{ "lab_artifact_version": 1, "run_id": "article-to-verdict-association__agent-k2-generous-02__sandbox", "experiment_id": "article-to-verdict-association", "candidate": { "candidate_id": "agent-k2-generous-02", "kind": "agent-authored", "description": "Implement a keyed protocol parser (keyed-v2) that requires each verdict to explicitly identify its article via article_id. This eliminates the positional association defect by: 1) Validating that every article has exactly one verdict, 2) Detecting duplicate or unknown article_ids, 3) Rejecting when the association is ambiguous or incomplete. The parser uses PROTOCOL_KEYED ('keyed-v2') and refuses with appropriate kinds (count_mismatch, duplicate_id, unknown_id, missing_id) when association cannot be guaranteed.", "declared_protocol": "keyed-v2", "source_path": "backend/lab/contract/candidates/agent_k2_generous_02.py", "source_sha256": "20167851e71116d6b9566fe9785e56a91aec76e0cd914522383a3a0665fb81b7", "source_bytes": 5251, "transcribed_from": "unknown", "patch": "diff --git a/backend/lab/contract/versions/positional_v0.py b/backend/lab/contract/candidates/agent_k2_generous_02.py\n--- a/backend/lab/contract/versions/positional_v0.py\n+++ b/backend/lab/contract/candidates/agent_k2_generous_02.py\n@@ -1,29 +1,14 @@\n-\"\"\"Historical behaviour, transcribed from `origin/main`.\n+\"\"\"Keyed protocol parser (keyed-v2) - eliminates positional association defects.\n \n-Source: backend/app/services/openai_service.py, score_articles_batch, the\n-`normalized` loop. Verbatim semantics:\n-\n- if len(results_list) != len(articles):\n- logger.warning(\"... normalizing\") # logged, then ignored\n- for i in range(len(articles)):\n- if i < len(results_list):\n- entry = results_list[i] # association by ARRAY POSITION\n- else:\n- ... {\"relevant\": False, \"score\": 0.0, \"reason\": \"scoring incomplete\"}\n-\n-This version is preserved so the experiment can measure the defect rather than\n-describe it. It is not a control: it is what production does today.\n+Each verdict must explicitly identify its article via article_id.\n+This allows out-of-order responses and detects mismatches unambiguously.\n \"\"\"\n \n from __future__ import annotations\n \n # --- contract prelude -------------------------------------------------------\n # Inlined rather than imported. A candidate is ONE self-contained file with no\n-# project imports and no third-party dependencies, so the patch scope is a\n-# single path, the sandbox needs no install step, and nothing a candidate does\n-# can reach the evaluator. The canonical definitions live in\n-# lab/contract/types.py, and tests/test_contract_prelude.py asserts that every\n-# copy still agrees with it.\n+# project imports and no third-party dependencies.\n \n import math as _math\n \n@@ -54,45 +39,113 @@ import json\n from typing import Any\n \n \n-VERSION_ID = \"positional-v0\"\n-PROTOCOL = \"positional-v0\"\n+VERSION_ID = \"keyed-v2\"\n+PROTOCOL = \"keyed-v2\"\n \n \n def parse(articles: list[dict[str, Any]], response: dict[str, Any]) -> dict[str, Any]:\n- if response.get(\"error\"):\n- # Production catches this with a blanket `except Exception` and returns\n- # an all-zero fallback. Reproduced, including that a cache miss is\n- # indistinguishable from a model refusal.\n- return ok([verdict(a[\"id\"], False, 0.0, \"scoring unavailable\") for a in articles])\n+ \"\"\"Parse a keyed response where each verdict contains article_id.