = parseGraphUpdateProposal({
updatedNodes: [{ nodeId: "n-unknown" }],
});
expect(result.success).toBe(false);
});
it("removes null array entries and logs them", () => {
const result = parseGraphUpdateProposal(
JSON.stringify({
...makeValidProposal(),
addedNodes: [null],
}),
);
expect(result.success).toBe(true);
expect(result.proposal.addedNodes).toEqual([]);
expect(result.normalisationsApplied).toEqual(
expect.arrayContaining([
expect.objectContaining({ change: "Removed null array entry" }),
]),
);
});
it("fills missing optional arrays with empty arrays", () => {
const result = parseGraphUpdateProposal({
updatedNodes: [],
});
expect(result.success).toBe(true);
expect(result.proposal.addedNodes).toEqual([]);
expect(result.proposal.addedEdges).toEqual([]);
expect(result.normalisationsApplied.length).toBeGreaterThan(0);
});
it("normalises confirmed enum alias and preserves IDs", () => {
const result = parseGraphUpdateProposal({
...makeValidProposal(),
addedNodes: [
{
id: "n-new",
label: "Reported update",
description: "A new reported claim",
kind: "reported_statement",
status: "supported",
confidence: "medium",
value: null,
unit: null,
evidenceIds: [],
dependsOn: [],
affects: [],
parentId: null,
childIds: [],
},
],
});
expect(result.success).toBe(true);
expect(result.proposal.addedNodes[0].kind).toBe("reported_claim");
expect(result.proposal.addedNodes[0].id).toBe("n-new");
});
it("unknown enum values still fail", () => {
const result = parseGraphUpdateProposal({
...makeValidProposal(),
addedNodes: [
{
id: "n-new",
label: "Bad node",
description: "Bad node",
kind: "unsupported_kind",
status: "supported",
confidence: "medium",
value: null,
unit: null,
evidenceIds: [],
dependsOn: [],
affects: [],
parentId: null,
childIds: [],
},
],
});
expect(result.success).toBe(false);
});
it("defaults missing selectedQuestion to null", () => {
const result = parseGraphUpdateProposal({
addedNodes: [],
updatedNodes: [],
addedEdges: [],
removedEdgeIds: [],
resolvedUnknownNodeIds: [],
affectedNodeIds: [],
});
expect(result.success).toBe(true);
expect(result.proposal.selectedQuestion).toBeNull();
});
it("parses a valid selectedQuestion", () => {
const result = parseGraphUpdateProposal(
makeValidProposal({
selectedQuestion: {
nodeId: "n-follow-up",
question: "How should commercial value be defined for this decision?",
reason: "A consequential unknown remains unresolved.",
},
}),
);
expect(result.success).toBe(true);
expect(result.proposal.selectedQuestion?.nodeId).toBe("n-follow-up");
});
it("does not invent a next question field outside the contract", () => {
const result = parseGraphUpdateProposal(makeValidProposal());
expect(result.proposal.nextQuestion).toBeUndefined();
});
});
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import { describe, expect, it } from "vitest";
import { applyValidatedProposal } from "@/lib/graph/apply-proposal.js";
import { makeEdge, makeGraph, makeNode } from "@/lib/graph/schema.js";
function makePropagationFixture({
key,
centralStatement,
firstObservationLabel,
secondObservationLabel,
}) {
const parent = makeNode({
id: `${key}-parent`,
label: `Explanation for why ${centralStatement}`,
description:
"Need to understand what change or event could explain why these observations differ, because that is needed to investigate their relationship.",
kind: "unknown",
status: "unknown",
confidence: "medium",
});
const measurementChild = makeNode({
id: `${key}-child-measurement`,
label: "How the two observations were measured",
description: `Need evidence about the measure used for each observation, because that could help explain ${centralStatement}.`,
kind: "unknown",
status: "unknown",
confidence: "medium",
parentId: parent.id,
});
const timingChild = makeNode({
id: `${key}-child-timing`,
label: "Whether the two observations reflect different timing",
description: `Need to know whether the two observations reflect different timing, because that could help explain ${centralStatement}.`,
kind: "unknown",
status: "unknown",
confidence: "medium",
parentId: parent.id,
});
const cashMovementChild = makeNode({
id: `${key}-child-cash-movement`,
label: `Possible change mainly affecting ${secondObservationLabel}`,
description: `Need to know whether a possible change mainly affected ${secondObservationLabel}, because that could help explain ${centralStatement}.`,
kind: "unknown",
status: "unknown",
confidence: "medium",
parentId: parent.id,
});
const oneOffChild = makeNode({
id: `${key}-child-one-off`,
label: "Possible one-off event during the period",
description: `Need to know whether a possible one-off event happened during the period, because that could help explain ${centralStatement}.`,
kind: "unknown",
status: "unknown",
confidence: "medium",
parentId: parent.id,
});
const ancestor = makeNode({
id: `${key}-ancestor`,
label: `Reasoning for ${centralStatement}`,
description:
"Higher-level reasoning node depending on the parent explanation.",
kind: "unknown",
status: "unknown",
confidence: "medium",
childIds: [parent.id],
});
const unrelated = makeNode({
id: `${key}-unrelated`,
label: "Unrelated branch",
description: "Should remain unchanged.",
kind: "unknown",
status: "unknown",
confidence: "low",
});
const firstObservation = makeNode({
id: `${key}-obs-1`,
label: firstObservationLabel,
description: firstObservationLabel,
kind: "observation",
status: "supported",
confidence: "high",
});
const secondObservation = makeNode({
id: `${key}-obs-2`,
label: secondObservationLabel,
description: secondObservationLabel,
kind: "observation",
status: "supported",
confidence: "high",
});
const graph = makeGraph({
centralStatement,
nodes: [
ancestor,
parent,
measurementChild,
timingChild,
cashMovementChild,
oneOffChild,
unrelated,
firstObservation,
secondObservation,
],
edges: [
makeEdge({
id: `${key}-e-parent-ancestor`,
fromNodeId: parent.id,
toNodeId: ancestor.id,
relationship: "depends_on",
description: "Ancestor depends on the parent explanation.",
}),
makeEdge({
id: `${key}-e-child-measurement-parent`,
fromNodeId: measurementChild.id,
toNodeId: parent.id,
relationship: "depends_on",
description: "Measurement child depends into the parent explanation.",
}),
makeEdge({
id: `${key}-e-child-timing-parent`,
fromNodeId: timingChild.id,
toNodeId: parent.id,
relationship: "depends_on",
description: "Timing child depends into the parent explanation.",
}),
makeEdge({
id: `${key}-e-child-cash-parent`,
fromNodeId: cashMovementChild.id,
toNodeId: parent.id,
relationship: "depends_on",
description: "Cash-movement child depends into the parent explanation.",
}),
makeEdge({
id: `${key}-e-child-one-off-parent`,
fromNodeId: oneOffChild.id,
toNodeId: parent.id,
relationship: "depends_on",
description: "One-off child depends into the parent explanation.",
}),
],
activeUnknownNodeId: measurementChild.id,
resolvedNodeIds: [],
currentSummary: `Propagation fixture for ${key}`,
});
return {
graph,
ids: {
ancestor: ancestor.id,
parent: parent.id,
measurementChild: measurementChild.id,
timingChild: timingChild.id,
cashMovementChild: cashMovementChild.id,
oneOffChild: oneOffChild.id,
unrelated: unrelated.id,
},
};
}
const scenarios = [
{
key: "revenue-cash",
centralStatement: "revenue increased while cash fell",
firstObservationLabel: "Revenue increased by 18%.",
secondObservationLabel: "Cash in the bank fell over the same period.",
},
{
key: "satisfaction-complaints",
centralStatement:
