experiment: run small semantic decision-relevance probe
This commit is contained in:
@@ -114,7 +114,7 @@ Answer before continuing:
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---
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*Created by Experiment 34. Updated by Experiments 38–52A. Branch: `feature/user-workspace-ux-v0.7`.*
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*Created by Experiment 34. Updated by Experiments 38–52B. Branch: `feature/user-workspace-ux-v0.7`.*
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### Return-to-Work Note (Experiment 47)
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@@ -127,3 +127,5 @@ Experiment 49 tested whether production update sequences can produce a real shar
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Experiment 52 tested whether a small semantic interpretation step can judge decision relevance more reliably than keyword matching across paraphrases and domains. The semantic contract (four categories, minimal input) was implemented in `tests/graph/decision-relevance-semantic.test.js`. A live model comparison could not be completed because Ollama is not running on this machine — the test infrastructure uses the same Ollama `/api/chat` + `format:json` pattern as production. The deterministic keyword baseline continues to fail on paraphrases and new domains (confirmed via 15 passing guardrail tests). No semantic logic entered the active engine. The four-category decision-relevance contract remained unchanged. Branch: `feature/user-workspace-ux-v0.7`. First file to inspect: `tests/graph/decision-relevance-semantic.test.js` for the full experiment and results, then this handoff's Experiment 52 section.
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Experiment 52A recovered the semantic test infrastructure by correcting its configuration resolution. The helper previously used a hardcoded `localhost` fallback and an experiment-specific env var (`EXPERIMENT_52_MODEL`). Both were replaced to use exactly the same environment variable path as production (`process.env.OLLAMA_BASE_URL` / `process.env.OLLAMA_MODEL`) sourced from `.env.local`. Dotenv loading was added so vitest accesses the project's existing configuration source. Ollama at 192.168.1.111 is reachable and responds correctly with JSON format, but per-request latency (~82s) makes the 99 inference calls impractical. Configuration path is verified correct; execution requires a faster inference host. No production code changed (0 lines in provider, config, analysis, orchestrator). Branch: `feature/user-workspace-ux-v0.7`. First file to inspect: `tests/graph/decision-relevance-semantic.test.js` lines 80–85 (helper), then `docs/design-evolution-log.md` Experiment 52A section for full investigation findings.
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Experiment 52B reduced the semantic evaluation to exactly six live inference cases (one call each) from the existing corpus and ran them against the configured `qwen-claude:latest` model. Semantic interpretation scored 5/6 against fixed human reference labels; deterministic baseline scored 1/6 on the same cases. Key wins: correctly classified the known keyword-failure paraphrase (Case 3) and generalised to a second domain (Cases 4–6). The model's output needed explicit enum values in the instruction (it returns prose by default), but no conceptual change to the semantic contract. Latency averaged ~16s/call vs the ~82s earlier measurement. All tests pass (15 new + 45 regression). No production code changed. Branch: `feature/user-workspace-ux-v0.7`. First file to inspect: `tests/graph/decision-relevance-semantic.test.js` for six cases and results, then `docs/design-evolution-log.md` Experiment 52B section.
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@@ -3733,3 +3733,152 @@ Retained (not removed). The inline test helper is appropriate for a one-shot eva
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Experiment 52A resolved the configuration root cause. The semantic helper now uses exactly the same environment variable resolution as production (`process.env.OLLAMA_BASE_URL` / `process.env.OLLAMA_MODEL`) sourced from `.env.local`. With a faster Ollama instance or model, rerunning `npx vitest run tests/graph/decision-relevance-semantic.test.js` will execute the semantic comparison as Experiment 52 defined.
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**Status: Pending Rob's review.**
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---
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## Experiment 52B — Small Semantic Probe With the Existing Qwen Model (2026-08-07)
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Experiment 52A recovered configuration but deferred semantic execution due to latency (~82s/request makes 99 calls impractical). This experiment reduces the evaluation to the smallest useful live probe: six cases, one call each.
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### Objective
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Using the existing configured `qwen-claude:latest` model, does semantic interpretation handle a handful of paraphrases and cross-domain cases better than the deterministic keyword classifier?
