experiment: test clarification null stability
This commit is contained in:
@@ -22,7 +22,7 @@
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Experiment 54R tested whether a consequential disagreement actually requires user clarification or can be resolved through evidence. Three fixed cases: competing delivery causes (evidence-resolvable → false), ambiguous growth-versus-risk priority (user-owned → true), no-material-disagreement control (false). All three correct (3/3) in one live inference call per case (~40s total). Across the three tested disagreement patterns, the model did not automatically map disagreement to user clarification. The Case 1 evaluator warning was a false positive from heuristic wording checks, not a semantic failure. No production code changed. Status pending Rob's review.
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Experiment 54S tested whether, once clarification is known to be required, the model can identify exactly what the user needs to clarify — three fixed cases: growth-versus-risk priority (true → "preference/trade-off or hard constraint"), evidence-resolvable delivery causes (false → null), ambiguous meaning of "affordable" (true → "upfront cost versus long-term total cost"). 3/3 correctly classified. The model can identify the specific unresolved user-owned distinction without generating a question, choosing a winner, or changing Behaviour Selection. One behavioral note: when requiresUserClarification is false, the model sometimes defaults to producing a target rather than respecting null — but this did not affect any of the three tested cases in this run. Same host/model; no production code changed. Status pending Rob's review.
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Experiment 54S tested whether, once clarification is known to be required, the model can identify exactly what the user needs to clarify — three fixed cases: growth-versus-risk priority (true → "preference/trade-off or hard constraint"), evidence-resolvable delivery causes (false → null), ambiguous meaning of "affordable" (true → "upfront cost versus long-term total cost"). The final run was 3/3 correct, but earlier repetitions showed instability when clarification was explicitly not required. Concept-overlap counts were diagnostic only; manual semantic review provided stronger evidence. Case 2 instability is an observed behaviour, not merely a test warning. Clarification-target identification appears promising, but null enforcement is not yet stable. Experiment 54T confirmed null-gating was stable across three repeated identical calls in a stability-only follow-up test (Case A: 3/3 null; Case B control: 3/3 correct target). Same host/model; no production code changed. Status pending Rob's review.
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- `docs/task-context-packs.md` chooses the minimum context documents for each work type.
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@@ -144,8 +144,8 @@ Answer before continuing:
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---
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*Created by Experiment 34. Updated by Experiments 38–53, 54A–54S. Branch: `feature/user-workspace-ux-v0.7`.*
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*Created by Experiment 34. Updated by Experiments 38–53, 54A–54T. Branch: `feature/user-workspace-ux-v0.7`.*
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### Return-to-Work Note (Experiment 54S)
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### Return-to-Work Note (Experiment 54T)
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54R showed that evidence uncertainty and user-owned ambiguity can be distinguished across three tested patterns. 54S tested whether, once clarification is known to be required, the model can identify exactly what the user needs to clarify — growth-versus-risk priority correctly identified preference/trade-off versus hard constraint; delivery causes correctly returned null (evidence-resolvable); affordable ambiguity correctly identified upfront versus long-term cost meaning. 3/3 clarified-target classification correct. No clarification question was generated. Behaviour Selection remained untouched. Same host/model (qwen-claude:latest on 192.168.1.111:11434); no production behaviour changed. Branch: `feature/user-workspace-ux-v0.7`. First test/file to inspect when resuming: `tests/reconstruction/semantic-clarification-target.test.js`.
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54S correctly identified clarification targets in its final run but earlier false/null behaviour was unstable across unrecorded repetitions. 54T repeated one `requiresUserClarification: false` case and one true control three times each to test stability. False consistently returned null (3/3). True consistently returned the intended semantic target (3/3). No asymmetric instability was observed in this experiment. No prompt correction or production integration was attempted. Behaviour Selection remained untouched. Same host/model (qwen-claude:latest on 192.168.1.111:11434); no production behaviour changed. Branch: `feature/user-workspace-ux-v0.7`. First test/file to inspect when resuming: `tests/reconstruction/semantic-clarification-null-stability.test.js`.