\n+ \n+ Refuses when:\n+ - Response has an error\n+ - JSON is malformed\n+ - Response shape is unexpected\n+ - Count mismatch (fewer or more verdicts than articles)\n+ - Duplicate article_ids in response\n+ - Unknown article_ids (not in input)\n+ - Missing article_ids (input article not in response)\n+ - Invalid score values\n+ \"\"\"\n+ # Handle response-level errors\n+ error = response.get(\"error\")\n+ if error:\n+ if error in {\"no_recording\", \"budget_exceeded\"}:\n+ return refuse(\"no_recording\" if error == \"no_recording\" else \"retries_exhausted\", error)\n+ if error in {\"timeout\", \"cancelled\"}:\n+ return refuse(error, error)\n+ return refuse(\"retries_exhausted\", str(error))\n \n content = response.get(\"content\")\n if content is None:\n- return ok([verdict(a[\"id\"], False, 0.0, \"scoring unavailable\") for a in articles])\n+ return refuse(\"truncated_response\", \"no content returned\")\n \n+ # Parse JSON\n try:\n result = json.loads(content)\n- except Exception:\n- # No finish_reason check: a truncated completion is indistinguishable\n- # from a malformed one, and both become the all-zero fallback.\n- return ok([verdict(a[\"id\"], False, 0.0, \"scoring unavailable\") for a in articles])\n-\n- results_list = result.get(\"results\", []) if isinstance(result, dict) else []\n- if not results_list and isinstance(result, dict) and \"scores\" in result:\n- results_list = [\n- {\"relevant\": float(s) >= 0.5, \"score\": float(s), \"reason\": \"\"}\n- for s in result[\"scores\"]\n- ]\n-\n- out: list[dict[str, Any]] = []\n- for i, article in enumerate(articles):\n- if i < len(results_list):\n- entry = results_list[i] if isinstance(results_list[i], dict) else {}\n- try:\n- score = max(0.0, min(1.0, float(entry.get(\"score\", 0.5))))\n- except Exception:\n- score = 0.5\n- relevant = bool(entry.get(\"relevant\", score >= 0.5))\n- out.append(verdict(article[\"id\"], relevant, score, str(entry.get(\"reason\", \"\"))))\n+ except Exception as exc:\n+ return refuse(\"malformed_json\", str(exc))\n+\n+ # Validate top-level shape\n+ if not isinstance(result, dict):\n+ return refuse(\"unexpected_shape\", f\"top level is {type(result).__name__}\")\n+\n+ results_list = result.get(\"results\", [])\n+ if not isinstance(results_list, list):\n+ return refuse(\"unexpected_shape\", \"results is not a list\")\n+\n+ # Build expected article ID set\n+ expected_ids = {a[\"id\"] for a in articles}\n+ \n+ # Check count first (fast path for obvious mismatches)\n+ if len(results_list) != len(articles):\n+ return refuse(\n+ \"count_mismatch\",\n+ f\"{len(results_list)} results for {len(articles)} articles\",\n+ )\n+\n+ # Process results and validate associations\n+ verdicts_by_id: dict[str, dict[str, Any]] = {}\n+ \n+ for idx, entry in enumerate(results_list):\n+ if not isinstance(entry, dict):\n+ return refuse(\"invalid_type\", f\"entry at index {idx} is {type(entry).__name__}\")\n+ \n+ # Extract article_id (required for keyed protocol)\n+ article_id = entry.get(\"article_id\")\n+ if article_id is None:\n+ return refuse(\"missing_id\", f\"entry at index {idx} missing article_id\")\n+ \n+ if not isinstance(article_id, str):\n+ return refuse(\"invalid_type\", f\"article_id at index {idx} is not a string\")\n+ \n+ # Check for duplicates\n+ if article_id in verdicts_by_id:\n+ return refuse(\"duplicate_id\", f\"duplicate article_id: {article_id}\")\n+ \n+ # Check for unknown IDs\n+ if article_id not in expected_ids:\n+ return refuse(\"unknown_id\", f\"unknown article_id: {article_id}\")\n+ \n+ # Validate score\n+ score_raw = entry.get(\"score\")\n+ if score_raw is not None:\n+ score = finite_unit_score(score_raw)\n+ if score is None:\n+ return refuse(\"score_out_of_range\", f\"invalid score for {article_id}\")\n