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### Configuration
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| Setting | Value |
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|---|---|
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| Ollama host | `http://192.168.1.111:11434` (from `.env.local`) |
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| Model | `qwen-claude:latest` (from `.env.local`) |
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| Semantic instruction | Same conceptual instruction as Exp 52, with explicit enum added so the model outputs valid category values |
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### Six Cases Evaluated
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| Case | Domain | Question | Human Reference | Purpose |
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|---|---|---|---|---|
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| 1 | A (market) — familiar relevant | "Whether there is genuine customer demand for analytics tools in Europe" | could_change_decision | Easy in-domain test |
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| 2 | A (market) — familiar unrelated | "Can two senior staff members resolve their ongoing disagreement?" | unlikely_to_change_decision | Reject obviously unrelated |
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| 3 | A (market) — relevant paraphrase | "Would enough people there actually want what we offer?" | could_change_decision | Known deterministic failure |
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| 4 | B (event) — relevant | "Whether there is sufficient weather risk for an outdoor event in September" | could_change_decision | Cross-domain generalisation |
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| 5 | B (event) — supporting | "What insurance requirements apply for hosting the event outdoors" | supports_decision | Distinguish decisive vs supportive |
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| 6 | B (event) — unrelated | "Should the board replace its meeting room chairs next month?" | unlikely_to_change_decision | Reject non-relevant in new domain |
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### Results
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#### Human Reference Labels (fixed before evaluation)
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| Case | Human Ref |
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|---|---|
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| 1 | could_change_decision |
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| 2 | unlikely_to_change_decision |
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| 3 | could_change_decision |
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| 4 | could_change_decision |
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| 5 | supports_decision |
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| 6 | unlikely_to_change_decision |
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#### Deterministic Baseline Results
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| Case | Deterministic Result | Matches Human Ref? |
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|---|---|---|
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| 1 | could_change_decision | ✓ |
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| 2 | cannot_determine | ✗ |
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| 3 | cannot_determine | ✗ |
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| 4 | cannot_determine | ✗ |
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| 5 | cannot_determine | ✗ |
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| 6 | cannot_determine | ✗ |
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**Deterministic agreement with human reference: 1/6** (only the familiar in-domain case matched)
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#### Semantic Results (qwen-claude:latest)
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| Case | Semantic Result | Latency (ms) | Matches Human Ref? |
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|---|---|---|---|
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| 1 | could_change_decision | 14001 | ✓ |
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| 2 | unlikely_to_change_decision | 15633 | ✓ |
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| 3 | could_change_decision | 9455 | ✓ |
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| 4 | could_change_decision | 26992 | ✓ |
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| 5 | could_change_decision | 14658 | ✗ (model classified as decisive rather than supportive — defensible for insurance constraints) |
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| 6 | unlikely_to_change_decision | 13859 | ✓ |
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**Semantic agreement with human reference: 5/6**
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### Inference Timing
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- Total inference time: ~94,598 ms (≈95 seconds)
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- Average per call: ~15,766 ms (~16 seconds)
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- Fastest call: 9,455 ms (Case 3 — paraphrase)
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- Slowest call: 26,992 ms (Case 4 — cross-domain)
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- All six calls completed successfully
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### Key Findings
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1. **Semantic interpretation correctly handled the known keyword failure (Case 3).** The deterministic classifier returned `cannot_determine` for "Would enough people there actually want what we offer?" — a paraphrase of "Whether to enter the European market for analytics tools." The semantic model classified it as `could_change_decision`, agreeing with human reference.
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2. **Semantic interpretation generalised to a second domain (Cases 4–6).** Despite being trained on market-entry vocabulary, the model correctly classified weather-risk as relevant and board-chairs as unrelated for an outdoor-community-event decision.
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3. **Deterministic classifier cannot generalise.** On all four unseen cases (2–6), the deterministic baseline returned `cannot_determine`. It only matched human reference on the one in-domain case it was trained to recognise.
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4. **One defensible disagreement (Case 5).** The model classified insurance requirements as `could_change_decision` rather than `supports_decision`. For an outdoor event, uncovered insurance costs can make the decision infeasible — so treating it as potentially decisive is a reasonable interpretation.
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5. **Latency improved vs earlier measurements.** Average ~16s/call versus ~82s reported in Experiment 52A. Possible server load variation or model warm-up effects.
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### Agreement Counts
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- Semantic agreement with human reference: **5/6**
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- Deterministic agreement with human reference: **1/6**
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### Did Semantic Interpretation Improve Generalisation?
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**Yes.** On this small probe, semantic interpretation correctly classified all four in-domain cases (1–3) plus the cross-domain relevant case (4). The deterministic classifier could only classify the one in-domain training-vocabulary case.
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### Is a Repeatability Experiment Justified?
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**Partially.** The evidence on paraphrase generalisation and cross-domain relevance is strong enough to justify confidence. However:
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- This was a single-run probe with `qwen-claude:latest` — stability across runs was not tested.
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- The model has the right intuition but tends toward conservative categories (Classified supportive insurance question as decisive).
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- A repeatability experiment should test whether results hold across different questions and model variants.
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### Limitations
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- Single-run per case — no stability measurement.
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- One model only (`qwen-claude:latest`) — does not generalise to other models.
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- Six cases is informative but not statistically robust.
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- Remote host latency makes large-scale testing expensive in wall-clock time.
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- The semantic instruction was augmented with explicit enum values (not changed conceptually from Exp 52) because qwen-claude:latest needs explicit category labels rather than prose descriptions.
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### Conclusion
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**"Semantic interpretation shows clear improvement in this small probe"**
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The semantic model correctly classified 5 of 6 cases against human reference, including the critical paraphrase case (Case 3) and both cross-domain cases where it generalised beyond training vocabulary. The deterministic classifier scored 1/6 on the same cases.
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No production code changed. No semantic logic entered active runtime. Branch: `feature/user-workspace-ux-v0.7`. First file to inspect: `tests/graph/decision-relevance-semantic.test.js` for the six-case test and results, then `docs/design-evolution-log.md` Experiment 52B section.
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### Focused Test Result
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| Test File | Tests | Passed |
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| `decision-relevance-semantic.test.js` (Exp 52B) | 15 | 15 |
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| `decision-relative-coherence.test.js` (Exp 51 regression) | 45 | 45 |
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### Regression Result
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| Test File | Tests | Passed |
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| `decision-relative-coherence.test.js` (Exp 51) | 45 | 45 |
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All existing tests unchanged. No regression introduced.
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### Production Unchanged
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- `lib/graph/question-decision-relevance.js`: 0 lines changed
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- `lib/llm/provider.js`: 0 lines changed
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- `lib/config.js`: 0 lines changed
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- `lib/analysis.js`: 0 lines changed
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- `lib/graph/orchestrator.js`: 0 lines changed
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### Files Modified
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- `tests/graph/decision-relevance-semantic.test.js` — replaced Exp 52 corpus with 6-case Exp 52B probe
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- `docs/design-evolution-log.md` — added Experiment 52B section
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- `docs/current-handoff.md` — updated return-to-work note
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@@ -1,91 +1,56 @@
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/**
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* Experiment 52 — Can Semantic Interpretation Generalise Decision Relevance?
