{ "lab_artifact_version": 1, "run_id": "article-to-verdict-association__agent-k2-generous-out-01__sandbox", "experiment_id": "article-to-verdict-association", "candidate": { "candidate_id": "agent-k2-generous-out-01", "kind": "agent-authored", "description": "The positional-v0 defect is associating results by array index without validating the association is unambiguous. When result count differs from article count, wrong verdicts get assigned. The fix validates structure, refuses on count_mismatch when len(results) != len(articles), validates score ranges with finite_unit_score, and only returns ok() for complete unambiguous associations. Evidence from observed-2026-09-02-002 shows count_mismatch is expected when counts differ.", "declared_protocol": "positional-v0", "source_path": "backend/lab/contract/candidates/agent_k2_generous_out_01.py", "source_sha256": "3af22fb105cee8e0045cc195c534a021647f46e88c55cefecd0bc0f908dad52a", "source_bytes": 4681, "transcribed_from": "unknown", "patch": "diff --git a/backend/lab/contract/versions/positional_v0.py b/backend/lab/contract/candidates/agent_k2_generous_out_01.py\n--- a/backend/lab/contract/versions/positional_v0.py\n+++ b/backend/lab/contract/candidates/agent_k2_generous_out_01.py\n@@ -1,30 +1,16 @@\n-\"\"\"Historical behaviour, transcribed from `origin/main`.\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+Robust batch scoring parser that refuses when association is ambiguous.\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+This parser addresses the positional association defect by:\n+1. Strictly validating response structure\n+2. Refusing when result count doesn't match article count\n+3. Validating score ranges and types\n+4. Only returning ok() when we have unambiguous complete association\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-\n import math as _math\n \n \n@@ -54,45 +40,97 @@ import json\n from typing import Any\n \n \n-VERSION_ID = \"positional-v0\"\n+VERSION_ID = \"candidate-robust-v1\"\n PROTOCOL = \"positional-v0\"\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-\n+ \"\"\"\n+ Parse batch scoring response and associate verdicts with articles.\n+ \n+ Returns ok(verdicts) only when we have a complete, unambiguous association.\n+ Returns refuse(kind, detail) when the response is malformed or ambiguous.\n+ \"\"\"\n+ # Handle error responses from the harness\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+ # Get response content\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-\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+ except json.JSONDecodeError as exc:\n+ return refuse(\"malformed_json\", str(exc))\n+ except Exception as exc:\n+ return refuse(\"malformed_json\", str(exc))\n+ \n+ # Validate top-level structure\n+ if not isinstance(result, dict):\n+ return refuse(\"unexpected_shape\", f\"top level is {type(result).__name__}\")\n+ \n+ # Extract results list\n+ results_list = result.get(\"results\", [])\n+ \n+ # Handle legacy \"scores\" format\n if not results_list and isinstance(result, dict) and \"scores\" in result:\n+ scores = result[\"scores\"]\n+ if not isinstance(scores, list):\n+ return refuse(\"unexpected_shape\", \"scores is not a list\")\n results_list = [\n {\"relevant\": float(s) >= 0.5, \"score\": float(s), \"reason\": \"\"}\n- for s in result[\"scores\"]\n+ for s in scores\n ]\n-\n+ \n+ # Validate results is a list\n+ if not isinstance(results_list, list):\n+ return refuse(\"unexpected_shape\", \"results is not a list\")\n+ \n+ # Check count mismatch - this is the key fix for the positional defect\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 each result entry\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+ for i, (article, entry) in enumerate(zip(articles, results_list)):\n+ # Validate entry is a dict\n+ if not isinstance(entry, dict):\n+ return refuse(\"invalid_type\", f\"entry {i} is {type(entry).__name__}\")\n+ \n+ # Extract and validate score using finite_unit_score\n+ raw_score = entry.get(\"score\", 0.5)\n+ score = finite_unit_score(raw_score)\n+ if score is None:\n+ return refuse(\"score_out_of_range\", f\"entry {i} has invalid score: {raw_score}\")\n+ \n+ # Extract relevant flag\n+ raw_relevant = entry.get(\"relevant\")\n+ if raw_relevant is None:\n+ relevant = score >= 0.5\n+ elif not isinstance(raw_relevant, bool):\n+ return refuse(\"invalid_type\", f\"entry {i} relevant is not boolean\")\n+ else:\n+ relevant = raw_relevant\n+ \n+ # Extract reason\n+ raw_reason = entry.get(\"reason\", \"\")\n+ if not isinstance(raw_reason, str):\n+ reason = str(raw_reason)\n else:\n- out.append(verdict(article[\"id\"], False, 0.0, \"scoring