{ "lab_artifact_version": 1, "run_id": "article-to-verdict-association__agent-k2-generous-out-06__sandbox", "experiment_id": "article-to-verdict-association", "candidate": { "candidate_id": "agent-k2-generous-out-06", "kind": "agent-authored", "description": "The defect is that positional association assumes results[i] corresponds to articles[i], which fails when the model returns results in a different order or omits some results. The fix is to use keyed association where each verdict explicitly contains the article_id it applies to. This allows us to verify that every article has exactly one verdict, detect duplicates, unknown IDs, and missing IDs. The parser expects each result entry to have an 'article_id' field and uses PROTOCOL_KEYED to signal this requirement.", "declared_protocol": "keyed-v2", "source_path": "backend/lab/contract/candidates/agent_k2_generous_out_06.py", "source_sha256": "94b1a5983d44ee8515ec0186a6baccf6dc5e4e53cea0ea59baa6191db56142e0", "source_bytes": 5119, "transcribed_from": "unknown", "patch": "diff --git a/backend/lab/contract/versions/positional_v0.py b/backend/lab/contract/candidates/agent_k2_generous_out_06.py\n--- a/backend/lab/contract/versions/positional_v0.py\n+++ b/backend/lab/contract/candidates/agent_k2_generous_out_06.py\n@@ -1,30 +1,13 @@\n-\"\"\"Historical behaviour, transcribed from `origin/main`.\n+\"\"\"Keyed association parser - fixes the positional association defect.\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 explicitly identifies which article it applies to via article_id,\n+allowing unambiguous association and detection of duplicates, unknown IDs,\n+and missing IDs.\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 +37,108 @@ 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-\n+ \"\"\"Parse response using keyed association.\n+ \n+ Each result entry must contain an 'article_id' field that explicitly\n+ identifies which article the verdict applies to. This allows verification\n+ of complete, unambiguous association.\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+ # Get 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+ return refuse(\"retries_exhausted\", \"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 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 expected article IDs\n+ expected_ids = {article[\"id\"] for article in articles}\n+ \n+ # Process results and validate associations\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+ # Get article_id from entry\n+ article_id = entry.get(\"article_id\")\n+ if article_id is None:\n+ return refuse(\"missing_id\", f\"entry {i} missing article_id\")\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 article_id\n+ if article_id not in expected_ids:\n+ return refuse(\"unknown_id\", f\"unknown article_id: {article_id}\")\n+ \n+ # Validate and extract score\n+ score_raw = entry.get(\"score\")\n+ if score_raw is None:\n+ return refuse(\"invalid_type\", f\"entry for {article_id} missing score\")\n+ \n+ score = finite_unit_score(score_raw)\n+ if score is None:\n+ return refuse(\"score_out_of_range\", f\"entry for {article_id} has invalid score: {score_raw}\")\n+ \n+ # Extract relevant (default based on score if not provided)\n+ relevant_raw = entry.get(\"relevant\")\n+ if relevant_raw is None:\n+ relevant = score >= 0.5\n else:\n- out.append(verdict(article[\"id\"], False, 0.0, \"scoring incomplete\"))\n+ if not isinstance(relevant_raw, bool):\n+ return refuse(\"invalid_type\", f\"entry for {article_id} has non-boolean relevant\")\n+ relevant = relevant_raw\n+ \n+ # Extract reason\n+ reason = entry.get(\"reason\", \"\")\n+ if not isinstance(reason, str):\n+ reason = str(reason)\n+ \n+ verdicts_by_id[article_id] = verdict(article_id, relevant, score, reason)\n+ \n+ # Check for missing article IDs\n+ received_ids = set(verdicts_by_id.keys())\n+ missing_ids = expected_ids - received_ids\n+ if missing_ids:\n+ return refuse(\"missing_id\", f\"missing verdicts for articles: {sorted(missing_ids)}\")\n+ \n+ # Check for count mismatch (shouldn't happen if we got here, but safety check)\n+ if len(verdicts_by_id) != len(articles):\n+ return refuse(\"count_mismatch\", f\"{len(verdicts_by_id)} results for {len(articles)} articles\")\n+ \n+ # Build ordered list matching input article order\n+ out: list[dict[str, Any]] = []\n+ for article in articles:\n+ article_id = article[\"id\"]\n+ out.append(verdicts_by_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": "080d784000c4dd4e", "executed_at": "2026-09-30T04:03:25.060Z", "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": "yellow-minimum-badger-Vb6BFb", "runtime": "python3.13", "region": "iad1", "network_policy": "deny-all", "egress_bytes": 23428, "active_cpu_ms": 1935, "boot_ms": 304, "wall_clock_ms": 411, "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": "94b1a5983d44ee8515ec0186a6baccf6dc5e4e53cea0ea59baa6191db56142e0", "bytes": 5119 } ], "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": "2026-09-30T04:03:25.062Z", "wall_clock_ms": 206434, "finish_reason": "stop", "stop_cause": "completed", "tool_calls_made": 17, "max_tool_calls": 24, "model_calls_made": 10, "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": 184744, "output_tokens": 4704, "total_tokens": 189448 }, "tokens_used": 189448, "cost_usd": 0.01784616, "proposed": true, "evaluation_requested_by": "agent", "hypothesis": "The