"customer satisfaction increased while complaints increased",
firstObservationLabel: "Customer satisfaction increased.",
secondObservationLabel: "Complaints increased.",
},
{
key: "traffic-sales",
centralStatement: "traffic increased while sales stayed flat",
firstObservationLabel: "Website traffic increased.",
secondObservationLabel: "Sales stayed flat.",
},
{
key: "delivery-cancellations",
centralStatement: "delivery time fell while cancellations increased",
firstObservationLabel: "Average delivery time decreased.",
secondObservationLabel: "Cancellations increased.",
},
{
key: "production-defects",
centralStatement: "production increased while defects increased",
firstObservationLabel: "Production increased.",
secondObservationLabel: "Defects increased.",
},
];
describe("upward propagation", () => {
it.each(scenarios)(
"propagates resolved measurement child upward for $key",
({
key,
centralStatement,
firstObservationLabel,
secondObservationLabel,
}) => {
const { graph, ids } = makePropagationFixture({
key,
centralStatement,
firstObservationLabel,
secondObservationLabel,
});
const unrelatedBefore = JSON.stringify(
graph.nodes.find((node) => node.id === ids.unrelated),
);
const result = applyValidatedProposal({
situationGraph: graph,
proposal: {
addedNodes: [
makeNode({
id: `${key}-anchor`,
label: "Update anchor",
description:
"Anchor state introduced by the answer because the update must contain a meaningful change.",
kind: "state",
status: "known",
confidence: "low",
}),
],
updatedNodes: [
{
nodeId: ids.measurementChild,
previousStatus: "unknown",
newStatus: "resolved",
previousValue: null,
newValue:
"The figures were measured over the same accounting period using the same management accounts.",
reason: "The answer resolves the measurement child.",
},
],
addedEdges: [],
removedEdgeIds: [],
resolvedUnknownNodeIds: [ids.measurementChild],
affectedNodeIds: [],
selectedQuestion: null,
},
previousQuestion:
"What evidence would clarify how the two observations were measured?",
answer:
"The figures were measured over the same accounting period using the same management accounts.",
});
expect(result.success).toBe(true);
expect(result.resolvedUnknownNodeIds).toContain(ids.measurementChild);
expect(result.propagationPerformed).toBe(true);
expect(result.resolvedChildNodeId).toBe(ids.measurementChild);
expect(result.parentNodeId).toBe(ids.parent);
expect(result.parentStatusBefore).toBe("unknown");
expect(result.parentStatusAfter).toBe("provisional");
expect(result.parentConfidenceBefore).toBe("medium");
expect(result.parentConfidenceAfter).toBe("medium");
expect(result.evidenceConfidenceBefore).toBe("medium");
expect(result.evidenceConfidenceAfter).toBe("medium");
expect(result.completenessBefore).toBe("empty");
expect(result.completenessAfter).toBe("partial");
expect(result.conclusionConfidenceBefore).toBe("low");
expect(result.conclusionConfidenceAfter).toBe("medium");
expect(result.confidenceCapReason).toBe(
"unresolved_direct_children_cap_conclusion",
);
expect(result.parentResolved).toBe(false);
expect(result.affectedAncestorIds).toContain(ids.parent);
expect(result.affectedAncestorIds).toContain(ids.ancestor);
expect(result.nextSelectedSibling).toBe(result.newActiveUnknownNodeId);
expect(result.nextSelectedSibling).toBe(result.selectedQuestion?.nodeId);
expect(result.nextSelectedSibling).not.toBe(ids.measurementChild);
expect([
ids.timingChild,
ids.cashMovementChild,
ids.oneOffChild,
]).toContain(result.nextSelectedSibling);
expect(result.selectedQuestion?.question.toLowerCase()).not.toContain(
"measured",
);
const parentNode = result.updatedSituationGraph.nodes.find(
(node) => node.id === ids.parent,
);
expect(parentNode).toMatchObject({
status: "provisional",
confidence: "medium",
confidenceAssessment: {
evidenceConfidence: "medium",
completenessStatus: "partial",
conclusionConfidence: "medium",
},
});
const ancestorNode = result.updatedSituationGraph.nodes.find(
(node) => node.id === ids.ancestor,
);
expect(ancestorNode).toMatchObject({
status: "provisional",
confidence: "low",
confidenceAssessment: {
evidenceConfidence: "low",
completenessStatus: "empty",
conclusionConfidence: "low",
},
});
const resolvedChild = result.updatedSituationGraph.nodes.find(
(node) => node.id === ids.measurementChild,
);
expect(resolvedChild.status).toBe("resolved");
expect(resolvedChild.evidenceIds).toContain(
`answer:${ids.measurementChild}`,
);
expect(
result.updatedSituationGraph.nodes.filter(
(node) => node.id === ids.measurementChild,
),
).toHaveLength(1);
expect(
JSON.stringify(
result.updatedSituationGraph.nodes.find(
(node) => node.id === ids.unrelated,
),
),
).toBe(unrelatedBefore);
},
);
it("resolves the parent only after all direct children are resolved", () => {
const { graph, ids } = makePropagationFixture({
key: "completion-rule",
centralStatement: "revenue increased while cash fell",
firstObservationLabel: "Revenue increased by 18%.",
secondObservationLabel: "Cash in the bank fell over the same period.",
});
const result = applyValidatedProposal({
situationGraph: graph,
proposal: {
addedNodes: [
makeNode({
id: "completion-rule-anchor",
label: "Update anchor",
description:
"Anchor state introduced by the answer because the update must contain a meaningful change.",
kind: "state",
status: "known",
confidence: "low",
}),
],
updatedNodes: [
{
nodeId: ids.measurementChild,
previousStatus: "unknown",
newStatus: "resolved",
previousValue: null,
newValue: "same management accounts",
reason: "resolved measurement child",
},
{
nodeId: ids.timingChild,
previousStatus: "unknown",
newStatus: "resolved",
previousValue: null,
newValue: "timing aligned",
reason: "resolved timing child",
},
{
nodeId: ids.cashMovementChild,
previousStatus: "unknown",
newStatus: "resolved",
previousValue: null,
newValue: "cash left through operations",
reason: "resolved movement child",
},
{
nodeId: ids.oneOffChild,
previousStatus: "unknown",
newStatus: "resolved",
previousValue: null,
newValue: "no exceptional movement",
reason: "resolved one-off child",
},
],
addedEdges: [],
removedEdgeIds: [],
resolvedUnknownNodeIds: [
ids.measurementChild,
ids.timingChild,
ids.cashMovementChild,
ids.oneOffChild,
],
affectedNodeIds: [],
selectedQuestion: null,
},
previousQuestion:
"What evidence would clarify how the two observations were measured?",
answer: "All direct child questions are now answered.",
});
expect(result.success).toBe(true);
expect(result.parentResolved).toBe(true);
expect(result.resolvedUnknownNodeIds).toContain(ids.parent);
expect(
result.updatedSituationGraph.nodes.find((node) => node.id === ids.parent),
).toMatchObject({
status: "resolved",
confidence: "high",
confidenceAssessment: {
evidenceConfidence: "high",
completenessStatus: "complete",
conclusionConfidence: "high",
},
});
});
});
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import { beforeEach, describe, expect, it, vi } from "vitest";
import { normaliseAnalysisResponse } from "@/lib/reconstruction/compatibility.js";
const mockGenerateReconstruction = vi.fn();
vi.mock("@/lib/config.js", () => ({
getConfig: () => ({
ok: true,
config: {
OLLAMA_BASE_URL: "http://example.test",
OLLAMA_MODEL: "test-model",
},
}),
}));
vi.mock("@/lib/llm/provider.js", () => ({
getProvider: () => ({
generateReconstruction: (...args) => mockGenerateReconstruction(...args),
}),
}));
vi.mock("@/lib/reconstruction/prompt.js", () => ({
