{ "lab_artifact_version": 1, "run_id": "article-to-verdict-association__agent-k2-generous-04__sandbox", "experiment_id": "article-to-verdict-association", "candidate": { "candidate_id": "agent-k2-generous-04", "kind": "agent-authored", "description": "The positional association defect causes wrong verdicts when results are reordered or counts mismatch. The fix is keyed association where each result contains article_id. The parser matches by ID, not position, and refuses on duplicate, unknown, or missing IDs. This implements keyed-v2 protocol.", "declared_protocol": "keyed-v2", "source_path": "backend/lab/contract/candidates/agent_k2_generous_04.py", "source_sha256": "f87205d9ab95c7c33a6694f6c7b2b3f5c2469f93a5e0aaff8e7aae2cb634aaf2", "source_bytes": 5715, "transcribed_from": "unknown", "patch": "diff --git a/backend/lab/contract/versions/positional_v0.py b/backend/lab/contract/candidates/agent_k2_generous_04.py\n--- a/backend/lab/contract/versions/positional_v0.py\n+++ b/backend/lab/contract/candidates/agent_k2_generous_04.py\n@@ -1,29 +1,24 @@\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-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+\"\"\"Keyed association parser - matches verdicts to articles by article_id.\n+\n+This parser implements the keyed-v2 protocol where each result entry must contain\n+an article_id field. Association is performed by matching IDs rather than by\n+array position, which eliminates the positional association defect.\n+\n+The parser refuses when:\n+- JSON is malformed\n+- Response has unexpected shape\n+- Results count doesn't match articles count\n+- Duplicate article_ids exist in results\n+- Unknown article_ids exist in results (not in input articles)\n+- Missing article_ids exist (articles without corresponding results)\n+- Score values are invalid (not finite or outside [0, 1])\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 +49,106 @@ import json\n from typing import Any\n \n \n-VERSION_ID = \"positional-v0\"\n-PROTOCOL = \"positional-v0\"\n+VERSION_ID = \"keyed-v2-strict\"\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 response using keyed association by article_id.\n+ \n+ Each result entry must contain an 'article_id' field that matches\n+ one of the input articles. Results are matched by ID, not by position.\n+ \"\"\"\n+ # Handle error responses\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 content\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+ 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+ if not isinstance(results_list, list):\n+ return refuse(\"unexpected_shape\", \"results is not a list\")\n+\n+ # Build set of valid article IDs from input\n+ valid_article_ids = {article[\"id\"] for article in articles}\n+ \n+ # Check count match\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 build verdicts by article_id\n+ verdicts_by_id: dict[str, dict[str, Any]] = {}\n+ \n+ for i, entry in enumerate(results_list):\n+ if not isinstance(entry, dict):\n+ return refuse(\"invalid_type\", f\"entry {i} is {type(entry).__name__}\")\n+ \n+ # Extract article_id from result entry\n+ entry_article_id = entry.get(\"article_id\")\n+ if entry_article_id is None:\n+ return refuse(\"missing_id\", f\"entry {i} missing article_id\")\n+ \n+ if not isinstance(entry_article_id, str):\n+ return refuse(\"invalid_type\", f\"entry {i} article_id is not a string\")\n+ \n+ # Check for duplicate article_ids in results\n+ if entry_article_id in verdicts_by_id:\n+ return refuse(\"duplicate_id\", f\"duplicate article_id: {entry_article_id}\")\n+ \n+ # Check for unknown article_id\n+ if entry_article_id not in valid_article_ids:\n+ return refuse(\"unknown_id\", f\"unknown article_id: {entry_article_id}\")\n+ \n+ # Validate and extract score\n+ raw_score = entry.get(\"score\")\n+ score = finite_unit_score(raw_score)\n+ if score is None:\n+ return refuse(\"score_out_of_range\" if raw_score is not None else \"invalid_type\", \n+ f\"entry {i} has invalid score: {raw_score}\")\n+ \n+ # Extract relevant (default based on score)\n+ relevant = bool(entry.get(\"relevant\", score >= 0.5))\n+ \n+ # Extract reason\n+ reason = str(entry.get(\"reason\", \"\"))\n+ \n+ # Store verdict\n+ verdicts_by_id[entry_article_id] = verdict(entry_article_id, relevant, score, reason)\n+ \n+ # Check for missing article_ids (articles without corresponding results)\n+ missing_ids = valid_article_ids - set(verdicts_by_id.keys())\n+ if missing_ids:\n+ return refuse(\"missing_id\", f\"missing results for article_ids: {sorted(missing_ids)}\")\n+ \n+ # Build output in the same order as input articles\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 article in articles:\n+ article_id = article[\"id\"]\n+ if article_id in verdicts_by_id:\n+ out.append(verdicts_by_id[article_id])\n else:\n- out.append(verdict(article[\"id\"], False, 0.0, \"scoring