defect is that positional association assumes results[i] corresponds to articles[i], which fails when the model returns results in a different order or omits some results. The fix is to use keyed association where each verdict explicitly contains the article_id it applies to. This allows us to verify that every article has exactly one verdict, detect duplicates, unknown IDs, and missing IDs. The parser expects each result entry to have an 'article_id' field and uses PROTOCOL_KEYED to signal this requirement.", "evidence": [ "From types.py: PROTOCOL_KEYED = 'keyed-v2' exists alongside PROTOCOL_POSITIONAL = 'positional-v0', indicating a keyed protocol is defined", "From types.py: REFUSAL_KINDS includes 'duplicate_id', 'unknown_id', 'missing_id' - these are specifically for keyed association failures", "From positional_v0.py: The defect is documented - 'association by ARRAY POSITION' causes misalignment when results are reordered or truncated", "From count_guard_v1.py: 'The guard is a real improvement... but it compares *lengths*, so an equal-length reordered response still passes through'", "From observed cases: Case observed-2026-09-02-002 shows 18 verdicts for 20 articles with expectation 'count_mismatch', demonstrating the need to refuse when counts don't match", "From observed cases: Case observed-2026-09-02-005 shows a valid case where count matches but association is 'parseable but unverified' - positional association cannot verify correct mapping" ], "scope_accepted": true, "scope_reason": "within scope; queued for sandboxed evaluation", "trace_path": "backend/lab/runs/agent-k2-generous-out-06-sandbox.trace.json", "n_trace_steps": 41 }, "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-06-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": "accepted-for-review", "verdict_reason": "all 6 criteria satisfied over 60 applicable cases", "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": 9, "rate": 1, "passed": true }, { "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": "accepted-for-review", "verdict_reason": "all 5 criteria satisfied over 60 applicable cases", "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": 9, "rate": 1, "passed": true }, { "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": "accepted-for-review", "verdict_reason": "all 6 criteria satisfied over 60 applicable cases", "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": 9, "rate": 1, "passed": true }, { "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 (missing_id)", "counterexample": null, "observed_refusal_kind": "missing_id", "expected_refusal_kinds": [ "count_mismatch" ], "ms": 0.177 }, { "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": 0.496 }, { "case_id": "observed-2026-09-02-002", "group": "observed", "family": "universal-refusal", "protocol": "positional-v0", "applicability": "scored", "status": "correct", "detail": "refused (missing_id)", "counterexample": null, "observed_refusal_kind": "missing_id", "expected_refusal_kinds": [ "count_mismatch" ], "ms": 0.029 }, { "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": 0.402 }, { "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": 0.39 }, { "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 (missing_id)", "counterexample": null, "observed_refusal_kind": "missing_id", "expected_refusal_kinds": [ "count_mismatch" ], "ms": 0.132 }, { "case_id": "observed-2026-09-02-007", "group": "observed", "family": "universal-refusal", "protocol": "positional-v0", "applicability": "scored", "status": "correct", "detail": "refused (missing_id)", "counterexample": null, "observed_refusal_kind": "missing_id", "expected_refusal_kinds": [ "count_mismatch" ], "ms": 0.03 }, { "case_id": "observed-2026-09-02-008", "group": "observed", "family": "universal-refusal", "protocol": "positional-v0", "applicability": "scored", "status": "correct", "detail": "refused (missing_id)", "counterexample": null, "observed_refusal_kind": "missing_id", "expected_refusal_kinds": [ "count_mismatch" ], "ms": 0.044 }, { "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": 0.299 }, { "case_id": "observed-2026-09-02-010", "group": "observed", "family": "universal-refusal", "protocol": "positional-v0", "applicability": "scored", "status": "correct", "detail": "refused (missing_id)", "counterexample": null, "observed_refusal_kind": "missing_id", "expected_refusal_kinds": [ "count_mismatch" ], "ms": 0.019 }, { "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": 0.288 }, { 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"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": 10, "replay_spend_usd": 0, "recording_cost_usd": "unknown", "provider_reported": { "input_tokens": 184744, "output_tokens": 4704, "total_tokens": 189448, "tokens_used": 189448, "cost_usd": 0.01784616 }, "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: 10 call(s) to moonshotai/kimi-k2 through the AI Gateway, 189448 tokens, $0.01784616 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: 1935ms of active CPU across 715ms wall clock." }, "attempts": [ { "attempt_id": "article-to-verdict-association__agent-k2-generous-out-06#01", "started_at": "2026-09-30T04:05:54.778Z", "ended_at": "2026-09-30T04:06:00.896Z", "status": "succeeded", "runner": "vercel-sandbox", "note": "sandbox yellow-minimum-badger-Vb6BFb in iad1, exit 0 in 411ms" } ] }