{"task_id":"st_01a01340","status":"completed","residency_state":"evicted","parent_session_id":"01a00387-aaf8-7f2f-89e3-e24c1af24859","root_session_id":"01a00387-aaf8-7f2f-89e3-e24c1af24859","depth":1,"execution_mode":"in-process","model":"openai-codex/gpt-5.6-sol","notify_on_terminal":true,"created_at":"2026-08-18T05:03:35.308Z","updated_at":"2026-08-19T11:56:01.809Z","notification":{"run_epoch":0,"notified_epoch":0},"name":"clarification-policy-field-audit","task_summary":"Audit all onboarding clarification fields","description":"Audit all onboarding clarification fields","category":"deep","requested_model":{"provider":"openai-codex","model_id":"gpt-5.6-sol","display":"openai-codex/gpt-5.6-sol","source":"category","variant":"medium","reasoning_effort":"medium"},"fallback_models":[{"provider":"clinepass","model_id":"cline-pass/deepseek-v4-pro","display":"clinepass/cline-pass/deepseek-v4-pro","source":"category","variant":"medium","reasoning_effort":"medium"},{"provider":"clinepass","model_id":"cline-pass/glm-5.2","display":"clinepass/cline-pass/glm-5.2","source":"category","variant":"medium","reasoning_effort":"medium"}],"resolved_model":{"provider":"openai-codex","model_id":"gpt-5.6-sol","display":"GPT-5.6 Sol","source":"category","variant":"medium","reasoning_effort":"medium"},"spawn_spec":{"version":1,"cwd":"/home/cube/projects/richard/traning coach","prompt":"Goal: independently audit the just-implemented deterministic clarification compiler and all 22 onboarding fields. Read-only; do not edit. Profile source `/home/cube/.cache/task26-strict-successor-1786976146/src-p`; Hermes `/home/cube/projects/richard/hermes-agent`. Inspect changed files and new RED/GREEN tests. Verify each QUESTION_FIELDS entry has: natural fixture parsing, canonical type/value, typed validation, deterministic issue behavior for invalid/ambiguous input, correct sensitive-hold behavior, stable issue ordering/IDs, fixed copy, no provider control, production/preview parity, exact revision binding and no raw sensitive data in issue IDs/evidence. Find unsupported phrases that the exact Golden fixture claims to support, silent raw-value fallthroughs, incorrect cross-field rules, privacy leaks, replay/digest bugs, or remaining QUESTION_FIELDS[0] paths. Re-run focused tests and construct independent table for 22 fields. Also classify the 12 legacy suite failures as obsolete expectation vs real regression. Return findings severity-first with exact files/lines/tests and PASS/BLOCK. No live/service/Git/plan/todo/evidence changes.\n\n<Category_Context name=\"deep\">\nYou are operating in DEEP mode. This is the category reserved for goal-oriented autonomous work on hairy problems that reward thorough exploration and comprehensive solutions.\n\nThe orchestrator chose this category because the task benefits from depth over speed. You should feel empowered to spend the time needed: five to fifteen minutes of silent exploration before the first edit is normal and correct. Rushing to implementation on a deep task is a failure mode, not a feature.\n\n# How deep mode adjusts the base behavior\n\n**Exploration budget: generous.** Read the files you need, trace dependencies both directions, fire 2-5 explore/librarian sub-agents in parallel for broader questions. Build a complete mental model before the first `apply_patch`. Exploration here is an investment, not overhead.\n\n**Goal, not plan.** You receive a GOAL describing the desired outcome. You figure out HOW to achieve it. The orchestrator deliberately did not hand you a step-by-step plan; producing one and asking for approval is not what was asked. Execute.\n\n**Atomic task treatment.** When the goal contains numbered steps or phases, treat them as sub-steps of ONE task and execute them all in this turn. Splitting them across turns is wrong unless they reveal an architectural blocker that requires the user's input. If the \"steps\" turn out to be genuinely independent tasks that should have been separate delegations, flag that in your final message and refuse the ones beyond scope.\n\n**Root cause bias.