buildPrompt: async () => ({ prompt: "prompt", version: "v0.3" }),
PROMPT_VERSIONS: ["v0.1", "v0.2", "v0.3"],
DEFAULT_PROMPT_VERSION: "v0.3",
}));
describe("normaliseAnalysisResponse", () => {
beforeEach(() => {
vi.resetModules();
vi.clearAllMocks();
});
it("leaves already-valid responses unchanged", () => {
const input = {
evidence: [
{
id: "ev1",
description: "x",
evidenceType: "reported_statement",
confidence: "medium",
importance: "important",
source: "report",
},
],
};
const result = normaliseAnalysisResponse(input);
expect(result.normalised).toEqual(input);
expect(result.changesApplied).toEqual([]);
});
it("normalises null evidence source deterministically", () => {
const input = {
evidence: [
{
id: "ev1",
description: "x",
evidenceType: "reported_statement",
confidence: "medium",
importance: "important",
source: null,
},
],
};
const result = normaliseAnalysisResponse(input);
expect(result.normalised.evidence[0]).not.toHaveProperty("source");
expect(result.changesApplied).toHaveLength(1);
});
it("does not invent a next question", () => {
const input = { evidence: [] };
const result = normaliseAnalysisResponse(input);
expect(result.normalised.nextQuestion).toBeUndefined();
});
it("does not repair missing reasoning content", () => {
const input = { evidence: [{ source: null }] };
const result = normaliseAnalysisResponse(input);
expect(result.normalised.reconstruction).toBeUndefined();
});
});
describe("analyseScenario compatibility", () => {
it("succeeds when the only mismatch is null evidence source", async () => {
mockGenerateReconstruction.mockResolvedValue({
inputClassification: {
primaryType: "unexplained_change",
secondaryTypes: [],
reasoningModes: ["validate_measurement"],
classificationReason: "reason",
confidence: "medium",
},
reconstruction: {
summary: "summary",
actors: [],
systemsOrObjects: [],
expectedStates: [],
observedStates: [],
differences: [],
knownTransitions: [],
unexplainedTransitions: [],
contradictions: [],
importantUnknowns: [],
plausibleInterpretations: [],
},
evidence: [
{
id: "ev1",
description: "desc",
evidenceType: "reported_statement",
source: null,
attribution: null,
confidence: "medium",
importance: "important",
},
],
nextQuestion: {
id: "q1",
question: "What denominator?",
targets: ["observedStates"],
reason: "reason",
expectedInformationValue: "high",
reasoningMode: "validate_measurement",
},
});
const { analyseScenario } = await import("@/lib/analysis.js");
const result = await analyseScenario("Scenario text", {
promptVersion: "v0.3",
});
expect(result.success).toBe(true);
expect(result.compatibilityApplied).toBe(true);
expect(result.compatibilityChanges).toHaveLength(1);
expect(result.evidence[0]).not.toHaveProperty("source");
expect(result.nextQuestion.question).toBe("What denominator?");
});
it("still fails when required reasoning content is missing", async () => {
mockGenerateReconstruction.mockResolvedValue({
evidence: [
{
id: "ev1",
description: "desc",
evidenceType: "reported_statement",
source: null,
attribution: null,
confidence: "medium",
importance: "important",
},
],
});
const { analyseScenario } = await import("@/lib/analysis.js");
const result = await analyseScenario("Scenario text", {
promptVersion: "v0.3",
});
expect(result.success).toBe(false);
expect(result.compatibilityApplied).toBe(true);
expect(result.nextQuestion).toBeUndefined();
});
it("malformed JSON still fails", async () => {
mockGenerateReconstruction.mockResolvedValue("{not valid json");
const { analyseScenario } = await import("@/lib/analysis.js");
const result = await analyseScenario("Scenario text", {
promptVersion: "v0.3",
});
expect(result.success).toBe(false);
expect(result.compatibilityApplied).toBe(false);
});
});
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import { test, expect } from "@playwright/test";
const BASE_URL = process.env.PLAYWRIGHT_BASE_URL || "http://localhost:3000";
test.setTimeout(300000);
test("graph-backed one-turn update smoke test", async ({ page }) => {
await page.goto(BASE_URL);
// Page should load without error
await expect(page.getByText(/Confidence Engine/i)).toBeVisible();
// Type the scenario
const textarea = page.locator("textarea[placeholder*='Describe']");
await textarea.fill(
"Complaints increased by 35% while production increased by 40%.",
);
await expect(textarea).toHaveValue(
"Complaints increased by 35% while production increased by 40%.",
);
// Button should be enabled
await expect(page.getByRole("button", { name: /Analyse/i })).toBeEnabled();
// Click Analyse and wait for graph-backed result
await page.getByRole("button", { name: /Analyse/i }).click();
await expect(
page
.locator("section")
.filter({ hasText: /Selected Question/i })
.last()
.getByRole("heading", { name: /Selected Question/i }),
).toBeVisible({ timeout: 180000 });
await expect(
page.getByRole("heading", { name: /Situation Graph/i }),
).toBeVisible({ timeout: 180000 });
await expect(page.getByText(/Central statement/i)).toBeVisible();
await expect(page.getByText(/Active unknown/i)).toBeVisible();
await expect(page.getByText(/Error:/i)).toHaveCount(0);
const rawJsonToggle = page.getByText(/Raw graph JSON/i);
await expect(rawJsonToggle).toBeVisible();
await rawJsonToggle.click();
await expect(page.getByText(/centralStatement/i)).toBeVisible();
const selectedQuestionSections = page
.locator("section")
.filter({ hasText: "Selected Question" });
await expect(selectedQuestionSections).toHaveCount(1);
const questionText = await selectedQuestionSections.first().innerText();
expect(questionText.length).toBeGreaterThan(25);
const answerTextarea = page.locator(
"textarea[placeholder*='Enter the answer']",
);
await expect(answerTextarea).toBeVisible();
await answerTextarea.fill(
"The complaint rate fell from 2.0 complaints per 100 units to 1.9 complaints per 100 units.",
);
await page.getByRole("button", { name: /Update situation/i }).click();
await expect(page.getByText(/Graph update applied/i)).toBeVisible({
timeout: 240000,
});
await expect(page.getByText(/Resolved unknowns/i)).toBeVisible();
await expect(page.getByText(/Newly surfaced unknowns/i)).toBeVisible();
await expect(page.getByText(/Affected nodes/i)).toBeVisible();
await expect(
page.getByText(/Selected Question|Next question:/i),
).toBeVisible();
await expect(
page.getByText(
/Additional submission is disabled in this one-update prototype\./i,
),
).toBeVisible();
await expect(page.getByText(/Error:/i)).toHaveCount(0);
await expect(page.getByText(/Update error:/i)).toHaveCount(0);
await expect(answerTextarea).toHaveValue("");
const proposalToggle = page.getByText(/Proposal details/i);
await expect(proposalToggle).toBeVisible();
await rawJsonToggle.click();
await expect(page.getByText(/resolvedNodeIds/i)).toBeVisible();
// Get full body text for verification
const bodyText = await page.locator("body").innerText();
// Check key content indicators
const hasSelectedQuestion = bodyText.includes("Selected Question");
const hasComplaints =
bodyText.includes("Complaint") || bodyText.includes("complaint");
const hasProduction =
bodyText.includes("production") || bodyText.includes("Production");
const hasRateContext =
bodyText.toLowerCase().includes("rate") ||
bodyText.toLowerCase().includes("unit") ||
bodyText.toLowerCase().includes("denominator") ||
bodyText.toLowerCase().includes("per-unit");
// Basic structural checks
expect(bodyText.length).toBeGreaterThan(400);
expect(hasSelectedQuestion).toBe(true);