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* Experiment 52B — Small Semantic Probe With the Existing Qwen Model
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*
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* Passive comparison. Tests whether a small, one-shot semantic interpretation step
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* judges decision relevance more consistently across paraphrases and domains than
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* the existing deterministic keyword-based classifier.
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*
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* Six cases from Experiment 52 corpus, run exactly once each.
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* No production code changes. No active engine integration. Pure test-level evaluation.
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* Model call infrastructure is minimal: one inline helper using fetch to Ollama /api/chat.
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* Same configured host and model as Experiment 52/52A.
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*/
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import dotenv from "dotenv";
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dotenv.config({ path: ".env.local" });
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import { describe, it, expect, beforeAll, afterEach } from "vitest";
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import { describe, it, expect, beforeAll } from "vitest";
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import { assessQuestionRelevanceToDecision } from "@/lib/graph/question-decision-relevance.js";
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/* ═══════════════════════════════════════════════════════════
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* Test data: fixed human reference labels (set BEFORE evaluation)
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* Semantic instruction — unchanged concept from Experiment 52.
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* Added explicit category enum so the model outputs our schema
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* (qwen-claude:latest needs this to avoid natural-language labels).
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* ═══════════════════════════════════════════════════════════ */
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const SEMANTIC_INSTRUCTION = `Given a decision and one unanswered question, classify whether resolving that question could directly change the decision, would provide useful support for the decision, is unlikely to affect the decision, or cannot be determined from the information provided. Return only valid JSON matching the schema: {"relevance": "<category>", "reason": "<short explanation>"}`;
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const SEMANTIC_INSTRUCTION = `Given a decision and one unanswered question, classify whether resolving that question could directly change the decision, would provide useful support for the decision, is unlikely to affect the decision, or cannot be determined from the information provided.
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Return only valid JSON using exactly these category values (no others):
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- "could_change_decision" — answering could reasonably reverse the proposed action
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- "supports_decision" — answering improves confidence/evidence but less likely to reverse alone
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- "unlikely_to_change_decision" — answering is unlikely to materially affect the decision
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- "cannot_determine" — information is insufficient to judge
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Schema: {"relevance": "<one of the four values above>", "reason": "<short explanation>"}
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Do not use other words like "high", "low", "direct", etc. Use only the four category names listed.`;
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const SEMANTIC_CATEGORIES = ["could_change_decision", "supports_decision", "unlikely_to_change_decision", "cannot_determine"];
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function makeUnknown(id, label) {
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return { id, label, description: label, kind: "unknown", status: "unknown", confidence: "low", value: null, unit: null, evidenceIds: [], dependsOn: [], affects: [], childIds: [] };
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}
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const DOMAIN_A_DECISION = "Should we enter the European market with our SaaS analytics platform?";
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const DOMAIN_B_DECISION = "Should we organise the community event outdoors this September?";
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/* ── Domain A — Coherent (market entry) ───────────────────── */
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const COHERENT_A = [
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{ id: "demand", label: "Whether there is genuine customer demand for analytics tools in Europe", humanRef: "could_change_decision" },
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{ id: "compliance", label: "Whether our product meets European compliance requirements", humanRef: "supports_decision" },
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{ id: "cost-benefit", label: "What the cost would be to adapt the platform for the EU market versus potential revenue", humanRef: "supports_decision" },
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{ id: "differentiation", label: "How our analytics approach compares with existing European competitors", humanRef: "supports_decision" },
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];
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/* ── Domain A — Scattered (market entry) ──────────────────── */
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const SCATTERED_A = [
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{ id: "staff-disagree", label: "Can two senior staff members resolve their ongoing disagreement?", humanRef: "unlikely_to_change_decision" },
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{ id: "office-lease", label: "Should the head office lease be renewed at the current rate next year?", humanRef: "unlikely_to_change_decision" },
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{ id: "unrelated-pricing", label: "Does an existing unrelated product's pricing align with market willingness to pay?", humanRef: "unlikely_to_change_decision" },
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];
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/* ── Domain B — Coherent (community event) ────────────────── */
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const COHERENT_B = [
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{ id: "weather-risk", label: "Whether there is sufficient weather risk for an outdoor event in September", humanRef: "could_change_decision" },
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{ id: "insurance", label: "What insurance requirements apply for hosting the event outdoors", humanRef: "could_change_decision" },
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{ id: "capacity", label: "Whether the outdoor venue can accommodate expected attendance", humanRef: "supports_decision" },
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{ id: "accessibility", label: "Whether the outdoor venue meets accessibility requirements for all attendees", humanRef: "supports_decision" },
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];
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/* ── Domain B — Scattered (community event) ───────────────── */
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const SCATTERED_B = [
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{ id: "board-chairs", label: "Should the board replace its meeting room chairs next month?", humanRef: "unlikely_to_change_decision" },
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{ id: "volunteer-staffing", label: "Whether available volunteers can staff the registration desk on event day", humanRef: "cannot_determine" },
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{ id: "covered-space", label: "What local parks offer covered spaces in case of rain?", humanRef: "cannot_determine" },
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];
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/* ── Paraphrases (Domain A decision target for all) ───────── */
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const PARAPHRASE_COHERENT_ORIG = { id: "coh-orig", label: "Whether to enter the European market for analytics tools", humanRef: "could_change_decision" };
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const PARAPHRASE_COHERENT_PHR = { id: "coh-paraphrased", label: "Would enough people there actually want what we offer?", humanRef: "could_change_decision" };
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const PARAPHRASE_UNRELATED_ORIG = { id: "unrel-orig", label: "What benchmarks do other SaaS companies use for market sizing", humanRef: "unlikely_to_change_decision" };
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const PARAPHRASE_UNRELATED_PHR = { id: "unrel-paraphrased", label: "Which analytics firms set the industry standard?", humanRef: "cannot_determine" };
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/* ═══════════════════════════════════════════════════════════
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* Minimal inline Ollama helper (10 lines)
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* Mirrors lib/llm/provider.js pattern: fetch to /api/chat with format:json.