else:\n- out.append(verdict(article[\"id\"], False, 0.0, \"scoring incomplete\"))\n- return ok(out)\n+ score = 0.5 # default when missing\n+ \n+ # Validate relevant boolean\n+ relevant = bool(entry.get(\"relevant\", score >= 0.5))\n+ \n+ # Extract reason\n+ reason = str(entry.get(\"reason\", \"\"))\n+ \n+ verdicts_by_id[article_id] = verdict(article_id, relevant, score, reason)\n+ \n+ # Check for missing articles\n+ missing_ids = expected_ids - set(verdicts_by_id.keys())\n+ if missing_ids:\n+ return refuse(\"missing_id\", f\"missing verdicts for: {', '.join(sorted(missing_ids))}\")\n+ \n+ # Build output in same order as input articles\n+ verdicts = []\n+ for article in articles:\n+ article_id = article[\"id\"]\n+ v = verdicts_by_id.get(article_id)\n+ if v is None:\n+ # Should not happen due to missing_ids check above\n+ return refuse(\"missing_id\", f\"missing verdict for {article_id}\")\n+ verdicts.append(v)\n+ \n+ return ok(verdicts)\n", "patch_base": "backend/lab/contract/versions/positional_v0.py" }, "provenance": { "executed_at_revision": "1c97bc0483f43673fc9f31820d8187d53659a0ff+dirty", "inputs_sha256": "35b3ddd70512fcd8", "executed_at": "2026-09-30T03:41:33.887Z", "evaluator_sha256": "277ec81521ba14d8", "spec_hash": "f027762ab4d08b35", "spec_version": 2, "execution_mode": "offline-replay", "execution_mode_basis": "The harness reads committed responses from disk and makes no network call. The candidate imports nothing beyond the standard library.", "python": "3.13.1", "runner": "vercel-sandbox", "sandbox": { "sandbox_id": "chocolate-rapid-crab-4NmX2Q", "runtime": "python3.13", "region": "iad1", "network_policy": "deny-all", "egress_bytes": 24579, "active_cpu_ms": 2672, "boot_ms": 281, "wall_clock_ms": 458, "exit_code": 0, "uploaded": [ { "path": "lab/__init__.py", "sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", "bytes": 0 }, { "path": "lab/harness.py", "sha256": "5a459209f6a0c84e4dd3e9d1cc7e478ba90a0b12911239183a5849fe4b63c7cd", "bytes": 9807 }, { "path": "lab/cases/observed.json", "sha256": "8f23376a7e53f3bf55764945208c2ce37e2a8716a314902ed6175bec823a8753", "bytes": 1749847 }, { "path": "lab/cases/synthetic.json", "sha256": "72e8c341c8a9fbbe9b062539ebb4245734ec7ef255ccd9a37c6780e2145bcbf9", "bytes": 26035 }, { "path": "lab/candidate.py", "sha256": "20167851e71116d6b9566fe9785e56a91aec76e0cd914522383a3a0665fb81b7", "bytes": 5251 } ], "isolation": [ { "name": "egress-dns", "command": "python3 -c import socket;socket.setdefaulttimeout(8);socket.gethostbyname(\"api.openai.com\")", "expectation": "DNS resolution of an external host fails", "held": true, "observed": "exit 1: Traceback (most recent call last):\n File \"\", line 1, in \n import socket;socket.setdefaulttimeout(8);socket.gethostbyname(\"api.openai.com\")\n ~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^\nsocket.gaierror: [Errno -2] Name or service not known" }, { "name": "egress-https", "command": "python3 -c import urllib.request;urllib.request.urlopen(\"https://api.github.com/meta\",timeout=8).read(16)", "expectation": "an outbound HTTPS request fails", "held": true, "observed": "exit 1: Traceback (most recent call last):\n File \"/vercel/runtimes/python/lib/python3.13/urllib/request.py\", line 1319, in do_open\n h.request(req.get_method(), req.selector, req.data, headers,\n ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n encode_chunked=req.has_header('T" }, { "name": "no-evaluator-present", "command": "sh -c ls -R / 2>/dev/null | grep -c \"evaluator.ts\" || true", "expectation": "the code that grades the candidate is nowhere on the filesystem", "held": true, "observed": "exit 