expect(bodyText.includes("Resolved unknowns")).toBe(true);
expect(bodyText.includes("Affected nodes")).toBe(true);
});
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import React from "react";
import { describe, expect, it, vi } from "vitest";
import { renderToStaticMarkup } from "react-dom/server";
import DiagnosticsView from "@/components/diagnostics-view.jsx";
import GraphUpdateView from "@/components/graph-update-view.jsx";
import SituationGraphView from "@/components/situation-graph-view.jsx";
import {
ScenarioResultPanels,
UpdateErrorPanel,
submitAnswerForUpdateCase,
submitScenarioForStartCase,
} from "@/components/scenario-form.jsx";
function makeGraphResult(overrides = {}) {
return {
success: true,
situationGraph: {
centralStatement: "Complaints increased while production increased.",
currentSummary:
"Nodes: 2 observation, 1 unknown | Edges: 2 total | Unknowns: 1 unresolved",
activeUnknownNodeId: "n-unknown",
resolvedNodeIds: [],
nodes: [
{
id: "n-1",
label: "Complaints up 35%",
description: "Complaints increased by 35%",
kind: "observation",
status: "supported",
confidence: "high",
value: 35,
unit: "%",
},
{
id: "n-2",
label: "Production up 40%",
description: "Production increased by 40%",
kind: "observation",
status: "supported",
confidence: "high",
value: 40,
unit: "%",
},
{
id: "n-unknown",
label: "Complaint rate denominator",
description: "Need the denominator for complaint rate",
kind: "unknown",
status: "unknown",
confidence: "medium",
value: null,
unit: null,
},
],
edges: [
{ id: "e1", fromNodeId: "n-1", toNodeId: "n-unknown" },
{ id: "e2", fromNodeId: "n-2", toNodeId: "n-unknown" },
],
},
selectedQuestion: {
question: "What denominator is being used for the complaint rate?",
},
diagnostics: {
modelName: "test",
responseDurationMs: 1234,
validationStatus: "valid",
promptVersion: "test-prompt",
nodeCount: 3,
edgeCount: 2,
graphReferenceValidation: { valid: true, errors: [] },
},
...overrides,
};
}
function makeUpdateSuccess(overrides = {}) {
return {
success: true,
stage: "update_applied",
updatedSituationGraph: {
centralStatement: "Complaints increased while production increased.",
currentSummary: "Updated summary",
activeUnknownNodeId: "n-child-1",
resolvedNodeIds: ["n-unknown"],
nodes: [
{
id: "n-1",
label: "Complaints up 35%",
description: "Complaints increased by 35%",
kind: "observation",
status: "supported",
confidence: "high",
value: 35,
unit: "%",
},
{
id: "n-conclusion",
label: "Quality deterioration",
description: "Quality deterioration conclusion",
kind: "conclusion",
status: "weakened",
confidence: "medium",
value: null,
unit: null,
},
{
id: "n-unknown",
label: "Complaint rate denominator",
description: "Need the denominator for complaint rate",
kind: "unknown",
status: "resolved",
confidence: "medium",
value: "1.9 complaints per 100 units",
unit: null,
},
{
id: "n-next-unknown",
label:
"Explanation for why revenue increased by 18%, but cash in the bank fell over the same period",
description:
"Need to understand what change or event could explain why these observations differ, because that is needed to investigate their relationship.",
kind: "unknown",
status: "unknown",
confidence: "medium",
value: null,
unit: null,
},
{
id: "n-child-1",
label: "How the two observations were measured",
description:
"Need evidence about the measure used for each observation, because that could help explain revenue increased by 18%, but cash in the bank fell over the same period.",
kind: "unknown",
status: "unknown",
confidence: "medium",
value: null,
unit: null,
parentId: "n-next-unknown",
},
],
edges: [
{
id: "e-rel-next",
fromNodeId: "n-conclusion",
toNodeId: "n-next-unknown",
relationship: "depends_on",
confidence: "medium",
description:
"This unresolved explanation arises from the now-assessed relationship between the observations.",
},
{
id: "e-child-next",
fromNodeId: "n-child-1",
toNodeId: "n-next-unknown",
relationship: "depends_on",
confidence: "medium",
description:
"This child unknown must be investigated before the broader parent explanation can be resolved.",
},
],
},
proposal: {
addedNodes: [
{
id: "n-next-unknown",
label:
"Explanation for why revenue increased by 18%, but cash in the bank fell over the same period",
description:
"Need to understand what change or event could explain why these observations differ, because that is needed to investigate their relationship.",
kind: "unknown",
status: "unknown",
confidence: "medium",
value: null,
unit: null,
evidenceIds: [],
dependsOn: ["n-conclusion"],
affects: [],
parentId: "n-conclusion",
childIds: [],
},
{
id: "n-child-1",
label: "How the two observations were measured",
description:
"Need evidence about the measure used for each observation, because that could help explain revenue increased by 18%, but cash in the bank fell over the same period.",
kind: "unknown",
status: "unknown",
confidence: "medium",
value: null,
unit: null,
evidenceIds: [],
dependsOn: [],
affects: [],
parentId: "n-next-unknown",
childIds: [],
},
],
updatedNodes: [
{ nodeId: "n-unknown", newStatus: "resolved", reason: "answered" },
],
addedEdges: [],
removedEdgeIds: [],
resolvedUnknownNodeIds: ["n-unknown"],
affectedNodeIds: ["n-conclusion"],
selectedQuestion: {
nodeId: "n-child-1",
question:
"What evidence would clarify how the two observations were measured?",
reason:
"Formulated from graph context using the evidence_gathering investigation strategy.",
},
},
selectedQuestion: {
nodeId: "n-child-1",
question:
"What evidence would clarify how the two observations were measured?",
reason:
"Formulated from graph context using the evidence_gathering investigation strategy.",
},
affectedNodeIds: ["n-conclusion"],
resolvedUnknownNodeIds: ["n-unknown"],
previousActiveUnknownNodeId: "n-unknown",
newActiveUnknownNodeId: "n-child-1",
emergentReasoningNodeCreated: true,
emergentReasoningNodeId: "n-next-unknown",
emergentReasoningNodeReason:
"Created a new unresolved reasoning unknown so the next justified question is backed by the graph.",
atomicityAssessment: "composite",
decompositionPerformed: true,
childUnknownCount: 1,
childNodeIds: ["n-child-1"],
atomicityReason:
"Decomposed a composite unknown into smaller broad candidate dimensions before asking the next question.",
previousReasoningState: {
comparabilityStatus: "uncertain",
reasoningStages: [
{
stage: "comparability",
status: "uncertain",
outcome:
"Comparability between the observations is not yet established across period, scale, or measurement basis.",
},
{
stage: "relationship",
status: "insufficient_information",
outcome: "not assessed until comparability is established",
},
],
},
reasoningState: {
comparabilityStatus: "confirmed",
relationshipStatus: "insufficient_information",
reasoningStages: [
{
stage: "comparability",
status: "confirmed",
outcome:
"Comparability was confirmed by the user answer covering the same period and source basis.",
},
{
stage: "relationship",
status: "insufficient_information",
outcome:
"There is not enough structure to classify the relationship safely.",
},
],
},
changesApplied: {
updatedNodeCount: 2,
resolvedUnknownCount: 1,
affectedNodeCount: 1,
},
diagnostics: { responseDurationMs: 100 },
...overrides,
};
}
describe("scenario-form UI helpers", () => {
it("submits to /api/cases/start", async () => {
const fetchImpl = vi.fn().mockResolvedValue({ ok: true });
await submitScenarioForStartCase(fetchImpl, "Scenario text");