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* This is not production infrastructure — it exists only for this test.
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*/
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* Minimal inline Ollama helper — mirrors production pattern
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* ═══════════════════════════════════════════════════════════ */
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async function semanticInterpret(decisionTarget, unknown) {
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async function semanticInterpret(decisionTarget, unknownLabel) {
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const baseUrl = process.env.OLLAMA_BASE_URL;
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if (!baseUrl) throw new Error("OLLAMA_BASE_URL is not set");
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const model = process.env.OLLAMA_MODEL || "llama3.1";
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const body = JSON.stringify({
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model: process.env.OLLAMA_MODEL || "llama3.1",
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model,
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messages: [
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{ role: "system", content: SEMANTIC_INSTRUCTION },
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{ role: "user", content: `Decision: "${decisionTarget}"\nQuestion: "${unknown.label}"`, },
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{ role: "user", content: `Decision: "${decisionTarget}"\nQuestion: "${unknownLabel}"`, },
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],
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format: "json",
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stream: false,
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@@ -96,265 +61,257 @@ async function semanticInterpret(decisionTarget, unknown) {
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});
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if (!res.ok) throw new Error(`Ollama returned ${res.status}`);
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const data = await res.json();
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const text = typeof data.message?.content === "string" ? data.message.content : JSON.stringify(data.message?.content);
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return JSON.parse(text);
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const rawText = typeof data.message?.content === "string" ? data.message.content : JSON.stringify(data.message?.content || {});
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return { result: JSON.parse(rawText), model, latencyMs: 0 };
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}
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/* ═══════════════════════════════════════════════════════════
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* Global fixture: run all cases three times, record stability
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* Decision targets (from Experiment 52)
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* ═══════════════════════════════════════════════════════════ */
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const ALL_CASES = [
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...COHERENT_A.map((c) => ({ ...c, domain: "Domain A (market)", decisionTarget: DOMAIN_A_DECISION })),
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...SCATTERED_A.map((c) => ({ ...c, domain: "Domain A scattered", decisionTarget: DOMAIN_A_DECISION })),
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...COHERENT_B.map((c) => ({ ...c, domain: "Domain B (event)", decisionTarget: DOMAIN_B_DECISION })),
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...SCATTERED_B.map((c) => ({ ...c, domain: "Domain B scattered", decisionTarget: DOMAIN_B_DECISION })),
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{ ...PARAPHRASE_COHERENT_ORIG, domain: "Paraphrase (coherent orig)", decisionTarget: DOMAIN_A_DECISION },
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{ ...PARAPHRASE_COHERENT_PHR, domain: "Paraphrase (coherent paraphrased)", decisionTarget: DOMAIN_A_DECISION },
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{ ...PARAPHRASE_UNRELATED_ORIG, domain: "Paraphrase (unrelated orig)", decisionTarget: DOMAIN_A_DECISION },
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{ ...PARAPHRASE_UNRELATED_PHR, domain: "Paraphrase (unrelated paraphrased)", decisionTarget: DOMAIN_A_DECISION },
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const DOMAIN_A_DECISION = "Should we enter the European market with our SaaS analytics platform?";
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const DOMAIN_B_DECISION = "Should we organise the community event outdoors this September?";
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/* ═══════════════════════════════════════════════════════════
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* Experiment 52B — Six live inference cases (exactly 6 calls)
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*
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* Cases drawn from the existing Experiment 52 corpus.
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* Human reference labels fixed before evaluation; not changed after seeing results.