incomplete\"))\n+ reason = raw_reason\n+ \n+ # Build verdict\n+ out.append(verdict(article[\"id\"], relevant, score, reason))\n+ \n return ok(out)\n", "patch_base": "backend/lab/contract/versions/positional_v0.py" }, "provenance": { "executed_at_revision": "b4562908be4b5a488eca64f985005db7fcff6686+dirty", "inputs_sha256": "1fc8c57fec3adcaf", "executed_at": "2026-09-29T23:22:40.670Z", "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": "cyan-marine-cockroach-oJ4QKY", "runtime": "python3.13", "region": "iad1", "network_policy": "deny-all", "egress_bytes": 34532, "active_cpu_ms": 3089, "boot_ms": 308, "wall_clock_ms": 549, "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": "3af22fb105cee8e0045cc195c534a021647f46e88c55cefecd0bc0f908dad52a", "bytes": 4681 } ], "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": "2026-09-29T23:22:40.671Z", "wall_clock_ms": 175585, "finish_reason": "tool-calls", "stop_cause": "step-limit", "tool_calls_made": 18, "max_tool_calls": 24, "model_calls_made": 12, "budget_label": "generous-out", "budget": { "max_proposals": 1, "max_tool_calls": 24, "max_model_calls": 12, "max_output_tokens": 8192, "max_total_tokens": 300000, "max_tool_result_chars": 8000, "max_usd": 1.5, "wall_clock_seconds": 360 }, "budget_ceiling_usd": 1, "usage": { "input_tokens": 224264, "output_tokens": 4506, "total_tokens": 228770 }, "tokens_used": 228770, "cost_usd": 0.06333732, "proposed": true, "evaluation_requested_by": "agent", "hypothesis": "The positional-v0 defect is associating results by array index without validating the association is unambiguous. When result count differs from article count, wrong verdicts get assigned. The fix validates structure, refuses on count_mismatch when len(results) != len(articles), validates score ranges with finite_unit_score, and only returns ok() for complete unambiguous associations. Evidence from observed-2026-09-02-002 shows count_mismatch is expected when counts differ.", "evidence": [ "observed-2026-09-02-002 expects 'count_mismatch' refusal for 18 results vs 20 articles", "count_guard_v1.py lines 97-101 shows count_mismatch is correct handling for length mismatch", "types.py lines 23-44 defines REFUSAL_KINDS including count_mismatch, malformed_json, unexpected_shape, invalid_type, score_out_of_range", "positional_v0.py lines 86-97 shows the defect: silently pads missing results with zeros" ], "scope_accepted": true, "scope_reason": "within scope; queued for sandboxed evaluation", "trace_path": "backend/lab/runs/agent-k2-generous-out-01-sandbox.trace.json", "n_trace_steps": 43 }, "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-out-01-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": "association-exact: 0/3 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": 0, "rate": 0, "passed": false }, { "id": "protocol-violation-refusal", "question": "Does it refuse duplicate, unknown, missing ids and unusable scores?", "threshold": 1, "applicable": 1, "satisfied": 0, "rate": 0, "passed": false }, { "id": "no-crash", "question": "Does it terminate on every case without crashing or hanging?", "threshold": 1, "applicable": 52, "satisfied": 52, "rate": 1, "passed": true }, { "id": "complete-evidence", "question": "Is there a prediction record for every applicable case?", "threshold": 1, "applicable": 52, "satisfied": 52, "rate": 1, "passed": true }, { "id": "protocol-exclusivity", "question": "On cases outside its declared protocol, does it refuse rather than associate anyway?", "threshold": 1, "applicable": 12, "satisfied": 8, "rate": 0.6666666666666666, "passed": false } ], "gradings": [ { "spec_version": 1, "spec_hash": "008af8266204438a", "verdict": "rejected", "verdict_reason": "association-exact: 0/3 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": 0, "rate": 0, "passed": false }, { "id": "protocol-violation-refusal", "question": "Does it refuse duplicate, unknown, missing ids and unusable scores?", "threshold": 1, "applicable": 1, "satisfied": 0, "rate": 0, "passed": false }, { "id": "no-crash", "question": "Does it terminate on every case without crashing or hanging?", "threshold": 1, "applicable": 52, "satisfied": 52, "rate": 1, "passed": true }, { "id": "complete-evidence", "question": "Is there a prediction record for every applicable case?", "threshold": 1, "applicable": 52, "satisfied": 52, "rate": 1, "passed": true } ] }, { "spec_version": 2, "spec_hash": "f027762ab4d08b35", "verdict": "rejected", "verdict_reason": "association-exact: 0/3 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": 0, "rate": 0, "passed": false }, { "id": "protocol-violation-refusal", "question": "Does it refuse