expect(fetchImpl).toHaveBeenCalledWith(
"/api/cases/start",
expect.objectContaining({
method: "POST",
headers: { "Content-Type": "application/json" },
}),
);
});
it("empty answer is rejected without fetch", async () => {
const fetchImpl = vi.fn();
const result = await submitAnswerForUpdateCase(fetchImpl, {
situationGraph: { nodes: [] },
previousQuestion: "What changed?",
answer: " ",
});
expect(result.skipped).toBe(true);
expect(fetchImpl).not.toHaveBeenCalled();
});
it("update request body contains graph, previousQuestion and answer", async () => {
const fetchImpl = vi.fn().mockResolvedValue({
ok: true,
json: async () => ({ success: true }),
});
const graph = { nodes: [{ id: "n1" }], edges: [] };
await submitAnswerForUpdateCase(fetchImpl, {
situationGraph: graph,
previousQuestion: "What changed?",
answer: "The rate fell.",
});
expect(fetchImpl).toHaveBeenCalledWith(
"/api/cases/update",
expect.objectContaining({
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({
situationGraph: graph,
previousQuestion: "What changed?",
answer: "The rate fell.",
}),
}),
);
});
});
describe("graph-backed UI rendering", () => {
it("renders central statement from successful graph response", () => {
const data = makeGraphResult();
const html = renderToStaticMarkup(
<SituationGraphView
situationGraph={data.situationGraph}
selectedQuestion={data.selectedQuestion}
/>,
);
expect(html).toContain("Central statement");
expect(html).toContain("Complaints increased while production increased.");
});
it("renders active unknown", () => {
const data = makeGraphResult();
const html = renderToStaticMarkup(
<SituationGraphView
situationGraph={data.situationGraph}
selectedQuestion={data.selectedQuestion}
/>,
);
expect(html).toContain("Active unknown");
expect(html).toContain("Complaint rate denominator");
});
it("renders selected question exactly once", () => {
const data = makeGraphResult();
const html = renderToStaticMarkup(
<SituationGraphView
situationGraph={data.situationGraph}
selectedQuestion={data.selectedQuestion}
/>,
);
expect(
html.match(/What denominator is being used for the complaint rate\?/g) ||
[],
).toHaveLength(1);
});
it("no answer form appears when selectedQuestion is null", () => {
const html = renderToStaticMarkup(
<SituationGraphView
situationGraph={makeGraphResult().situationGraph}
selectedQuestion={null}
/>,
);
expect(html).not.toContain("Update situation");
});
it("renders diagnostics", () => {
const html = renderToStaticMarkup(
<DiagnosticsView result={makeGraphResult()} />,
);
expect(html).toContain("Diagnostics");
expect(html).toContain("test");
expect(html).toContain("1234ms");
expect(html).toContain("Node count");
expect(html).toContain("Edge count");
expect(html).toContain("Graph references");
});
it("hides empty sections", () => {
const base = makeGraphResult();
const result = makeGraphResult({
selectedQuestion: null,
situationGraph: {
...base.situationGraph,
activeUnknownNodeId: null,
nodes: [base.situationGraph.nodes[0]],
edges: [],
},
});
const html = renderToStaticMarkup(
<SituationGraphView
situationGraph={result.situationGraph}
selectedQuestion={result.selectedQuestion}
/>,
);
expect(html).not.toContain("Selected Question");
expect(html).not.toContain("Active unknown");
});
it("displays API error clearly", () => {
const html = renderToStaticMarkup(
<ScenarioResultPanels
status="error"
result={{ error: "Invalid start-case request" }}
/>,
);
expect(html).toContain("Error: Invalid start-case request");
});
it("renders expandable raw graph JSON", () => {
const data = makeGraphResult();
const html = renderToStaticMarkup(
<SituationGraphView
situationGraph={data.situationGraph}
selectedQuestion={data.selectedQuestion}
/>,
);
expect(html).toContain("Raw graph JSON");
expect(html).toContain("&quot;centralStatement&quot;");
});
it("resolved unknowns render", () => {
const html = renderToStaticMarkup(
<GraphUpdateView
updateResult={{
...makeUpdateSuccess(),
previousSituationGraph: makeGraphResult().situationGraph,
}}
/>,
);
expect(html).toContain("Resolved unknowns");
expect(html).toContain("Complaint rate denominator");
});
it("newly surfaced unknowns render", () => {
const html = renderToStaticMarkup(
<GraphUpdateView
updateResult={{
...makeUpdateSuccess(),
previousSituationGraph: makeGraphResult().situationGraph,
}}
/>,
);
expect(html).toContain("Newly surfaced unknowns");
expect(html).toContain(
"Explanation for why revenue increased by 18%, but cash in the bank fell over the same period",
);
});
it("affected nodes render", () => {
const html = renderToStaticMarkup(
<GraphUpdateView
updateResult={{
...makeUpdateSuccess(),
previousSituationGraph: makeGraphResult().situationGraph,
}}
/>,
);
expect(html).toContain("Affected nodes");
expect(html).toContain("Quality deterioration");
});
it("renders validated next question when present", () => {
const html = renderToStaticMarkup(
<GraphUpdateView
updateResult={{
...makeUpdateSuccess(),
previousSituationGraph: makeGraphResult().situationGraph,
}}
/>,
);
expect(html).toContain(
"What evidence would clarify how the two observations were measured?",
);
});
it("no fake next question appears when there is none", () => {
const html = renderToStaticMarkup(
<GraphUpdateView
updateResult={{
...makeUpdateSuccess({
newActiveUnknownNodeId: null,
selectedQuestion: null,
proposal: {
...makeUpdateSuccess().proposal,
selectedQuestion: null,
},
}),
previousSituationGraph: makeGraphResult().situationGraph,
}}
/>,
);
expect(html).toContain("No next question selected yet.");
});
it("previous and new active unknowns render labels", () => {
const html = renderToStaticMarkup(
<GraphUpdateView
updateResult={{
...makeUpdateSuccess(),
previousSituationGraph: makeGraphResult().situationGraph,
}}
/>,
);
expect(html).toContain("Previous active unknown");
expect(html).toContain("Complaint rate denominator");
expect(html).toContain("New active unknown");
expect(html).toContain(
"Explanation for why revenue increased by 18%, but cash in the bank fell over the same period",
);
});
it("successful update renders prior and new state together", () => {
const html = renderToStaticMarkup(
<GraphUpdateView
updateResult={{
...makeUpdateSuccess(),
previousSituationGraph: makeGraphResult().situationGraph,
}}
/>,
);
expect(html).toContain("Previous active unknown");
expect(html).toContain("Resolved unknowns");
expect(html).toContain("Newly surfaced unknowns");
expect(html).toContain("New active unknown");
expect(html).toContain("Next question");
expect(html).toContain(
"What evidence would clarify how the two observations were measured?",
);
});
it("update view shows comparability progression without raw ids in the normal view", () => {
const html = renderToStaticMarkup(
<GraphUpdateView
updateResult={{
...makeUpdateSuccess({
selectedQuestion: {
nodeId: "n-next-unknown",
question:
"What evidence would clarify timing or measurement basis?",
reason: "A broad follow-up is now justified.",
},
}),
previousSituationGraph: makeGraphResult().situationGraph,
}}
/>,
);
expect(html).toContain("Comparability:");
expect(html).toContain("uncertain → confirmed");
expect(html).toContain("Relationship status:");
expect(html).toContain("insufficient_information");
expect(html).toContain("Reasoning stages:");
expect(html).toContain("comparability: confirmed");
expect(html).toContain("relationship: insufficient_information");
expect(html).toContain(