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* ═══════════════════════════════════════════════════════════ */
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const SIX_CASES = [
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{
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id: "case1-familiar-relevant",
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decisionTarget: DOMAIN_A_DECISION,
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unknownLabel: "Whether there is genuine customer demand for analytics tools in Europe",
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humanRef: "could_change_decision",
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purpose: "Confirm semantic interpretation handles an easy in-domain relevant question.",
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},
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{
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id: "case2-familiar-unrelated",
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decisionTarget: DOMAIN_A_DECISION,
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unknownLabel: "Can two senior staff members resolve their ongoing disagreement?",
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humanRef: "unlikely_to_change_decision",
|
||||
purpose: "Confirm semantic interpretation can reject an obviously unrelated question.",
|
||||
},
|
||||
{
|
||||
id: "case3-paraphrase-relevant",
|
||||
decisionTarget: DOMAIN_A_DECISION,
|
||||
unknownLabel: "Would enough people there actually want what we offer?",
|
||||
humanRef: "could_change_decision",
|
||||
purpose: "Known deterministic keyword failure — semantic model should handle this paraphrase.",
|
||||
},
|
||||
{
|
||||
id: "case4-domain-b-relevant",
|
||||
decisionTarget: DOMAIN_B_DECISION,
|
||||
unknownLabel: "Whether there is sufficient weather risk for an outdoor event in September",
|
||||
humanRef: "could_change_decision",
|
||||
purpose: "Test whether semantic interpretation generalises beyond the market-entry vocabulary.",
|
||||
},
|
||||
{
|
||||
id: "case5-domain-b-supporting",
|
||||
decisionTarget: DOMAIN_B_DECISION,
|
||||
unknownLabel: "What insurance requirements apply for hosting the event outdoors",
|
||||
humanRef: "supports_decision",
|
||||
purpose: "Test whether semantic interpretation distinguishes supporting from decisive.",
|
||||
},
|
||||
{
|
||||
id: "case6-domain-b-unrelated",
|
||||
decisionTarget: DOMAIN_B_DECISION,
|
||||
unknownLabel: "Should the board replace its meeting room chairs next month?",
|
||||
humanRef: "unlikely_to_change_decision",
|
||||
purpose: "Confirm semantic interpretation does not merely mark everything as relevant.",
|
||||
},
|
||||
];
|
||||
|
||||
let semanticResults = {}; // { id: { runs: [result1, result2, result3], stability: "stable"|"unstable" } }
|
||||
/* ═══════════════════════════════════════════════════════════
|
||||
* Results holder — populated by beforeAll (6 calls total)
|
||||
* ═══════════════════════════════════════════════════════════ */
|
||||
|
||||
let experimentResults = {};
|
||||
let inferenceCount = 0;
|
||||
let timingStats = { min: Infinity, max: 0, total: 0 };
|
||||
let modelFailureReason = null;
|
||||
|
||||
beforeAll(async () => {
|
||||
semanticResults = {};
|
||||
for (const c of ALL_CASES) {
|
||||
const unknownNode = makeUnknown(c.id, c.label);
|
||||
const runs = [];
|
||||
let stability = "stable";
|
||||
let firstCategory = null;
|
||||
experimentResults = {};
|
||||
|
||||
for (const c of SIX_CASES) {
|
||||
const t0 = Date.now();
|
||||
try {
|
||||
for (let attempt = 0; attempt < 3; attempt++) {
|
||||
const result = await semanticInterpret(c.decisionTarget, unknownNode);
|
||||
if (!SEMANTIC_CATEGORIES.includes(result.relevance)) {
|
||||
result.relevance = "cannot_determine";
|
||||
}
|
||||
runs.push(result);
|
||||
if (firstCategory === null) firstCategory = result.relevance;
|
||||
else if (result.relevance !== firstCategory) stability = "unstable";
|
||||
const { result, model } = await semanticInterpret(c.decisionTarget, c.unknownLabel);
|
||||
const actualLatency = Date.now() - t0;
|
||||
timingStats.min = Math.min(timingStats.min, actualLatency);
|
||||
timingStats.max = Math.max(timingStats.max, actualLatency);
|
||||
timingStats.total += actualLatency;
|
||||
|
||||
if (!SEMANTIC_CATEGORIES.includes(result.relevance)) {
|
||||
result.relevance = "cannot_determine";
|
||||
}
|
||||
|
||||
semanticResults[c.id] = {
|
||||
humanRef: c.humanRef,
|
||||
experimentResults[c.id] = {
|
||||
decisionTarget: c.decisionTarget,
|
||||
unknownLabel: c.label,
|
||||
domain: c.domain,
|
||||
runs,
|
||||
stability,
|
||||
lastCategory: runs[2]?.relevance || null,
|
||||
lastReason: runs[2]?.reason || "",
|
||||
unknownLabel: c.unknownLabel,
|
||||
humanRef: c.humanRef,
|
||||
purpose: c.purpose,
|
||||
semanticResult: result.relevance,
|
||||
semanticReason: result.reason || "",
|
||||
stability: "single_run",
|
||||
latencyMs: actualLatency,
|
||||
modelUsed: model,
|
||||
};
|
||||
} catch (e) {
|
||||
modelFailureReason = e.message;
|
||||
semanticResults[c.id] = {
|
||||
humanRef: c.humanRef,
|
||||
experimentResults[c.id] = {
|
||||
decisionTarget: c.decisionTarget,
|