duplicate, unknown, missing ids and unusable scores?", "threshold": 1, "applicable": 1, "satisfied": 0, "rate": 0, "passed": false }, { "id": "no-crash", "question": "Does it terminate on every case without crashing or hanging?", "threshold": 1, "applicable": 52, "satisfied": 52, "rate": 1, "passed": true }, { "id": "complete-evidence", "question": "Is there a prediction record for every applicable case?", "threshold": 1, "applicable": 52, "satisfied": 52, "rate": 1, "passed": true }, { "id": "protocol-exclusivity", "question": "On cases outside its declared protocol, does it refuse rather than associate anyway?", "threshold": 1, "applicable": 12, "satisfied": 8, "rate": 0.6666666666666666, "passed": false } ] } ], "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": 0.234 }, { "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": 1.124 }, { "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": 0.048 }, { "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": 1.069 }, { "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": 1.086 }, { "case_id": "observed-2026-09-02-005", "group": "observed", "family": "protocol-association", "protocol": "positional-v0", "applicability": "scored", "status": "wrong-association", "detail": "no ground truth recorded", "counterexample": null, "observed_refusal_kind": null, "expected_refusal_kinds": [], "ms": 0.145 }, { "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": 0.31 }, { "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": 0.062 }, { "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": 0.064 }, { "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": 0.81 }, { "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": 0.048 }, { "case_id": "observed-2026-09-02-011", "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": 0.78 }, { "case_id": "observed-2026-09-02-012", "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": 0.748 }, { "case_id": "observed-2026-09-02-013", "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": 0.055 }, { "case_id": "observed-2026-09-02-014", "group": "observed", "family": "universal-refusal", "protocol": "positional-v0", "applicability": "scored", "status": "correct", "detail": "refused (malformed_json)", "counterexample": null, 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"case_id": "syn-exec-budget", "group": "synthetic", "family": "universal-refusal", "protocol": "keyed-v2", "applicability": "scored", "status": "correct", "detail": "refused (retries_exhausted)", "counterexample": null, "observed_refusal_kind": "retries_exhausted", "expected_refusal_kinds": [ "retries_exhausted", "no_recording" ], "ms": 0.006 }, { "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": 0.015 }, { "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": 0.018 }, { "case_id": "syn-positional-reordered", "group": "synthetic", "family": "protocol-association", "protocol": "positional-v0", "applicability": "scored", "status": "should-have-refused", "detail": "equal length, internally reordered — a length check cannot detect this", "counterexample": null, "observed_refusal_kind": null, "expected_refusal_kinds": [ "count_mismatch", "unexpected_shape", "unsupported_protocol", "invalid_type", "missing_id" ], "ms": 0.04 } ], "counts": { "correct": 48, "wrong-association": 3, "should-have-refused": 1, "should-have-parsed": 0, "crashed": 0, "timeout": 0, "missing-record": 0, "not-applicable": 12 }, "smallest_counterexample": null, "diagnostics": [ { "id": "out-of-protocol-association", "question": "On cases outside its declared protocol, did it refuse — or associate anyway?", "value": 4, "of": 12, "detail": "It produced a complete association on 4 case(s) outside its declared protocol. Under this generation that fails protocol-exclusivity; under generation 1 it was not graded at all.", "case_ids": [ "syn-keyed-in-order", "syn-keyed-reversed", "syn-keyed-rotated", "syn-unknown-id" ] } ], "usage": { "model_calls": 12, "replay_spend_usd": 0, "recording_cost_usd": "unknown", "provider_reported": { "input_tokens": 224264, "output_tokens": 4506, "total_tokens": 228770, "tokens_used": 228770, "cost_usd": 0.06333732 }, "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, 228770 tokens, $0.06333732 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: 3089ms of active CPU across 857ms wall clock." }, "attempts": [ { "attempt_id": "article-to-verdict-association__agent-k2-generous-out-01#01", "started_at": "2026-09-29T23:25:29.540Z", "ended_at": "2026-09-29T23:25:36.252Z", "status": "succeeded", "runner": "vercel-sandbox", "note": "sandbox cyan-marine-cockroach-oJ4QKY in iad1, exit 0 in 549ms" } ] }