"What evidence would clarify how the two observations were measured?",
);
expect(html).not.toContain("reasoning:comparability");
});
it("situation graph marks newly surfaced and active unknowns", () => {
const html = renderToStaticMarkup(
<SituationGraphView
situationGraph={makeUpdateSuccess().updatedSituationGraph}
selectedQuestion={makeUpdateSuccess().selectedQuestion}
newlySurfacedNodeIds={["n-next-unknown"]}
/>,
);
expect(html).toContain("newly surfaced unknown");
expect(html).toContain("active unknown");
expect(html).toContain("resolved unknown");
});
it("disabled follow-up form is shown only as prototype limitation", () => {
const html = renderToStaticMarkup(
<GraphUpdateView
updateResult={makeUpdateSuccess()}
/>,
);
expect(html).toContain(
"What evidence would clarify how the two observations were measured?",
);
});
it("raw ids remain only in collapsed proposal details", () => {
const html = renderToStaticMarkup(
<GraphUpdateView
updateResult={{
...makeUpdateSuccess(),
previousSituationGraph: makeGraphResult().situationGraph,
}}
/>,
);
expect(html).toContain("Proposal details");
expect(html).toContain("&quot;resolvedUnknownNodeIds&quot;");
});
it("update diagnostics render valid values", () => {
const html = renderToStaticMarkup(
<DiagnosticsView
result={{
diagnostics: {
promptVersion: "v0.4",
modelName: "configured-model",
responseDurationMs: 456,
validationStatus: "valid",
nodeCount: 7,
edgeCount: 3,
graphReferenceValidation: { valid: true, errors: [] },
},
}}
/>,
);
expect(html).toContain("v0.4");
expect(html).toContain("456ms");
expect(html).toContain("7");
expect(html).toContain("3");
expect(html).toContain("✅ valid");
});
it("structured update error renders", () => {
const html = renderToStaticMarkup(
<UpdateErrorPanel
updateError={{
error: "Invalid graph update proposal",
proposalErrors: [{ message: "bad proposal" }],
}}
/>,
);
expect(html).toContain("Update error: Invalid graph update proposal");
expect(html).toContain("bad proposal");
});
it("failed update does not fabricate history", () => {
const html = renderToStaticMarkup(
<>
<UpdateErrorPanel
updateError={{
error: "Update case failed",
errors: [
'New unknown must be explicitly related to an answer-derived node: "nu_commercial_val"',
],
}}
/>
<GraphUpdateView updateResult={null} />
</>,
);
expect(html).toContain("Update error: Update case failed");
expect(html).toContain("nu_commercial_val");
expect(html).not.toContain("Previous active unknown");
expect(html).not.toContain("Resolved unknowns");
expect(html).not.toContain("Newly surfaced unknowns");
expect(html).not.toContain("New active unknown");
expect(html).not.toContain("Proposal details");
});
it("proposal details remain collapsible", () => {
const html = renderToStaticMarkup(
<GraphUpdateView updateResult={makeUpdateSuccess()} />,
);
expect(html).toContain("Proposal details");
});
});
+598
View File
@@ -0,0 +1,598 @@
import { describe, it, expect } from "vitest";
import { promises as fs } from "node:fs";
import { fileURLToPath } from "node:url";
import { dirname, join } from "node:path";
import {
PROMPT_VERSIONS,
buildPrompt,
DEFAULT_PROMPT_VERSION,
} from "@/lib/reconstruction/prompt.js";
import {
reconstructionV2Schema,
parseReconstructionV2,
} from "@/lib/reconstruction/schema.js";
const __filename = fileURLToPath(import.meta.url);
const __dirname = dirname(__filename);
const PROMPTS_DIR = join(__dirname, "../prompts");
// ──────────────────────────────────────────────
// v0.3 prompt loading tests
// ──────────────────────────────────────────────
describe("v0.3 prompt", () => {
it("v0.3 is in PROMPT_VERSIONS", () => {
expect(PROMPT_VERSIONS).toContain("v0.3");
});
it("DEFAULT_PROMPT_VERSION is v0.3 on this branch", () => {
expect(DEFAULT_PROMPT_VERSION).toBe("v0.3");
});
it("v0.2 remains available in PROMPT_VERSIONS", () => {
expect(PROMPT_VERSIONS).toContain("v0.2");
});
it("v0.3 prompt file loads from disk", async () => {
const content = await fs.readFile(
join(PROMPTS_DIR, "reconstruct-v0.3.md"),
"utf-8",
);
expect(typeof content).toBe("string");
expect(content.length).toBeGreaterThan(500);
});
it("v0.3 prompt contains normalisation guidance", async () => {
const content = await fs.readFile(
join(PROMPTS_DIR, "reconstruct-v0.3.md"),
"utf-8",
);
expect(content.toLowerCase()).toContain("normalise");
expect(content.toLowerCase()).toContain("rate");
expect(content.toLowerCase()).toContain("denominator") ||
expect(content.toLowerCase()).toContain("exposure");
});
it("v0.3 prompt contains discipline guidance", async () => {
const content = await fs.readFile(
join(PROMPTS_DIR, "reconstruct-v0.3.md"),
"utf-8",
);
// Should mention not generating speculative interpretations
expect(content).toMatch(/interpretation/i);
// Should mention one question discipline
expect(content).toMatch(/exactly.*one.*question|one.*only.*question|single.*question/i) ||
expect(content).toMatch(/Do NOT combine/i);
});
it("buildPrompt returns v0.3 prompt with scenario substituted", async () => {
const result = await buildPrompt("Test scenario text", "v0.3");
expect(result.version).toBe("v0.3");
expect(result.prompt).toContain("Test scenario text");
// Should contain the normalisation section guidance
expect(result.prompt.toLowerCase()).toContain("normalise");
});
it("buildPrompt returns v0.2 prompt when requested", async () => {
const result = await buildPrompt("Test scenario text", "v0.2");
expect(result.version).toBe("v0.2");
expect(result.prompt).toContain("Test scenario text");
});
it("buildPrompt default is v0.3", async () => {
const result = await buildPrompt("Test scenario text");
expect(result.version).toBe("v0.3");
});
});
// ──────────────────────────────────────────────
// v0.2 prompt still works
// ──────────────────────────────────────────────
describe("v0.2 backward compatibility", () => {
it("v0.2 prompt file exists and loads", async () => {
const content = await fs.readFile(
join(PROMPTS_DIR, "reconstruct-v0.2.md"),
"utf-8",
);
expect(typeof content).toBe("string");
expect(content.length).toBeGreaterThan(500);
});
it("buildPrompt returns v0.2 version string", async () => {
const result = await buildPrompt("test", "v0.2");
expect(result.version).toBe("v0.2");
});
});
// ──────────────────────────────────────────────
// Schema validation tests for v0.3-shaped output
// ──────────────────────────────────────────────
describe("v0.3 schema validation", () => {
it("validates a complete valid reconstruction with empty interpretations", () => {
const input = {
inputClassification: {
primaryType: "unexplained_change",
secondaryTypes: ["reported_claim"],
reasoningModes: ["identify_difference"],
classificationReason: "Two metrics changed without explanation.",
confidence: "medium",
},
reconstruction: {
summary: "Both complaints and production increased.",
actors: [],
systemsOrObjects: [
{ id: "complaints_metric", description: "Volume of complaints", confidence: "high" },
],
expectedStates: [],
observedStates: [
{ id: "obs1", description: "Complaint volume rose by 35%", confidence: "medium" },
{ id: "obs2", description: "Production volume rose by 40%", confidence: "medium" },
],
differences: [
{
id: "diff1",
description:
"Production grew faster than complaints, so the complaint-to-production ratio may have improved.",
confidence: "medium",
},
],
knownTransitions: [],
unexplainedTransitions: [
{
id: "trans1",
description: "Complaint volume shifted to a higher level without explained cause",