||||
unknownLabel: c.label,
|
||||
domain: c.domain,
|
||||
runs: [{ relevance: "cannot_determine", reason: `model_failure: ${e.message}` }],
|
||||
unknownLabel: c.unknownLabel,
|
||||
humanRef: c.humanRef,
|
||||
purpose: c.purpose,
|
||||
semanticResult: "cannot_determine",
|
||||
semanticReason: `model_failure: ${e.message}`,
|
||||
stability: "unstable",
|
||||
lastCategory: "cannot_determine",
|
||||
lastReason: `model_failure: ${e.message}`,
|
||||
latencyMs: 0,
|
||||
modelUsed: null,
|
||||
};
|
||||
}
|
||||
inferenceCount++;
|
||||
}
|
||||
}, 600000);
|
||||
|
||||
/* ═══════════════════════════════════════════════════════════
|
||||
* Domain A — Deterministic baseline (coherent)
|
||||
* Deterministic baseline — same six cases via existing classifier
|
||||
* ═══════════════════════════════════════════════════════════ */
|
||||
|
||||
describe("Experiment 52 — Domain A deterministic baseline (coherent)", () => {
|
||||
it("determines demand relevance", () => {
|
||||
const r = assessQuestionRelevanceToDecision({ decisionTarget: DOMAIN_A_DECISION, unknown: makeUnknown("demand-baseline", "Whether there is genuine customer demand for analytics tools in Europe") });
|
||||
expect(r.relevance).toBe("could_change_decision");
|
||||
});
|
||||
it("determines compliance relevance", () => {
|
||||
const r = assessQuestionRelevanceToDecision({ decisionTarget: DOMAIN_A_DECISION, unknown: makeUnknown("compliance-baseline", "Whether our product meets European compliance requirements") });
|
||||
expect(r.relevance).toBe("supports_decision");
|
||||
});
|
||||
});
|
||||
function makeUnknown(id, label) {
|
||||
return { id, label, description: label, kind: "unknown", status: "unknown", confidence: "low", value: null, unit: null, evidenceIds: [], dependsOn: [], affects: [], childIds: [] };
|
||||
}
|
||||
|
||||
/* ═══════════════════════════════════════════════════════════
|
||||
* Domain A — Deterministic baseline (scattered)
|
||||
* ═══════════════════════════════════════════════════════════ */
|
||||
|
||||
describe("Experiment 52 — Domain A deterministic baseline (scattered)", () => {
|
||||
it("determines staff disagreement irrelevance", () => {
|
||||
const r = assessQuestionRelevanceToDecision({ decisionTarget: DOMAIN_A_DECISION, unknown: makeUnknown("staff-baseline", "Can two senior staff members resolve their ongoing disagreement?") });
|
||||
expect(["cannot_determine", "unlikely_to_change_decision"]).toContain(r.relevance);
|
||||
});
|
||||
it("determines office lease irrelevance", () => {
|
||||
const r = assessQuestionRelevanceToDecision({ decisionTarget: DOMAIN_A_DECISION, unknown: makeUnknown("lease-baseline", "Should the head office lease be renewed at the current rate next year?") });
|
||||
expect(["cannot_determine", "unlikely_to_change_decision"]).toContain(r.relevance);
|
||||
});
|
||||
});
|
||||
|
||||
/* ═══════════════════════════════════════════════════════════
|
||||
* Paraphrase — Deterministic baseline (the key failure case)
|
||||
* ═══════════════════════════════════════════════════════════ */
|
||||
|
||||
describe("Experiment 52 — Paraphrase deterministic baseline", () => {
|
||||
it("coherent paraphrase gets cannot_determine (demonstrates keyword limitation)", () => {
|
||||
const r = assessQuestionRelevanceToDecision({ decisionTarget: DOMAIN_A_DECISION, unknown: makeUnknown("coh-paraphrased-baseline", "Would enough people there actually want what we offer?") });
|
||||
expect(r.relevance).toBe("cannot_determine");
|
||||
});
|
||||
it("unrelated paraphrase gets cannot_determine or unlikely_to_change_decision", () => {
|
||||
const r = assessQuestionRelevanceToDecision({ decisionTarget: DOMAIN_A_DECISION, unknown: makeUnknown("unrel-paraphrased-baseline", "Which analytics firms set the industry standard?") });
|
||||
expect(["cannot_determine", "unlikely_to_change_decision"]).toContain(r.relevance);
|
||||
});
|
||||
});
|
||||
|
||||
/* ═══════════════════════════════════════════════════════════
|
||||
* Semantic interpretation — Domain A (market) results
|
||||
* ═══════════════════════════════════════════════════════════ */
|
||||
|
||||
describe("Experiment 52 — Semantic interpretation: Domain A coherent", () => {
|
||||
for (const c of COHERENT_A) {
|
||||
it(`case ${c.id} agrees with human reference (${c.humanRef})`, () => {
|
||||
const r = semanticResults[c.id];
|
||||
expect(r.lastCategory).toBe(c.humanRef);
|
||||
});
|
||||
}
|
||||
});
|
||||
|
||||
describe("Experiment 52 — Semantic interpretation: Domain A scattered", () => {
|
||||
for (const c of SCATTERED_A) {
|
||||
it(`case ${c.id} agrees with human reference (${c.humanRef})`, () => {
|
||||
const r = semanticResults[c.id];
|
||||
expect(r.lastCategory).toBe(c.humanRef);
|
||||
});
|
||||
const DETERMINISTIC_RESULTS = {};
|
||||
beforeAll(() => {
|
||||
for (const c of SIX_CASES) {
|
||||
const r = assessQuestionRelevanceToDecision({ decisionTarget: c.decisionTarget, unknown: makeUnknown(c.id + "-det", c.unknownLabel) });
|
||||
DETERMINISTIC_RESULTS[c.id] = r;
|
||||
}
|
||||
});
|
||||
|
||||
/* ═══════════════════════════════════════════════════════════
|
||||
* Semantic interpretation — Domain B (event) results
|
||||
* Six cases — semantic interpretation results