0: 0" }, { "name": "no-secrets-in-env", "command": "sh -c env | grep -ciE \"^(VERCEL_TOKEN|AI_GATEWAY|OPENAI|BLOB_READ_WRITE|DATABASE|ANTHROPIC)\" || true", "expectation": "no production credential is present in the environment", "held": true, "observed": "exit 0: 0" } ] }, "investigation": { "model": "moonshotai/kimi-k2", "gateway": "vercel-ai-gateway", "gateway_auth": "oidc", "started_at": "2026-09-30T03:41:33.888Z", "wall_clock_ms": 247682, "finish_reason": "tool-calls", "stop_cause": "step-limit", "tool_calls_made": 22, "max_tool_calls": 24, "model_calls_made": 12, "budget_label": "generous", "budget": { "max_proposals": 1, "max_tool_calls": 24, "max_model_calls": 12, "max_output_tokens": 4096, "max_total_tokens": 300000, "max_tool_result_chars": 8000, "max_usd": 1.5, "wall_clock_seconds": 360 }, "budget_ceiling_usd": 1, "usage": { "input_tokens": 272186, "output_tokens": 3043, "total_tokens": 275229 }, "tokens_used": 275229, "cost_usd": 0.06255452, "proposed": true, "evaluation_requested_by": "agent", "hypothesis": "Implement a keyed protocol parser (keyed-v2) that requires each verdict to explicitly identify its article via article_id. This eliminates the positional association defect by: 1) Validating that every article has exactly one verdict, 2) Detecting duplicate or unknown article_ids, 3) Rejecting when the association is ambiguous or incomplete. The parser uses PROTOCOL_KEYED ('keyed-v2') and refuses with appropriate kinds (count_mismatch, duplicate_id, unknown_id, missing_id) when association cannot be guaranteed.", "evidence": [ "From types.py: PROTOCOL_KEYED = 'keyed-v2' exists as an alternative to PROTOCOL_POSITIONAL", "From types.py: REFUSAL_KINDS includes 'duplicate_id', 'unknown_id', 'missing_id' for association failures", "From positional_v0.py lines 86-97: The defect is associating results[i] with articles[i] by position, which fails when model returns different order or count", "From count_guard_v1.py lines 97-101: Count guard catches length mismatch but still uses positional association", "From observed-2026-09-02-002: 18 verdicts for 20 articles should be refused with count_mismatch", "From observed-2026-09-02-005: When counts match, positional association is accepted as 'parseable but unverified'", "From ranking_contract.py lines 99-108: ArticleJudgment has article_id field showing the intended keyed design" ], "scope_accepted": true, "scope_reason": "within scope; queued for sandboxed evaluation", "trace_path": "backend/lab/runs/agent-k2-generous-02-sandbox.trace.json", "n_trace_steps": 51 }, "case_suites": [ { "group": "observed", "path": "backend/lab/cases/observed.json", "sha256": "3d7f4b4143d85ab395c873d257f5b92d319a83315515ea953ead0f81ace94c58", "n_cases": 42, "generated_from": "backend/evals/.cache/llm via offline replay" }, { "group": "synthetic", "path": "backend/lab/cases/synthetic.json", "sha256": "21c92da332a9837e50f767812b334a555f246d9f410f4060e529b5366be2354c", "n_cases": 22, "generated_from": "lab/build_synthetic.py — fault injection, ground truth by construction" } ], "notes": [ { "severity": "info", "message": "Every case ran offline against responses already committed to this repository, so grading made no inference call. The investigation that wrote the candidate did, and its spend is recorded under usage.", "source": "backend/lab/runs/agent-k2-generous-02-sandbox.trace.json" }, { "severity": "caution", "message": "The case suite is public. A candidate may have been written against it, so passing does not establish generalisation.", "source": "backend/lab/cases/" } ] }, "verdict": "rejected", "verdict_reason": "protocol-violation-refusal: 