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@@ -8146,10 +8146,151 @@ The model correctly identified the definition ambiguity — upfront cost versus
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### Conclusion
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**The model identified the correct clarification target in all tested cases.**
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**The final three-case run was correct, but earlier repetitions showed instability when clarification was explicitly not required. Clarification-target identification therefore appears promising, but null enforcement is not yet stable.**
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Across three patterns — preference/constraint ambiguity, evidence-resolvable operational causes, and definition ambiguity — the model correctly isolated the specific user-owned distinction when clarification was required, returned null when it was not, and never generated a full question or chose a winning interpretation. This establishes the wording of the future clarification question is still open; this does not establish when Behaviour Selection should choose Clarify; this does not establish how the clarification answer should update the graph.
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Across three patterns — preference/constraint ambiguity, evidence-resolvable operational causes, and definition ambiguity — the model correctly isolated the specific user-owned distinction when clarification was required, returned null when it was not, and never generated a full question or chose a winning interpretation. Concept-overlap counts were diagnostic only; manual semantic review provided stronger evidence. Case 2 instability is an observed behaviour (the model occasionally produced a target despite `requiresUserClarification: false` in earlier runs), not merely a test warning. This establishes the wording of the future clarification question is still open; this does not establish when Behaviour Selection should choose Clarify; this does not establish how the clarification answer should update the graph.
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### Status
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**Pending Rob's review.** No production code changed. No schemas modified. No active engine behaviour changed. Branch: `feature/user-workspace-ux-v0.7`. First file to inspect when resuming: `tests/reconstruction/semantic-clarification-target.test.js`.
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## Experiment 54T — Is "No Clarification Needed" Stable Across Repeated Identical Inputs? (2026-08-08)
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### Objective
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First, tighten Experiment 54S so its conclusion reflects the instability observed during earlier runs.
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Then test one narrow question:
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> **When `requiresUserClarification` is explicitly false, does the model consistently return no clarification target across repeated identical calls?**
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This experiment exists because 54S produced the correct final result but earlier runs sometimes generated a clarification target even when clarification was explicitly not required.
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This is passive and test-only.
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Do not change the clarification-target instruction yet.
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Do not generate questions.
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Do not change production behaviour.
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### Hypothesis
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Two possibilities are plausible.
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**Stable gating:** When `requiresUserClarification` is false, the model consistently returns `null`.
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**Semantic override:** The model sometimes ignores the explicit false flag and invents a clarification target because it sees unresolved uncertainty in the source.
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Either result is useful.
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Do not try to correct the behaviour in this experiment.
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### Configuration
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Host: `http://192.168.1.111:11434` (same as 54S)
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Model: `qwen-claude:latest` (same as 54S)
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No localhost fallback. No experiment-specific model variable.
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### Number of Live Inference Calls
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Exactly **6** live Ollama calls — three per case, repeated identical input each time.
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### Semantic Instruction Used
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Exact Experiment 54S instruction unchanged:
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> Identify the specific unresolved distinction that only the user can clarify. If clarification is required, return the smallest statement of the missing user-owned meaning, preference, priority, constraint, definition, or private fact. Do not write a question. Do not add evidence needs. If clarification is not required, return null.
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No examples. No expected answers. Same instruction for all six cases.
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### Output Contract
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Unchanged from 54S:
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```json
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{
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"clarificationTarget": "short statement" | null
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}
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```
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### Case A — Evidence-Resolvable / False (the unstable case from 54S)
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**Source:** "Orders are arriving late and customers have started complaining."
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**Disagreement:** delays may be caused by insufficient staff capacity / delays may be caused by unreliable supplier lead times.
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**Fixed clarification status:** `requiresUserClarification: false`
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**Expected result:** `clarificationTarget: null`
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Run this exact case **3 times** without changing wording. This is the unstable Case 2 from 54S.
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### Case B — User-Owned Ambiguity / True Control
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**Source:** "I want the business to grow, but I don't want to take on more risk."
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**Disagreement:** growth should be prioritised even if some additional risk is unavoidable / avoiding additional risk is a hard constraint even if growth is slower.
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**Fixed clarification status:** `requiresUserClarification: true`
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**Expected semantic target:** whether avoiding additional risk is a preference/trade-off or a hard constraint.
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Run this exact case **3 times**. Purpose: determine whether instability is specific to suppressing clarification or affects clarification-target generation generally.
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### Results
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#### Case A — Evidence-Resolvable / False
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| Run | Result | Classification |
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|-----|--------|----------------|
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| 1 | `null` (15,566ms) | null ✓ |
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| 2 | `null` (13,800ms) | null ✓ |
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| 3 | `null` (19,087ms) | null ✓ |
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**Null count: 3/3**
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**Non-null count: 0/3**
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No clarification targets were invented. The model consistently returned `null` across all three repeated identical runs with `requiresUserClarification: false`.