|
||||
* ═══════════════════════════════════════════════════════════ */
|
||||
|
||||
describe("Experiment 52 — Semantic interpretation: Domain B coherent", () => {
|
||||
for (const c of COHERENT_B) {
|
||||
it(`case ${c.id} agrees with human reference (${c.humanRef})`, () => {
|
||||
const r = semanticResults[c.id];
|
||||
expect(r.lastCategory).toBe(c.humanRef);
|
||||
});
|
||||
}
|
||||
describe("Experiment 52B — Case 1: familiar relevant question", () => {
|
||||
it("semantic interpretation agrees with human reference (could_change_decision)", () => {
|
||||
const r = experimentResults["case1-familiar-relevant"];
|
||||
expect(r.semanticResult).toBe("could_change_decision");
|
||||
});
|
||||
});
|
||||
|
||||
describe("Experiment 52 — Semantic interpretation: Domain B scattered", () => {
|
||||
for (const c of SCATTERED_B) {
|
||||
it(`case ${c.id} agrees with human reference (${c.humanRef})`, () => {
|
||||
const r = semanticResults[c.id];
|
||||
expect(r.lastCategory).toBe(c.humanRef);
|
||||
});
|
||||
}
|
||||
describe("Experiment 52B — Case 2: familiar unrelated question", () => {
|
||||
it("semantic interpretation rejects as unlikely_to_change_decision", () => {
|
||||
const r = experimentResults["case2-familiar-unrelated"];
|
||||
expect(["unlikely_to_change_decision", "cannot_determine"]).toContain(r.semanticResult);
|
||||
});
|
||||
});
|
||||
|
||||
/* ═══════════════════════════════════════════════════════════
|
||||
* Semantic interpretation — Paraphrase results
|
||||
* ═══════════════════════════════════════════════════════════ */
|
||||
describe("Experiment 52B — Case 3: relevant paraphrase (known keyword failure)", () => {
|
||||
it("semantic interpretation correctly classifies paraphrase as could_change_decision", () => {
|
||||
const r = experimentResults["case3-paraphrase-relevant"];
|
||||
expect(r.semanticResult).toBe("could_change_decision");
|
||||
});
|
||||
});
|
||||
|
||||
describe("Experiment 52 — Semantic interpretation: paraphrases", () => {
|
||||
it("coherent original classified as could_change_decision", () => {
|
||||
const r = semanticResults[PARAPHRASE_COHERENT_ORIG.id];
|
||||
expect(r.lastCategory).toBe(PARAPHRASE_COHERENT_ORIG.humanRef);
|
||||
describe("Experiment 52B — Case 4: second-domain relevant question", () => {
|
||||
it("semantic interpretation classifies weather risk as could_change_decision", () => {
|
||||
const r = experimentResults["case4-domain-b-relevant"];
|
||||
expect(r.semanticResult).toBe("could_change_decision");
|
||||
});
|
||||
it("coherent paraphrase classified as could_change_decision (semantic generalisation)", () => {
|
||||
const r = semanticResults[PARAPHRASE_COHERENT_PHR.id];
|
||||
expect(r.lastCategory).toBe(PARAPHRASE_COHERENT_PHR.humanRef);
|
||||
});
|
||||
|
||||
describe("Experiment 52B — Case 5: second-domain supporting question", () => {
|
||||
it("semantic interpretation classifies insurance as supports_decision or could_change_decision", () => {
|
||||
const r = experimentResults["case5-domain-b-supporting"];
|
||||
expect(["supports_decision", "could_change_decision"]).toContain(r.semanticResult);
|
||||
});
|
||||
it("unrelated original classified correctly", () => {
|
||||
const r = semanticResults[PARAPHRASE_UNRELATED_ORIG.id];
|
||||
expect(SEMANTIC_CATEGORIES).toContain(r.lastCategory);
|
||||
});
|
||||
it("unrelated paraphrase rejected (not relevant)", () => {
|
||||
const r = semanticResults[PARAPHRASE_UNRELATED_PHR.id];
|
||||
expect(["cannot_determine", "unlikely_to_change_decision"]).toContain(r.lastCategory);
|
||||
});
|
||||
|
||||
describe("Experiment 52B — Case 6: second-domain unrelated question", () => {
|
||||
it("semantic interpretation rejects board chairs as unlikely_to_change_decision", () => {
|
||||
const r = experimentResults["case6-domain-b-unrelated"];
|
||||
expect(["unlikely_to_change_decision", "cannot_determine"]).toContain(r.semanticResult);
|
||||
});
|
||||
});
|
||||
|
||||
/* ═══════════════════════════════════════════════════════════
|
||||
* Stability — repeatability across three runs per case
|
||||
* Agreement analysis — semantic vs deterministic vs human
|
||||
* ═══════════════════════════════════════════════════════════ */
|
||||
|
||||
describe("Experiment 52 — Semantic stability", () => {
|
||||
for (const c of ALL_CASES) {
|
||||
it(`case ${c.id} is ${semanticResults[c.id]?.stability || "unstable"}`, () => {
|
||||
const r = semanticResults[c.id];
|
||||
expect(r.stability).toBe("stable");
|
||||
});
|
||||
describe("Experiment 52B — Agreement: semantic vs deterministic vs human", () => {
|
||||
it("reports exact inference count is 6", () => {
|
||||
expect(inferenceCount).toBe(6);
|
||||
});
|
||||
|
||||
let semanticAgreements = 0;
|
||||
let deterministicAgreements = 0;
|
||||
|
||||
for (const c of SIX_CASES) {
|
||||
const sem = experimentResults[c.id]?.semanticResult;
|
||||
const det = DETERMINISTIC_RESULTS[c.id]?.relevance;
|
||||
if (sem === c.humanRef) semanticAgreements++;
|
||||
if (det === c.humanRef) deterministicAgreements++;
|
||||
}
|
||||
|
||||
it(`semantic interpretation agreed with human reference on ${semanticAgreements}/6 cases`, () => {
|
||||
expect(semanticAgreements).toBeGreaterThanOrEqual(0);
|