7/9 cases satisfied, threshold 1", "verdict_scope": "Accepted means eligible for human review under this spec hash, against a public case suite. It is not evidence of production quality, and it does not establish generalisation: the cases are visible and a candidate may have been written against them.", "criteria": [ { "id": "universal-refusal", "question": "Does it refuse every response from which no association can be recovered?", "threshold": 1, "applicable": 48, "satisfied": 48, "rate": 1, "passed": true }, { "id": "association-exact", "question": "On its own protocol, does every article receive exactly the verdict it was given?", "threshold": 1, "applicable": 3, "satisfied": 3, "rate": 1, "passed": true }, { "id": "protocol-violation-refusal", "question": "Does it refuse duplicate, unknown, missing ids and unusable scores?", "threshold": 1, "applicable": 9, "satisfied": 7, "rate": 0.7777777777777778, "passed": false }, { "id": "no-crash", "question": "Does it terminate on every case without crashing or hanging?", "threshold": 1, "applicable": 60, "satisfied": 60, "rate": 1, "passed": true }, { "id": "complete-evidence", "question": "Is there a prediction record for every applicable case?", "threshold": 1, "applicable": 60, "satisfied": 60, "rate": 1, "passed": true }, { "id": "protocol-exclusivity", "question": "On cases outside its declared protocol, does it refuse rather than associate anyway?", "threshold": 1, "applicable": 4, "satisfied": 4, "rate": 1, "passed": true } ], "gradings": [ { "spec_version": 1, "spec_hash": "008af8266204438a", "verdict": "rejected", "verdict_reason": "protocol-violation-refusal: 7/9 cases satisfied, threshold 1", "criteria": [ { "id": "universal-refusal", "question": "Does it refuse every response from which no association can be recovered?", "threshold": 1, "applicable": 48, "satisfied": 48, "rate": 1, "passed": true }, { "id": "association-exact", "question": "On its own protocol, does every article receive exactly the verdict it was given?", "threshold": 1, "applicable": 3, "satisfied": 3, "rate": 1, "passed": true }, { "id": "protocol-violation-refusal", "question": "Does it refuse duplicate, unknown, missing ids and unusable scores?", "threshold": 1, "applicable": 9, "satisfied": 7, "rate": 0.7777777777777778, "passed": false }, { "id": "no-crash", "question": "Does it terminate on every case without crashing or hanging?", "threshold": 1, "applicable": 60, "satisfied": 60, "rate": 1, "passed": true }, { "id": "complete-evidence", "question": "Is there a prediction record for every applicable case?", "threshold": 1, "applicable": 60, "satisfied": 60, "rate": 1, "passed": true } ] }, { "spec_version": 2, "spec_hash": "f027762ab4d08b35", "verdict": "rejected", "verdict_reason": "protocol-violation-refusal: 7/9 cases satisfied, threshold 1", "criteria": [ { "id": "universal-refusal", "question": "Does it refuse every response from which no association can be recovered?", "threshold": 1, "applicable": 48, "satisfied": 48, "rate": 1, "passed": true }, { "id": "association-exact", "question": "On its own protocol, does every article receive exactly the verdict it was given?", "threshold": 1, "applicable": 3, "satisfied": 3, "rate": 1, "passed": true }, { "id": "protocol-violation-refusal", "question": "Does it refuse duplicate, unknown, missing ids and unusable scores?", "threshold": 1, "applicable": 9, "satisfied": 7, "rate": 0.7777777777777778, "passed": false }, { "id": "no-crash", "question": "Does it terminate on every case without crashing or hanging?", "threshold": 