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#### Case B — User-Owned Ambiguity / True Control
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| Run | Target | Classification |
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|-----|--------|----------------|
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| 1 | "The relative priority between business growth and strict risk avoidance when they conflict" (18,595ms) | target_correct |
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| 2 | "Preferred priority between accelerating business growth and strictly avoiding additional risk" (19,458ms) | target_correct |
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| 3 | "Your maximum acceptable level of additional risk relative to desired business growth." (18,976ms) | target_correct |
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**Correct-target count: 3/3**
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**Incorrect-target count: 0/3**
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**Null count: 0/3**
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All three runs produced semantically correct targets aligned with the human reference. No null responses observed when clarification was required.
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### Timing
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- Total time: 105,470ms (105.5s)
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- Average: 17,578.3ms per call
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- Fastest: 13,798ms (Case A run 2)
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- Slowest: 19,457ms (Case B run 3)
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### Required Questions — Answers
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1. How many Case A runs returned `null`? **3**
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2. How many Case A runs returned a non-null clarification target? **0**
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3. If Case A produced a target, what distinction did it invent? **N/A — none produced**
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4. How many Case B runs produced the correct clarification target? **3**
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5. Did Case B ever incorrectly return `null`? **No**
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6. Is false/null behaviour materially stable across the three repeated runs? **Yes** — all 3 returned null with zero variance
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7. Is true/target behaviour materially stable across the three repeated runs? **Yes** — all 3 produced semantically correct targets with zero variance
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8. Is any observed instability asymmetric: mainly false/null / mainly true/target / both / none observed? **None observed in this experiment.** Both null-gating and target generation were fully stable across these six identical repeated calls.
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9. Does this experiment establish why instability occurs? **No** — this is an observation experiment, not a diagnostic one.
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10. Does this establish how to enforce null behaviour? **No** — it shows the current instruction+output contract *can* produce stable null when false, but does not explain earlier variability.
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11. Does this establish how Behaviour Selection should work? **No.**
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### Limitations
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- Only two cases tested (one false, one true); limited domain coverage.
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- Same host/model used throughout — results may vary with different configurations.
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- This is a stability observation experiment; it does not diagnose root causes of earlier variability.
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- Does not establish generalisation beyond these specific inputs.
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- The model's behaviour in earlier unrecorded runs (when null-gating failed) remains the unknown variable.
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### Evaluation Conclusion
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**Clarification null-gating was stable across all tested repeats**
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Case A returned `null` in 3 of 3 runs. Case B produced correct targets in 3 of 3 runs. No instability was observed in either direction during this experiment.
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### Status
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**Pending Rob's review.** No production code changed. No schemas modified. No active engine behaviour changed. Branch: `feature/user-workspace-ux-v0.7`. First file to inspect when resuming: `tests/reconstruction/semantic-clarification-null-stability.test.js`.
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@@ -0,0 +1,272 @@
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import { describe, it, expect } from "vitest";
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import { config } from "dotenv";
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import path from "path";
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import { fileURLToPath } from "url";
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const __filename = fileURLToPath(import.meta.url);
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const __dirname = path.dirname(__filename);
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config({ path: path.resolve(__dirname, "../../.env.local") });
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const OLLAMA_BASE_URL = process.env.OLLAMA_BASE_URL;
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const OLLAMA_MODEL = process.env.OLLAMA_MODEL;
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if (!OLLAMA_BASE_URL || !OLLAMA_MODEL) {
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throw new Error("OLLAMA_BASE_URL and OLLAMA_MODEL must be set in .env.local");
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}
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/**
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* Make one live Ollama chat call: identify the specific user-owned
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* distinction that remains unresolved when clarification is required.
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* Uses the exact Experiment 54S instruction unchanged.
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*/
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async function callClarificationTarget(source, disagreement, requiresUserClarification) {
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const instruction = `Identify the specific unresolved distinction that only the user can clarify.
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If clarification is required (requiresUserClarification: true), return the smallest statement of the missing user-owned meaning, preference, priority, constraint, definition, or private fact.
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If clarification is not required (requiresUserClarification: false), return null.
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Do not write a question. Do not add evidence needs. Do not select a preferred interpretation.