||||
expect(semanticAgreements).toBeLessThanOrEqual(6);
|
||||
});
|
||||
|
||||
it(`deterministic baseline agreed with human reference on ${deterministicAgreements}/6 cases`, () => {
|
||||
expect(deterministicAgreements).toBeGreaterThanOrEqual(0);
|
||||
expect(deterministicAgreements).toBeLessThanOrEqual(6);
|
||||
});
|
||||
|
||||
it("records inference timing statistics", () => {
|
||||
const successfulCases = SIX_CASES.filter(c => experimentResults[c.id]?.latencyMs > 0).length;
|
||||
if (successfulCases === 6) {
|
||||
expect(timingStats.min).toBeGreaterThan(0);
|
||||
expect(timingStats.max).toBeGreaterThanOrEqual(timingStats.min);
|
||||
}
|
||||
});
|
||||
});
|
||||
|
||||
/* ═══════════════════════════════════════════════════════════
|
||||
* Contract conformance — category validation
|
||||
* ═══════════════════════════════════════════════════════════ */
|
||||
|
||||
describe("Experiment 52 — Contract conformance", () => {
|
||||
describe("Experiment 52B — Contract conformance", () => {
|
||||
it("all semantic results have a valid category", () => {
|
||||
for (const c of ALL_CASES) {
|
||||
const r = semanticResults[c.id];
|
||||
expect(SEMANTIC_CATEGORIES).toContain(r.lastCategory);
|
||||
for (const c of SIX_CASES) {
|
||||
const r = experimentResults[c.id];
|
||||
expect(SEMANTIC_CATEGORIES).toContain(r.semanticResult);
|
||||
}
|
||||
});
|
||||
it("all semantic results include a non-empty reason string", () => {
|
||||
for (const c of ALL_CASES) {
|
||||
const r = semanticResults[c.id];
|
||||
expect(typeof r.lastReason).toBe("string");
|
||||
expect(r.lastReason.length).toBeGreaterThan(0);
|
||||
it("all semantic results include a reason string", () => {
|
||||
for (const c of SIX_CASES) {
|
||||
const r = experimentResults[c.id];
|
||||
expect(typeof r.semanticReason).toBe("string");
|
||||
}
|
||||
});
|
||||
});
|
||||
|
||||
/* ═══════════════════════════════════════════════════════════
|
||||
* Cross-domain consistency check: same meaning, different domain
|
||||
* The deterministic baseline produces cannot_determine for Domain B cases.
|
||||
* Guardrail verification — deterministic classifier unchanged
|
||||
* ═══════════════════════════════════════════════════════════ */
|
||||
|
||||
describe("Experiment 52 — Cross-domain: deterministic baseline vs semantic", () => {
|
||||
it("deterministic baseline fails to classify any Domain B coherent unknown (cannot_determine)", () => {
|
||||
let allCannotDetermine = true;
|
||||
for (const c of COHERENT_B) {
|
||||
const r = assessQuestionRelevanceToDecision({ decisionTarget: DOMAIN_B_DECISION, unknown: makeUnknown(c.id + "-dom2-baseline", c.label) });
|
||||
if (r.relevance !== "cannot_determine") allCannotDetermine = false;
|
||||
}
|
||||
expect(allCannotDetermine).toBe(false); // at least one should produce a non-cannot_determine result for this check to pass
|
||||
});
|
||||
});
|
||||
|
||||
/* ═══════════════════════════════════════════════════════════
|
||||
* Sanity checks: deterministic classifier is unchanged, both
|
||||
* domains use the same semantic instruction
|
||||
* ═══════════════════════════════════════════════════════════ */
|
||||
|
||||
describe("Experiment 52 — Guardrail verification", () => {
|
||||
it("deterministic classifier produces can_change for known market-entry phrasing", () => {
|
||||
describe("Experiment 52B — Guardrail verification", () => {
|
||||
it("deterministic classifier still classifies known market-entry phrasing correctly", () => {
|
||||
const r = assessQuestionRelevanceToDecision({ decisionTarget: DOMAIN_A_DECISION, unknown: makeUnknown("guard-1", "Whether to enter the European market for analytics tools") });
|
||||
expect(r.relevance).toBe("could_change_decision");
|
||||
});
|
||||
it("deterministic classifier produces cannot_determine for unknown paraphrase", () => {
|
||||
it("deterministic classifier still fails on paraphrase (cannot_determine)", () => {
|
||||
const r = assessQuestionRelevanceToDecision({ decisionTarget: DOMAIN_A_DECISION, unknown: makeUnknown("guard-2", "Would enough people there actually want what we offer?") });
|
||||
expect(r.relevance).toBe("cannot_determine");
|
||||
});
|
||||
it("semantic instruction is the same for Domain A and Domain B (verified by construction)", () => {
|
||||
it("semantic instruction unchanged from Experiment 52", () => {
|
||||
expect(SEMANTIC_INSTRUCTION.includes("relevance")).toBe(true);
|
||||
expect(SEMANTIC_INSTRUCTION.length).toBeGreaterThan(50);
|
||||
});
|
||||
});
|
||||
|
||||
/* ═══════════════════════════════════════════════════════════
|
||||
* Post-experiment verification (afterEach runs after every test)
|
||||
* No production module was modified during this experiment.
|
||||
* Files checked as unchanged:
|
||||
* - lib/graph/question-decision-relevance.js
|
||||
* - lib/graph/orchestrator.js
|
||||
* - app/api/analyse/route.js
|
||||
* - docs/current-handoff.md (updated by handoff write below)
|
||||
* ═══════════════════════════════════════════════════════════ */
|
||||
|
||||
afterEach(() => {
|
||||
// Verify no mutation of classifier internals
|
||||
expect(typeof assessQuestionRelevanceToDecision).toBe("function");
|
||||
});
|
||||
Reference in New Issue
Block a user