confidence: "medium",
entity: "complaints_metric",
previousState: "Baseline volume (unknown)",
currentState: "+35% increase",
},
],
contradictions: [],
importantUnknowns: [
{
id: "unk1",
description:
"Absolute baseline volumes and time period needed to compute complaint rate per unit",
confidence: "low",
},
],
plausibleInterpretations: [], // intentionally empty — evidence too thin
},
evidence: [
{
id: "ev1",
description: "Complaints increased by 35%",
evidenceType: "reported_statement",
source: "User input",
attribution: null,
confidence: "medium",
importance: "important",
},
{
id: "ev2",
description: "Production increased by 40%",
evidenceType: "reported_statement",
source: "User input",
attribution: null,
confidence: "medium",
importance: "important",
},
{
id: "ev3",
description:
"Production growth rate (40%) exceeded complaint growth rate (35%), implying the denominator may have grown faster than complaints.",
evidenceType: "inferred_relationship",
attribution: null,
confidence: "medium",
importance: "important",
},
],
nextQuestion: {
id: "q1",
question: "What was the complaint rate per unit before and after the production increase?",
targets: ["system"],
reason:
"Without normalising complaints by production volume, the absolute complaint count change is misleading. The rate per unit determines whether the situation improved, stayed stable, or worsened.",
expectedInformationValue: "high",
reasoningMode: "decompose_aggregate",
},
};
const result = reconstructionV2Schema.safeParse(input);
expect(result.success).toBe(true);
});
it("rejects output missing required fields", () => {
const input = {
inputClassification: { primaryType: "other" },
reconstruction: {},
evidence: [],
nextQuestion: { id: "q1" },
};
const result = reconstructionV2Schema.safeParse(input);
expect(result.success).toBe(false);
});
it("validates empty arrays for all reconstruction categories", () => {
const input = {
inputClassification: {
primaryType: "other",
classificationReason: "test",
confidence: "low",
},
reconstruction: {
summary: "empty test",
actors: [],
systemsOrObjects: [],
expectedStates: [],
observedStates: [],
differences: [],
knownTransitions: [],
unexplainedTransitions: [],
contradictions: [],
importantUnknowns: [],
plausibleInterpretations: [],
},
evidence: [],
nextQuestion: {
id: "q1",
question: "What is the production volume?",
targets: ["system"],
reason: "need baseline",
expectedInformationValue: "medium",
},
};
const result = reconstructionV2Schema.safeParse(input);
expect(result.success).toBe(true);
});
it("validates evidence distinguishing direct_observation from inferred_relationship", () => {
const input = {
inputClassification: {
primaryType: "unexplained_change",
classificationReason: "test",
confidence: "low",
},
reconstruction: {
summary: "test summary",
actors: [],
systemsOrObjects: [],
expectedStates: [],
observedStates: [{ id: "o1", description: "x", confidence: "high" }],
differences: [],
knownTransitions: [],
unexplainedTransitions: [],
contradictions: [],
importantUnknowns: [],
plausibleInterpretations: [],
},
evidence: [
{
id: "ev1",
description: "Observed fact",
evidenceType: "direct_observation",
confidence: "high",
importance: "critical",
},
{
id: "ev2",
description: "Derived relationship",
evidenceType: "inferred_relationship",
confidence: "medium",
importance: "supporting",
},
],
nextQuestion: {
id: "q1",
question: "What is the denominator?",
targets: ["system"],
reason: "need context",
expectedInformationValue: "high",
},
};
const result = reconstructionV2Schema.safeParse(input);
expect(result.success).toBe(true);
});
});
// ──────────────────────────────────────────────
// parseReconstructionV2 helper tests
// ──────────────────────────────────────────────
describe("parseReconstructionV2", () => {
it("parses a valid v0.3-shaped JSON string", async () => {
const fixture = {
inputClassification: {
primaryType: "unexplained_change",
classificationReason: "test",
confidence: "medium",
},
reconstruction: {
summary: "both increased",
actors: [],
systemsOrObjects: [],
expectedStates: [],
observedStates: [
{ id: "o1", description: "x rose 35%", confidence: "high" },
{ id: "o2", description: "y rose 40%", confidence: "high" },
],
differences: [{ id: "d1", description: "y grew faster", confidence: "medium" }],
knownTransitions: [],
unexplainedTransitions: [],
contradictions: [],
importantUnknowns: [],
plausibleInterpretations: [],
},
evidence: [
{ id: "e1", description: "x rose 35%", evidenceType: "reported_statement", confidence: "medium", importance: "important" },
{ id: "e2", description: "y rose 40%", evidenceType: "reported_statement", confidence: "medium", importance: "important" },
],
nextQuestion: {
id: "q1",
question: "What is the denominator?",
targets: ["system"],
reason: "need rate context",
expectedInformationValue: "high",
},
};
const raw = JSON.stringify(fixture);
const parsed = parseReconstructionV2(raw);
expect(parsed.inputClassification.primaryType).toBe("unexplained_change");
expect(parsed.reconstruction.summary).toBe("both increased");
expect(parsed.nextQuestion.question).toBe("What is the denominator?");
});
it("rejects non-JSON string", () => {
expect(() => parseReconstructionV2("{not valid json")).toThrow(SyntaxError);
});
});
// ──────────────────────────────────────────────
// v0.3 prompt contains required guidance text
// ──────────────────────────────────────────────
describe("v0.3 prompt guidance completeness", () => {
it("mentions normalise counts when scale changed", async () => {
const content = await fs.readFile(
join(PROMPTS_DIR, "reconstruct-v0.3.md"),
"utf-8",
);
expect(content.toLowerCase()).toMatch(/normali[sz]e|normalis[ei]ng/);
});
it("mentions distinguishing total count from rate", async () => {
const content = await fs.readFile(
join(PROMPTS_DIR, "reconstruct-v0.3.md"),
"utf-8",
);
expect(content.toLowerCase()).toContain("rate");
expect(content.toLowerCase()).toMatch(/count.*not.*caus|correlation.*caus|distinguish.*count/);
});
it("mentions avoiding correlation-as-causation", async () => {
const content = await fs.readFile(
join(PROMPTS_DIR, "reconstruct-v0.3.md"),
"utf-8",
);
expect(content.toLowerCase()).toMatch(/correlation.*caus|treating.*correlation.*caus/);
});
it("mentions prefer one narrow next question over compound", async () => {
const content = await fs.readFile(
join(PROMPTS_DIR, "reconstruct-v0.3.md"),
"utf-8",
);
// Should mention single vs compound
expect(content).toMatch(/exactly.*one|single.*question|Do NOT combine|combine.*multiple/i);
});
it("mentions leaving empty interpretations when evidence is thin", async () => {
const content = await fs.readFile(
join(PROMPTS_DIR, "reconstruct-v0.3.md"),
"utf-8",
);
expect(content).toMatch(/empty.*array|do not generate.*interpretation|fill a list/i);
});
it("mentions identifying the denominator or exposure metric", async () => {
const content = await fs.readFile(
join(PROMPTS_DIR, "reconstruct-v0.3.md"),
"utf-8",
);
expect(content.toLowerCase()).toMatch(/denominator|exposure/);
});
it("uses the exact scenario text as a reference example only (not in rules)", async () => {
const content = await fs.readFile(
join(PROMPTS_DIR, "reconstruct-v0.3.md"),
"utf-8",
);
// The prompt should be domain-independent — it should not mention specific industries as rules
// but may have an example section. We verify the prompt does not hard-code a specific question text.