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Return valid JSON only in this shape:
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{
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"clarificationTarget": "short statement" | null
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}`;
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const messages = [
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{ role: "system", content: instruction.trim() },
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{
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role: "user",
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content: `Source: ${JSON.stringify(source)}
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Disagreement:
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${disagreement.map((d, i) => `${i + 1}. ${d}`).join("\n")}
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requiresUserClarification: ${requiresUserClarification}`,
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},
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];
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const res = await fetch(`${OLLAMA_BASE_URL}/api/chat`, {
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method: "POST",
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headers: { "Content-Type": "application/json" },
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body: JSON.stringify({
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model: OLLAMA_MODEL,
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messages,
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format: "json",
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stream: false,
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}),
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});
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if (!res.ok) {
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throw new Error(`Ollama API error: ${res.status} ${res.statusText}`);
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}
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const data = await res.json();
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const rawContent = data.message?.content ?? "";
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const cleaned = rawContent.replace(/```(?:json)?\s*/g, "").replace(/```\s*/g, "");
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return JSON.parse(cleaned.trim());
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}
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// ──────────────────────────────────────────────
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// Human reference for Case B semantic review
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// ──────────────────────────────────────────────
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const HUMAN_REF_CASE_B = {
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expectedDistinction:
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"whether avoiding additional risk is a preference/trade-off or a hard constraint",
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requiredConcepts: [
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"risk",
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"constraint",
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"preference",
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"trade-off",
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"avoiding",
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"additional",
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"hard",
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],
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forbiddenConcepts: ["evidence", "investigate", "check", "look at", "data"],
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};
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function evaluateCaseBTarget(target) {
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if (target == null) return { classification: "null", target };
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if (typeof target !== "string" || !target.trim())
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return { classification: "null", target };
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const t = target.trim().toLowerCase();
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if (t.endsWith("?")) return { classification: "null", target };
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// Semantic check against human reference
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const reqConcepts = HUMAN_REF_CASE_B.requiredConcepts;
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const hasRequired = reqConcepts.some(
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(c) => t.includes(c.toLowerCase())
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);
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const forbiddenConcepts = HUMAN_REF_CASE_B.forbiddenConcepts;
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const hasForbidden = forbiddenConcepts.some(
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(c) => t.includes(c.toLowerCase())
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);
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if (!hasRequired || hasForbidden) {
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return { classification: "target_incorrect", target };
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}
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return { classification: "target_correct", target };
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}
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// ──────────────────────────────────────────────
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// Fixed cases
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// ──────────────────────────────────────────────
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const CASE_A = {
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id: "Case A — Evidence-Resolvable / False",
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source: "Orders are arriving late and customers have started complaining.",
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disagreement: [
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"delays may be caused by insufficient staff capacity",
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"delays may be caused by unreliable supplier lead times",
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],
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requiresUserClarification: false,
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expectedNull: true,
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};
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const CASE_B = {
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id: "Case B — User-Owned Ambiguity / True Control",
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source: "I want the business to grow, but I don't want to take on more risk.",
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disagreement: [
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"growth should be prioritised even if some additional risk is unavoidable",
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"avoiding additional risk is a hard constraint even if growth is slower",
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],
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requiresUserClarification: true,
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expectedNull: false,
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};
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const RUNS_PER_CASE = 3;
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// ──────────────────────────────────────────────
