{"claim":{"id":"3e8fe728-aacc-4477-a932-9a2c799bf3f8","submission_id":"f9888591-da07-40fa-9c39-ea3713e60d17","submission_revision_id":"47378546-cbc1-40dc-a0c0-1571c465cfdd","builder":"builder-01","status":"active","claimed_at":"2026-10-15T15:26:23.283233Z","delivered_at":null,"ended_at":null,"delivery_note":null,"end_reason":null},"task":{"submission_id":"f9888591-da07-40fa-9c39-ea3713e60d17","submission_revision_id":"47378546-cbc1-40dc-a0c0-1571c465cfdd","revision_version":3,"submission_status":"under_review","submitted_at":"2026-09-13T17:07:16.353748Z","submitter_name":"Ziqi Liu","submitter_email":"ziqi.liu1030@gmail.com","task":{"status":"submitted","version":3,"extra_fields":{},"submitted_at":"2026-09-13T17:07:16.353748+00:00","industry_domain":"Data Engineering / Order CDC Incident Recovery","task_short_name":"order_cdc_recovery_913_v1","operating_system":"MacOS","task_description":"Repair an order change-data-capture loader and reconstruct its history after a failed deployment.\n\nSTARTING FILES\ninput_materials_final.zip contains TASK.md (assignment/CLI), CONTRACT.md (complete normative rules, schemas and rubric), ENVIRONMENT.md (actual tested environment), data/manifest.json, data/cutoffs.json, data/batch_001.jsonl through batch_008.jsonl, starter/pipeline.py and starter/warehouse.sqlite. Batches 001–003 have 591 lines each; 004–008 have 590 each. The fixture contains 900 original synthetic orders and 4,723 delivered events. It is not real customer data. The v1 SQLite database contains business_notes(id INTEGER PRIMARY KEY,note TEXT NOT NULL) with one operational note. The starter incorrectly retains only the last delivered payload and commits each row.\n\nASSIGNMENT\n1. Repair or replace the Python/SQLite loader as solution/pipeline.py. Migrate the database to user_version=2 while preserving business_notes. Persist every unique canonical envelope in raw_events(raw_id TEXT PRIMARY KEY,payload TEXT NOT NULL), using SHA-256 of the full canonical envelope as raw_id. Ingest each batch in one transaction, including migration.\n2. Ingest all batches in any order. Export day1 through day5 using the daily UTC received-time cutoffs in cutoffs.json (2025-04-02 through 2025-04-06, 00:00:00Z). Rebuild each snapshot only from evidence visible at that cutoff.\n3. Produce decisions, history, current state and currency/status totals under the rules below and the complete CONTRACT.md. Demonstrate idempotence, reversed ingestion and injected pre-commit failure/retry. Include an incident_report.md citing actual event IDs for observed defect categories and a run_log.txt.\n\nKEY RULES\nEach event has event_id, revision (positive integer), received_at, order_id, effective_at, source_seq, op and data. Times use canonical UTC YYYY-MM-DDTHH:MM:SSZ. For each cutoff select the highest visible revision per event_id. Different selected-revision bodies after removing only received_at are a CONFLICT; identical semantic replays count as one candidate. An invalid highest revision is INVALID with no fallback. Sort valid candidates by (effective_at,source_seq,event_id).\nUPSERT is a full replacement with exactly customer_id (nonempty string), currency (USD/EUR/JPY), amount_minor (integer >=0, not Boolean), status (OPEN/PAID/CANCELLED), and region (nonempty string or null). PATCH is a nonempty subset of these fields; omitted fields stay unchanged and explicit null clears region only. Unknown data keys are invalid. DELETE requires empty data. PATCH/DELETE on an absent or deleted order is ORPHAN. UPSERT can create or restore. No other status transitions are restricted. A DELETE clears all five business fields to null. Invalid/conflicting/orphan events create no history row. Canonical JSON and all tie/ordering rules are specified in CONTRACT.md.\n\nOUTPUTS\nsolution/pipeline.py; warehouse.sqlite; incident_report.md; run_log.txt; snapshots/day1 through day5, each containing:\n- decisions.json: rows {event_id,revision,decision}, one per selected event_id, sorted by event_id; decision is APPLIED, ORPHAN, INVALID or CONFLICT.\n- history.json: one row per applied event with order_id,event_id,revision,valid_from,valid_to,deleted,customer_id,currency,amount_minor,status,region. valid_to is the next applied event time for that order or null. Retain same-time zero-length intervals; sort by order_id and event replay order.\n- current.json: each order's last history row, including tombstones, sorted by order_id.\n- totals.json: nondeleted current orders grouped by currency,status, keys currency,status,order_count,amount_minor; include CANCELLED, omit empty groups, sort currency/status. Use exact integer amounts and no currency conversion.\n\nCLI\npython3 solution/pipeline.py ingest --db warehouse.sqlite --batch data/batch_001.jsonl\npython3 solution/pipeline.py ingest --db warehouse.sqlite --batch data/batch_002.jsonl --fail-before-commit\npython3 solution/pipeline.py export --db warehouse.sqlite --cutoff 2025-04-02T00:00:00Z --out snapshots/day1\nThe injected failure must exit nonzero without changing previously committed tables; retry without the flag must succeed. Export must not modify the database or input. No hardcoded fixture-specific answers; equivalent unseen identities/amounts are tested.\n\nPROVENANCE AND ENVIRONMENT\nContributor: Liu Ziqi. Original synthetic task with reference outputs generated by actual program execution. Python 3.9.6, SQLite 3.51.0, macOS 26.5.2 arm64 were actually tested. Standard library only; no extra dependencies, paid services, network or GPU. Linux/Windows portability has not been run; see ENVIRONMENT.md. No blind frontier-model trial or expert-duration study has been performed; no difficulty tier or authorship eligibility is claimed.