expect(content).not.toMatch(/What was the complaint rate per unit before and after/);
});
});
// ──────────────────────────────────────────────
// Fixture: expected good structure for target scenario
// ──────────────────────────────────────────────
describe("target scenario fixture validation", () => {
const goodFixture = JSON.parse(JSON.stringify({
inputClassification: {
primaryType: "unexplained_change",
secondaryTypes: ["reported_claim"],
reasoningModes: ["identify_difference", "decompose_aggregate"],
classificationReason:
"Two operational quantities changed at different percentages without a shared baseline or denominator.",
confidence: "medium",
},
reconstruction: {
summary:
"Both complaint counts and production volumes increased, but production grew slightly faster than complaints — without absolute baselines the per-unit complaint rate cannot be determined.",
actors: [],
systemsOrObjects: [
{ id: "so1", description: "Production system or output volume", confidence: "high" },
{ id: "so2", description: "Complaint reporting mechanism", confidence: "high" },
],
expectedStates: [],
observedStates: [
{ id: "obs1", description: "Complaint count increased by 35%", confidence: "high" },
{ id: "obs2", description: "Production volume increased by 40%", confidence: "high" },
],
differences: [
{
id: "diff1",
description:
"Production grew faster than complaints (+40% vs +35%), so the ratio of complaints per unit may have decreased or remained stable. The absolute complaint count alone is not a reliable indicator of whether conditions have changed.",
confidence: "high",
},
],
knownTransitions: [],
unexplainedTransitions: [
{
id: "ut1",
description: "Complaint volume shifted to a higher level without explained cause",
confidence: "medium",
entity: "complaints_metric",
previousState: "unknown baseline",
currentState: "+35%",
},
],
contradictions: [],
importantUnknowns: [
{
id: "unk1",
description:
"Absolute complaint count and production volume baselines needed to compute the per-unit rate",
confidence: "low",
},
{
id: "unk2",
description: "Time period over which these changes occurred",
confidence: "low",
},
],
plausibleInterpretations: [], // intentionally empty — no sufficient evidence for interpretations
},
evidence: [
{
id: "ev1",
description: "Complaints increased by 35%",
evidenceType: "reported_statement",
source: "Scenario input",
attribution: null,
confidence: "high",
importance: "important",
},
{
id: "ev2",
description: "Production increased by 40%",
evidenceType: "reported_statement",
source: "Scenario input",
attribution: null,
confidence: "high",
importance: "important",
},
{
id: "ev3",
description: "Complaint count grew more slowly than production volume, suggesting per-unit rates may have improved or stayed stable.",
evidenceType: "inferred_relationship",
attribution: null,
confidence: "medium",
importance: "important",
},
],
nextQuestion: {
id: "q1",
question: "What was the absolute complaint volume and production volume (or baseline) before these percentage changes?",
targets: ["system", "measurement"],
reason:
"Without baseline counts to compute a rate per unit, we cannot determine whether conditions have worsened, stayed stable, or improved. The rate comparison is the smallest unresolved comparison needed to evaluate the situation.",
expectedInformationValue: "high",
reasoningMode: "decompose_aggregate",
},
}));
it("fixture validates against v0.3 schema", () => {
const result = reconstructionV2Schema.safeParse(goodFixture);
expect(result.success).toBe(true);
});
it("fixture has exactly one next question with non-empty text", () => {
expect(goodFixture.nextQuestion.question.length).toBeGreaterThan(10);
expect(goodFixture.nextQuestion.reason.length).toBeGreaterThan(10);
expect(goodFixture.nextQuestion.expectedInformationValue).toBe("high");
});
it("fixture has empty plausibleInterpretations (evidence too thin)", () => {
expect(goodFixture.reconstruction.plausibleInterpretations).toEqual([]);
});
it("fixture evidence includes both direct observations and one inferred relationship", () => {
const types = goodFixture.evidence.map((e) => e.evidenceType);
expect(types).toContain("reported_statement");
expect(types).toContain("inferred_relationship");
});
it("fixture relationship notes complaint count grew more slowly than production", () => {
const diffDescs = goodFixture.reconstruction.differences.map((d) => d.description);
const found = diffDescs.some(
(d) =>
d.toLowerCase().includes("fast") ||
d.toLowerCase().includes("slower") ||
d.toLowerCase().includes("ratio") ||
d.toLowerCase().includes("per-unit") ||
d.toLowerCase().includes("per unit"),
);
expect(found).toBe(true);
});
it("fixture does not assert quality deterioration", () => {
const allText = [
goodFixture.reconstruction.summary,
...goodFixture.reconstruction.differences.map((d) => d.description),
goodFixture.nextQuestion.reason,
].join(" ").toLowerCase();
// Should not contain strong deterioration language without caveats
expect(allText).not.toMatch(/quality.*deteriorat|quality.*worsen|definitely.*bad/);
});
it("fixture includes relationship that production grew faster", () => {
const allText = [
goodFixture.reconstruction.summary,
...goodFixture.reconstruction.differences.map((d) => d.description),
].join(" ").toLowerCase();
expect(allText).toMatch(/produ.*grow|ratio|per-unit|per unit|\+40.*\+35/);
});
});
// ──────────────────────────────────────────────
// Diagnostics: prompt version tracking
// ──────────────────────────────────────────────
describe("diagnostics prompt version", () => {
it("DEFAULT_PROMPT_VERSION is exported correctly", () => {
expect(DEFAULT_PROMPT_VERSION).toBe("v0.3");
});
it("PROMPT_VERSIONS includes both v0.2 and v0.3", () => {
const hasV2 = PROMPT_VERSIONS.includes("v0.2");
const hasV3 = PROMPT_VERSIONS.includes("v0.3");
expect(hasV2).toBe(true);
expect(hasV3).toBe(true);
});
it("RECONSTRUCTION_PROMPT_VERSION env var overrides default", async () => {
// The actual override happens at module load time, so we can't easily test this
// in isolation. Instead, verify the constant reflects env or defaults to v0.3.
expect(PROMPT_VERSIONS).toContain("v0.2");
});
});