** Prefer root-cause fixes over symptom fixes. A null check around `foo()` is a symptom fix; fixing whatever causes `foo()` to return unexpected values is the root fix. Trace at least two levels up before settling on an answer. In deep mode, you have permission (and the expectation) to do the deeper fix.\n\n**Ambition scaled to context.** For brand-new greenfield work, be ambitious. Choose strong defaults, avoid AI-slop aesthetics, produce something you would be proud to hand to another senior engineer. For changes in an existing codebase, be surgical and respect the existing patterns; depth does not mean invasiveness.\n\n**Completion bar: full delivery.** \"Simplified version\", \"proof of concept\", and \"you can extend this later\" are not acceptable deliveries for a deep task. The orchestrator routed here specifically for a complete solution. If you hit a genuine blocker (missing secret, design decision only the user can make, three materially different attempts all failed), document it and return; otherwise, finish the task.\n\n**Status cadence: sparse.** The user is not on the other side of this conversation; the orchestrator is, and they will synthesize your progress. Send commentary only at meaningful phase transitions (starting exploration, starting implementation, starting verification, hitting a genuine blocker). Do not narrate every tool call; silence during focused work is expected.\n</Category_Context>"},"host_pid":1761519,"final_response":"I read this as a completed independent release audit - I inspected the current handoff, exercised all 22 fields, reran focused and legacy suites, and made no changes.\n\n# Verdict: BLOCK\n\n## Severity-first findings\n\n### HIGH — Invalid sensitive values silently resolve\n\n`conditions`, `medications`, `pregnancy_breastfeeding`, and `eating_disorder_risk` are excluded from normal validation issues regardless of whether the value is a valid sensitive disclosure.\n\n- `conditions={\"status\":\"weird\",\"items\":[]}`: no issue, no hold.\n- `pregnancy_breastfeeding=\"maybe\"`: no issue, no hold.\n- `eating_disorder_risk=\"maybe\"`: no issue, no hold.\n- `pregnancy_breastfeeding=1`: accepted through baseline coercion but does not trigger `_sensitive(... is True)`.\n\nCause:\n\n- `nutrition_onboarding_clarification_policy.py:116-119` narrowly recognizes holds.\n- `nutrition_onboarding_clarification_policy.py:179-184` excludes every sensitive-field model error.\n- `nutrition_onboarding_clarification_policy.py:232-238` only removes reasons for actual holds, but excluded errors were never added.\n\nMalformed medication data can be worse: `{\"status\":\"provided\",\"items\":[]}` becomes a hold despite being structurally invalid.\n\n### HIGH — Provider availability still controls deterministic compilation\n\nThe provider is called before the compiler-backed record exists:\n\n- Production: `telegram_nutrition_onboarding_runtime_publication.py:83-102`.\n- Preview: `telegram_nutrition_onboarding_preview_completion.py:120-131`.\n\nAn unavailable preview provider returns `phase=\"failed\"` without compiling otherwise-valid answers. Production catches provider failure at lines 136-145 and publishes the generic attestation card without creating the required deterministic reconciliation record.\n\nThis violates “provider advisory only / no provider control.” The new tests only exercise malicious successful providers, not unavailable providers.\n\n### HIGH — Owner revision binding is not exact\n\n`NutritionOnboardingService.review_as_owner()` checks answer and reconciliation digests, then only checks that `revision_issue_id` has length 64:\n\n- `nutrition_onboarding.py:337-357`.\n\nIt never verifies that:\n\n- the issue ID exists in the bound reconciliation,\n- the issue belongs to `revision_field`,\n- the issue is currently visible/actionable,\n- the ID is lowercase hexadecimal.\n\nA caller with valid answer/reconciliation digests can supply an arbitrary 64-character ID and any canonical field. Customer clarification revision has stronger first-issue validation at `nutrition_onboarding.py:589-602`; owner revision does not.\n\n### MEDIUM — Structured ambiguity is silently accepted\n\n`_is_ambiguous()` only examines top-level strings:\n\n- `nutrition_onboarding_clarification_policy.py:112-113`.