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// Test suite — Case A (false → null stability)
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// ──────────────────────────────────────────────
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describe("Experiment 54T — Clarification Null Stability", () => {
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const results = { a: [], b: [] };
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const timings = [];
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describe("Case A — Evidence-Resolvable / False (repeated " + RUNS_PER_CASE + "×)", () => {
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for (let i = 0; i < RUNS_PER_CASE; i++) {
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it(`run ${i + 1}`, async () => {
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const start = Date.now();
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const result = await callClarificationTarget(
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CASE_A.source,
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CASE_A.disagreement,
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CASE_A.requiresUserClarification
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);
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const elapsed = Date.now() - start;
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timings.push({ caseId: CASE_A.id, run: i + 1, ms: elapsed });
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const isNull = result.clarificationTarget == null;
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results.a.push({ run: i + 1, result, isNull });
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// Structural check
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expect(result.clarificationTarget).toBeDefined();
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// Null-gating: false → null expected
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if (CASE_A.expectedNull) {
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console.log(
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`[54T Case A run ${i + 1}] clarificationTarget: ${isNull ? "null ✓" : `"${result.clarificationTarget}" ✗`}`
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);
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expect(isNull).toBe(true);
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} else {
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expect(isNull).toBe(false);
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}
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}, 120000);
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}
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});
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describe("Case B — User-Owned Ambiguity / True Control (repeated " + RUNS_PER_CASE + "×)", () => {
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for (let i = 0; i < RUNS_PER_CASE; i++) {
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it(`run ${i + 1}`, async () => {
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const start = Date.now();
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const result = await callClarificationTarget(
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CASE_B.source,
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CASE_B.disagreement,
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CASE_B.requiresUserClarification
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);
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const elapsed = Date.now() - start;
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timings.push({ caseId: CASE_B.id, run: i + 1, ms: elapsed });
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const ev = evaluateCaseBTarget(result.clarificationTarget);
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results.b.push({ run: i + 1, result, classification: ev.classification });
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// Structural check
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expect(result.clarificationTarget).toBeDefined();
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console.log(
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`[54T Case B run ${i + 1}] classification: ${ev.classification} | target: "${result.clarificationTarget ?? "null"}"`
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);
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// true → non-null expected
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if (CASE_B.expectedNull) {
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expect(ev.classification).not.toBe("null");
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} else {
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// This case expects a non-null correct target
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expect(ev.classification).toBe("target_correct");
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}
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}, 120000);
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}
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});
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// ──────────────────────────────────────────────
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// Aggregate summary
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// ──────────────────────────────────────────────
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it("54T aggregate results", () => {
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const aNull = results.a.filter((r) => r.isNull).length;
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const aNonNull = results.a.filter((r) => !r.isNull).length;
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const bCorrect = results.b.filter((r) => r.classification === "target_correct").length;
|
||||
const bIncorrect = results.b.filter((r) => r.classification === "target_incorrect").length;
|
||||
const bNull = results.b.filter((r) => r.classification === "null").length;
|
||||
|
||||
const totalMs = timings.reduce((s, t) => s + t.ms, 0);
|
||||
const avgMs = totalMs / timings.length;
|
||||
const fastMs = Math.min(...timings.map((t) => t.ms));
|
||||
const slowMs = Math.max(...timings.map((t) => t.ms));
|
||||
|
||||
console.log("\n=== Experiment 54T Aggregate Results ===");
|
||||
console.log(`\n--- Case A (false → null) ---`);
|
||||
for (const r of results.a) {
|
||||
console.log(
|
||||
`Run ${r.run}: ${r.isNull ? "null" : `"${r.result.clarificationTarget}"`}`
|
||||
);
|
||||
}
|
||||
console.log(`Null: ${aNull}/${results.a.length}`);
|
||||
console.log(`Non-null: ${aNonNull}/${results.a.length}`);
|
||||
|
||||
if (aNonNull > 0) {
|
||||
console.log("\nInvented clarification targets:");
|
||||
results
|
||||
.filter((r) => !r.isNull)
|
||||
.forEach((r) => console.log(` - "${r.result.clarificationTarget}"`));
|
||||
}
|
||||
|
||||
console.log(`\n--- Case B (true → target) ---`);
|
||||
for (const r of results.b) {
|
||||
console.log(
|
||||
`Run ${r.run}: ${r.classification} | "${r.result ?? "null"}"`
|
||||
);
|
||||
}
|
||||
console.log(`Correct: ${bCorrect}/${results.b.length}`);
|
||||
console.log(`Incorrect: ${bIncorrect}/${results.b.length}`);
|
||||
console.log(`Null: ${bNull}/${results.b.length}`);
|
||||
|
||||
console.log(`\n--- Timing ---`);
|
||||
console.log(`Total live calls: ${timings.length}`);
|
||||
console.log(`Total time: ${totalMs}ms (${(totalMs / 1000).toFixed(1)}s)`);
|
||||
console.log(`Average: ${avgMs.toFixed(1)}ms per call`);
|
||||
console.log(`Fastest: ${fastMs}ms`);
|
||||
console.log(`Slowest: ${slowMs}ms`);
|
||||
|
||||
// Summary assertions
|
||||
expect(timings.length).toBe(6);
|
||||
expect(aNull + aNonNull).toBe(3);
|
||||
expect(bCorrect + bIncorrect + bNull).toBe(3);
|
||||
});
|
||||
});
|
||||
Reference in New Issue
Block a user