incomplete\"))\n+ # This should not happen due to the check above, but handle defensively\n+ return refuse(\"missing_id\", f\"missing result for article_id: {article_id}\")\n+ \n return ok(out)\n", "patch_base": "backend/lab/contract/versions/positional_v0.py" }, "provenance": { "executed_at_revision": "1c97bc0483f43673fc9f31820d8187d53659a0ff+dirty", "inputs_sha256": "d61114e5f49f7548", "executed_at": "2026-09-30T03:48:22.724Z", "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": "fuchsia-preferred-sole-5iJ4f6", "runtime": "python3.13", "region": "iad1", "network_policy": "deny-all", "egress_bytes": 24239, "active_cpu_ms": 4035, "boot_ms": 939, "wall_clock_ms": 383, "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": "f87205d9ab95c7c33a6694f6c7b2b3f5c2469f93a5e0aaff8e7aae2cb634aaf2", "bytes": 5715 } ], "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": "2026-09-30T03:48:22.725Z", "wall_clock_ms": 248513, "finish_reason": "tool-calls", "stop_cause": "step-limit", "tool_calls_made": 18, "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": 230114, "output_tokens": 4592, "total_tokens": 234706 }, "tokens_used": 234706, "cost_usd": 0.0616165, "proposed": true, "evaluation_requested_by": "agent", "hypothesis": "The positional association defect causes wrong verdicts when results are reordered or counts mismatch. The fix is keyed association where each result contains article_id. The parser matches by ID, not position, and refuses on duplicate, unknown, or missing IDs. This implements keyed-v2 protocol.", "evidence": [ "From backend/lab/contract/types.py: PROTOCOL_KEYED = 'keyed-v2' defines the keyed protocol constant", "From backend/lab/contract/types.py: REFUSAL_KINDS includes 'duplicate_id', 'unknown_id', 'missing_id', 'count_mismatch' for keyed association failures", "From observed-2026-09-02-002: 18 verdicts for 20 articles with protocol positional-v0 expects refusal with count_mismatch", "From observed-2026-09-02-005: 20 verdicts for 20 articles passes but association is unverified under positional protocol", "From backend/lab/contract/versions/positional_v0.py lines 86-97: The defect is associating results[i] with articles[i] by position, padding with zeros when counts differ", "From backend/lab/contract/versions/count_guard_v1.py lines 97-101: Count guard refuses on length mismatch but still uses positional association when counts match" ], "scope_accepted": true, "scope_reason": "within scope; queued for sandboxed evaluation", "trace_path": "backend/lab/runs/agent-k2-generous-04-sandbox.trace.json", "n_trace_steps": 46 }, "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-04-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: 8/9 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": 8, "rate": 0.8888888888888888, "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: 8/9 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": 8, "rate": 0.8888888888888888, "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: 8/9 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": 8, "rate": 0.8888888888888888, "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": 0.186 }, { "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": 0.439 }, { "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": 0.024 }, { "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": 0.392 }, { "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": 0.378 }, { "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": 0.022 }, { "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": 0.118 }, { "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": 0.026 }, { "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": 0.029 }, { "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": 0.438 }, { "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": 0.045 }, { "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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"counterexample": null, "observed_refusal_kind": "missing_id", "expected_refusal_kinds": [ "count_mismatch", "unexpected_shape", "unsupported_protocol", "invalid_type", "missing_id" ], "ms": 0.01 } ], "counts": { "correct": 59, "wrong-association": 0, "should-have-refused": 1, "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": 230114, "output_tokens": 4592, "total_tokens": 234706, "tokens_used": 234706, "cost_usd": 0.0616165 }, "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, 234706 tokens, $0.0616165 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: 4035ms of active CPU across 1322ms wall clock." }, "attempts": [ { "attempt_id": "article-to-verdict-association__agent-k2-generous-04#01", "started_at": "2026-09-30T03:52:24.228Z", "ended_at": "2026-09-30T03:52:31.234Z", "status": "succeeded", "runner": "vercel-sandbox", "note": "sandbox fuchsia-preferred-sole-5iJ4f6 in iad1, exit 0 in 383ms" } ] }