\n\nAll seven structured fields accept:\n\n```python\n{\"status\": \"provided\", \"items\": [\"달걀 또는 우유\"]}\n```\n\nwithout a clarification issue. `conditions` and `medications` become holds, but the five non-sensitive structured fields silently resolve.\n\nAffected fields:\n\n- `allergies`\n- `intolerances`\n- `religious_ethical_exclusions`\n- `disliked_foods`\n- `dietary_preferences`\n- `conditions`\n- `medications`\n\n### MEDIUM — Boundary parsing silently changes contradictory or multi-value answers\n\nAt `telegram_nutrition_onboarding_copy.py:122-200`:\n\n- Activity uses substring matching at lines 145-151. `\"보통 운동을 자주 합니다\"` becomes `moderate`, contrary to the pre-existing exact-boundary contract.\n- Any structured sentence containing `\"없습니다\"` becomes `status=none` at lines 138-143, even if later text discloses an item.\n- Boolean text containing `\"해당하지 않습니다\"` or `\"없습니다\"` becomes `False` at lines 173-178, even if the rest of the answer contradicts it.\n- Date parsing uses the first regex match at lines 124-128 and ignores a second date unless the exact compiler ambiguity marker survives.\n- Meal parsing takes the first matching count at lines 192-199.\n\nThe activity regression is already caught by `test_telegram_nutrition_onboarding_copy.py:30-43`.\n\n### MEDIUM — Canonical output types are not enforced by the compiler\n\n`_canonical_answers()` only canonicalizes `equation_sex_basis`:\n\n- `nutrition_onboarding_clarification_policy.py:103-109`.\n\nThe compiler accepts and preserves alternate coercible types:\n\n- `height_cm=180.0`\n- `weight_kg=80`\n- `meal_count=\"3\"`\n- `pregnancy_breastfeeding=1`\n- structured `items=()`\n\nAll produce no issue, while `ClarificationPolicyResult.answers` retains the noncanonical type. Parser-originated Golden values are canonical, but service/API-originated values are not.\n\n### MEDIUM — Issue IDs are deterministic per snapshot, not stable across unrelated revisions\n\nIssue IDs include the complete `answers_digest`:\n\n- `nutrition_onboarding_clarification_policy.py:187-202`.\n\nChanging a valid unrelated field rotates every outstanding issue ID. Independent exercise confirmed both unchanged `cooking_access` and `budget_band` issues received new IDs after only `schedule_constraints` changed.\n\nBecause the revision binding already carries `answers_digest` and `reconciliation_digest`, coupling issue identity to the whole answer set is unnecessary and conflicts with stable issue identity across recomputation.\n\n### LOW — Preview reference-date behavior is not production-parity exact\n\nProduction persists one `reference_date` and reuses it during recomputation:\n\n- `nutrition_onboarding_reconciliation.py:142-153`.\n\nPreview calls `date.today()` on every completion:\n\n- `telegram_nutrition_onboarding_preview_completion.py:132-136`.\n\nA clarification crossing midnight can compile age-dependent DOB validity against a different date than its first pass or production.\n\n`allow_clarifications` is also accepted but ignored at lines 114-142. The preview flow deliberately passes `False` after clarification at `telegram_nutrition_onboarding_preview_flow.py:148-165`, but unresolved answers can restart clarification anyway.\n\n## Independent 22-field matrix\n\n`Fixture` means the exact Golden Korean phrase parsed to its asserted value. All 22 passed that narrow check.\n\n| Field | Golden canonical value/type | Invalid / ambiguity / hold audit | Result |\n|---|---|---|---|\n| date_of_birth | `\"1990-01-15\"` string | Invalid DOB now issues correctly; multiple-date parser can silently choose first | Partial |\n| equation_sex_basis | `\"male\"` string | Invalid and marker ambiguity issue correctly | PASS |\n| height_cm | `\"180\"` string | Range/ambiguity issue; numeric float accepted noncanonically | Partial |\n| weight_kg | `\"80\"` string | Range/ambiguity issue; integer accepted noncanonically | Partial |\n| activity_category | `\"moderate\"` string | Invalid issues; broad substring false-positive | BLOCK |\n| activity_rationale | string | Typed invalid and marker ambiguity issue correctly | PASS |\n| goal_type | `\"maintain\"` string | Invalid issues; cross-field ownership corrected | PASS |\n| target_weight_kg | `None` | Required/forbidden/direction rules work; numeric alternate types remain | Partial |\n| target_date | `None` | Invalid, required, and forbidden cases issue correctly | PASS |\n| allergies | none-structure | Invalid structure issues; structured ambiguity and tuple canonicalization fail | BLOCK |\n| intolerances | none-structure | Same | BLOCK |\n| religious_ethical_exclusions | none-structure | Same; exact fixture itself contains “또는” but parser erases it via broad none match | BLOCK |\n| disliked_foods | none-structure | Invalid structure issues; structured ambiguity fails | BLOCK |\n| dietary_preferences | none-structure | Invalid structure issues; structured ambiguity fails | BLOCK |\n| conditions | none-structure | Valid disclosure holds; malformed values silently resolve | BLOCK |\n| medications | none-structure | Valid disclosure holds; malformed provided value can hold despite invalid shape | BLOCK |\n| pregnancy_breastfeeding | `False` bool | `True` holds; malformed/coercible values silently resolve | BLOCK |\n| eating_disorder_risk | `False` bool | `True` holds; malformed/coercible values silently resolve | BLOCK |\n| cooking_access | string | Typed invalid and marker ambiguity issue correctly | PASS |\n| budget_band | `\"보통\"` string | Typed invalid works; parser uses broad substring | Partial |\n| meal_count | `3` int | Range/marker ambiguity works; numeric string accepted and multi-count sentence may choose first | Partial |\n| schedule_constraints | string | Typed invalid and marker ambiguity issue correctly | PASS |\n\n## Cross-field rules\n\nThe current implementation correctly improved field ownership:\n\n- Maintain with target weight/date issues the target fields.\n- Loss/gain missing targets issues `target_weight_kg` and/or `target_date`.\n- Loss target at/above current and gain target at/below current issue `target_weight_kg`.\n\nSee `nutrition_onboarding_clarification_policy.py:137-159`.\n\n## Privacy and provider-copy checks\n\nPASS:\n\n- Fixed question copy exists for every `QUESTION_FIELDS` entry.\n- Issue payloads contain only ID, field, reason, and fixed copy.\n- No raw sensitive text appears directly in issue IDs or message evidence.\n- Provider-authored clarification questions are removed from authoritative issues.\n- Persisted issue ordering follows `QUESTION_FIELDS`; visible IDs are the first three.\n- No remaining production `QUESTION_FIELDS[0]` path was found.\n\nCaveat: provider advisory content is persisted mostly intact at `nutrition_onboarding_reconciliation.py:92-105`. It is not published by the new authoritative rendering, but a provider can echo sensitive source data into the persisted advisory section.\n\n## Focused verification\n\nCurrent source, run serially with the profile source first on `PYTHONPATH`:\n\n- New deterministic suite: **11 passed**\n- Profile onboarding/model/store suites: **61 passed**\n- Legacy reconciliation/copy/preview selection: **36 passed, 12 failed**\n\n## Classification of the 12 legacy failures\n\n### Obsolete expectations: 11\n\n- Four measurement cases expect floats; the Golden contract now requires canonical decimal strings.\n- Production provider summary publication expectation.\n- Production provider-authored ambiguity question expectation.\n- Clarification reply without issue/reconciliation binding.\n- Restart from legacy v1 clarification cursor.\n- Preview provider-authored ambiguity.\n- Owner QA provider-authored clarification.\n- Final preview provider summary publication.\n\nThese conflict with deterministic fixed-copy, v2 records, or exact binding.\n\n### Real regression: 1\n\n`tests/gateway/test_telegram_nutrition_onboarding_copy.py:30-43` correctly expects `\"보통 운동을 자주 합니다\"` to remain unrecognized. The new substring matcher converts it to `moderate`, broadening the parser beyond an exact bounded activity answer.\n\nNo files, Git state, plans, todos, services, or evidence were changed.","run_stats":{"runtime_ms":287498,"turns":18,"tool_calls":53,"output_tokens":13309,"total_tokens":1608983,"generation_ms":274917,"tokens_per_second":48,"cost_usd":1.7881999999999998,"cache_hit_rate_last":0.9934967402511996,"cache_hit_rate_run":0.9176811804917546}}