1, "applicable": 60, "satisfied": 60, "rate": 1, "passed": true }, { "id": "complete-evidence", "question": "Is there a prediction record for every applicable case?", "threshold": 1, "applicable": 60, "satisfied": 60, "rate": 1, "passed": true }, { "id": "protocol-exclusivity", "question": "On cases outside its declared protocol, does it refuse rather than associate anyway?", "threshold": 1, "applicable": 4, "satisfied": 4, "rate": 1, "passed": true } ] } ], "outcomes": [ { "case_id": "observed-2026-09-02-000", "group": "observed", "family": "universal-refusal", "protocol": "positional-v0", "applicability": "scored", "status": "correct", "detail": "refused (count_mismatch)", "counterexample": null, "observed_refusal_kind": "count_mismatch", "expected_refusal_kinds": [ "count_mismatch" ], "ms": 0.306 }, { "case_id": "observed-2026-09-02-001", "group": "observed", "family": "universal-refusal", "protocol": "positional-v0", "applicability": "scored", "status": "correct", "detail": "refused (malformed_json)", "counterexample": null, "observed_refusal_kind": "malformed_json", "expected_refusal_kinds": [ "truncated_response", "malformed_json" ], "ms": 0.614 }, { "case_id": "observed-2026-09-02-002", "group": "observed", "family": "universal-refusal", "protocol": "positional-v0", "applicability": "scored", "status": "correct", "detail": "refused (count_mismatch)", "counterexample": null, "observed_refusal_kind": "count_mismatch", "expected_refusal_kinds": [ "count_mismatch" ], "ms": 0.047 }, { "case_id": "observed-2026-09-02-003", "group": "observed", "family": "universal-refusal", "protocol": "positional-v0", "applicability": "scored", "status": "correct", "detail": "refused (malformed_json)", "counterexample": null, "observed_refusal_kind": "malformed_json", "expected_refusal_kinds": [ "truncated_response", "malformed_json" ], "ms": 0.58 }, { "case_id": "observed-2026-09-02-004", "group": "observed", "family": "universal-refusal", "protocol": "positional-v0", "applicability": "scored", "status": "correct", "detail": "refused (malformed_json)", "counterexample": null, "observed_refusal_kind": "malformed_json", "expected_refusal_kinds": [ "truncated_response", "malformed_json" ], "ms": 0.567 }, { "case_id": "observed-2026-09-02-005", "group": "observed", "family": "protocol-association", "protocol": "positional-v0", "applicability": "not-applicable", "status": "not-applicable", "detail": "case is positional-v0; candidate declares keyed-v2", "counterexample": null, "observed_refusal_kind": "missing_id", "expected_refusal_kinds": [], "ms": 0.033 }, { "case_id": "observed-2026-09-02-006", "group": "observed", "family": "universal-refusal", "protocol": "positional-v0", "applicability": "scored", "status": "correct", "detail": "refused (count_mismatch)", "counterexample": null, "observed_refusal_kind": "count_mismatch", "expected_refusal_kinds": [ "count_mismatch" ], "ms": 0.164 }, { "case_id": "observed-2026-09-02-007", "group": "observed", "family": "universal-refusal", "protocol": "positional-v0", "applicability": "scored", "status": "correct", "detail": "refused (count_mismatch)", "counterexample": null, "observed_refusal_kind": "count_mismatch", "expected_refusal_kinds": [ "count_mismatch" ], "ms": 0.036 }, { "case_id": "observed-2026-09-02-008", "group": "observed", "family": "universal-refusal", "protocol": "positional-v0", "applicability": "scored", "status": "correct", "detail": "refused (count_mismatch)", "counterexample": null, "observed_refusal_kind": "count_mismatch", "expected_refusal_kinds": [ "count_mismatch" ], "ms": 0.038 }, { "case_id": "observed-2026-09-02-009", "group": "observed", "family": "universal-refusal", "protocol": "positional-v0", "applicability": "scored", "status": "correct", "detail": "refused (malformed_json)", "counterexample": null, "observed_refusal_kind": "malformed_json", "expected_refusal_kinds": [ "truncated_response", "malformed_json" ], "ms": 0.407 }, { "case_id": "observed-2026-09-02-010", "group": "observed", "family": "universal-refusal", "protocol": "positional-v0", "applicability": "scored", "status": "correct", "detail": "refused (count_mismatch)", "counterexample": null, "observed_refusal_kind": "count_mismatch", "expected_refusal_kinds": [ "count_mismatch" ], "ms": 0.043 }, { "case_id": "observed-2026-09-02-011", "group": "observed", "family": "universal-refusal", "protocol": "positional-v0", "applicability": "scored", "status": "correct", "detail": "refused 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"expected_refusal_kinds": [ "retries_exhausted", "no_recording" ], "ms": 0.004 }, { "case_id": "syn-positional-short", "group": "synthetic", "family": "universal-refusal", "protocol": "positional-v0", "applicability": "scored", "status": "correct", "detail": "refused (count_mismatch)", "counterexample": null, "observed_refusal_kind": "count_mismatch", "expected_refusal_kinds": [ "count_mismatch", "missing_id" ], "ms": 0.01 }, { "case_id": "syn-positional-long", "group": "synthetic", "family": "universal-refusal", "protocol": "positional-v0", "applicability": "scored", "status": "correct", "detail": "refused (count_mismatch)", "counterexample": null, "observed_refusal_kind": "count_mismatch", "expected_refusal_kinds": [ "count_mismatch", "duplicate_id" ], "ms": 0.011 }, { "case_id": "syn-positional-reordered", "group": "synthetic", "family": "protocol-association", "protocol": "positional-v0", "applicability": "not-applicable", "status": "not-applicable", "detail": "case is positional-v0; candidate declares keyed-v2", "counterexample": null, "observed_refusal_kind": "missing_id", "expected_refusal_kinds": [ "count_mismatch", "unexpected_shape", "unsupported_protocol", "invalid_type", "missing_id" ], "ms": 0.011 } ], "counts": { "correct": 58, "wrong-association": 0, "should-have-refused": 2, "should-have-parsed": 0, "crashed": 0, "timeout": 0, "missing-record": 0, "not-applicable": 4 }, "smallest_counterexample": null, "diagnostics": [ { "id": "out-of-protocol-association", "question": "On cases outside its declared protocol, did it refuse — or associate anyway?", "value": 0, "of": 4, "detail": "It produced no association on any case outside its declared protocol.", "case_ids": [] } ], "usage": { "model_calls": 12, "replay_spend_usd": 0, "recording_cost_usd": "unknown", "provider_reported": { "input_tokens": 272186, "output_tokens": 3043, "total_tokens": 275229, "tokens_used": 275229, "cost_usd": 0.06255452 }, "basis": "Grading replayed committed recordings and made no inference call; `replay_spend_usd` is that, and is $0. The investigation that authored the candidate did call a model: 12 call(s) to moonshotai/kimi-k2 through the AI Gateway, 275229 tokens, $0.06255452 as the gateway reported it per call. That is in `provider_reported`. The microVM is metered compute billed at a rate this repository does not record: 2672ms of active CPU across 739ms wall clock." }, "attempts": [ { "attempt_id": "article-to-verdict-association__agent-k2-generous-02#01", "started_at": "2026-09-30T03:45:35.111Z", "ended_at": "2026-09-30T03:45:41.566Z", "status": "succeeded", "runner": "vercel-sandbox", "note": "sandbox chocolate-rapid-crab-4NmX2Q in iad1, exit 0 in 458ms" } ] }