\n\nCONTRIBUTOR AUTHORIZATION\nLiu Ziqi explicitly approved the Agents' Last Exam Contributor Terms and the data/code rights authorization on 2026-09-14. The full declaration is attached in reference_and_evaluation_final.zip at reference/CONTRIBUTOR_AUTHORIZATION.md. It authorizes benchmark use, data/documentation release under CC BY 4.0 and original-code release under Apache-2.0, consistent with the Contributor Terms.","software_licensing":"Free/Open Source","evaluation_criteria":null,"verification_method":"Use the separate reference_and_evaluation_final.zip as evaluator-only material; never stage its solution, oracle, generator or reference outputs in the agent workspace. The package contains the actual reference executable and database, all 20 snapshot JSON files, metrics, incident report, command logs, independent SQL/Python oracle and executable grader.\n\nRun in an isolated, network-disabled benchmark sandbox:\npython3 reference/evaluation/grade.py --solution <agent>/solution/pipeline.py --input input --deliverables <agent-output-root> --report grade_report.json\n\n100 points, all-or-nothing per item: migration/preserved business note 10; complete canonical raw evidence and idempotence 10; exact decisions at all five cutoffs 15; exact history at all cutoffs 20; exact current state 10; exact currency/status totals 10; reversed and repeated batch replay 10; pre-commit failure leaves unchanged committed state and retry succeeds 10; input integrity plus contract-equivalent unseen identity/amount perturbations 5.\n\nCompare JSON logical values, exact types and array order; ignore whitespace and object-key order. Compare SQLite logical table contents and schema version, not physical database bytes. Require 100/100 and matching saved output artifacts plus nonempty incident_report.md/run_log.txt for full success. Partial scores remain available for analysis. The report is an evidence/completeness deliverable, not a subjective style score.\n\nReference correctness check passed 100/100, including saved artifacts. Three semantic/recovery mutation controls failed as intended (45/100, 20/100, 90/100); broken starter scored 0. These are software verification controls, NOT frontier-model self-test scores. No difficulty self-test result is claimed. Current harness is a local correctness evaluator, not an adversarial code sandbox; ALE must provide process/reference isolation. Each subprocess has a 30-second operational timeout. Linux/Windows verification remains pending.\n\nMINOR REVISION RESPONSE\nThe sole material gap identified by the last DeepReview was finite contributor-side rights/terms confirmation. This is now recorded in reference/CONTRIBUTOR_AUTHORIZATION.md, explicitly approved by Liu Ziqi on 2026-09-14. The reference README points to that declaration. The task, functional inputs, solution, grader behavior and generated output values remain unchanged; this revision updates contributor attribution and authorization documentation.","software_and_version":"Python 3.9.6; SQLite 3.51.0 (standard library sqlite3)","specific_requirements":null,"expected_completion_time":null},"files":[{"file_id":"35bc12e0-959b-403b-a6b8-f01fd4c78a1b","filename":"input_materials_final.zip","category":"input_materials","content_type":"application/zip","size_bytes":73624},{"file_id":"4fb74892-9dc5-4c04-ad5c-68ef3d4af138","filename":"reference_and_evaluation_final.zip","category":"reference_output","content_type":"application/zip","size_bytes":733079}],"deep_review":{"overall_verdict":"Accept","review_text":"**Overall Verdict:** Accept — The revised submission is complete, coherent, reachable, and has an objective offline evaluation path; no material gap remains.\n\n**Completeness Check:**\n- Instruction: Complete — `TASK.md` and `CONTRACT.md` define the recovery workflow, schemas, transaction semantics, outputs, CLI, and acceptance conditions.\n- Inputs: Complete — `input_materials_final.zip` contains all eight batches, metadata, cutoff definitions, starter code, and the legacy SQLite database; manifest counts match the 4,723 parsed records.\n- Toolchain: Complete — Standard-library Python and SQLite are sufficient, with no credentials, network, or paid dependencies required. Linux/Windows portability remains uncertified but is not a material reachability issue.\n- Reference output: Complete — The evaluator-only package contains the reference implementation, evaluator, oracle, saved database, all 20 snapshot artifacts, metrics, logs, authorization declaration, and a successful saved grade report.\n- Rubric: Complete — The 100-point rubric covers migration, evidence retention, exact outputs, replay order, transaction recovery, perturbation behavior, and artifact completeness.\n\n**File and Task Reachability:**\n\nThe attachment classification is `reachable`, and the task classification is `reachable`. I inspected both ZIP archives, their member inventories, integrity, regular-file modes, path safety, encodings, and declared sizes. I inspected `TASK.md`, `CONTRACT.md`, `ENVIRONMENT.md`, `manifest.json`, `cutoffs.json`, all eight JSONL batches, the starter script and database, `CONTRIBUTOR_AUTHORIZATION.md`, `EVALUATOR_README.md`, all evaluator/reference scripts, the saved grade and metrics reports, the incident report, run log, reference database, and all 20 snapshot JSON files. Every JSON artifact parsed successfully; large arrays were structurally summarized and boundary-sampled rather than manually enumerated row by row. No required attachment was unreadable or left inaccessible.\n\nThe solver-visible input package is separated from the evaluator-only reference package, and no answer artifact or evaluator implementation was found in the input package. The end-to-end path is clear: a contributor uses a standard-library Python/SQLite implementation, ingests the supplied batches, exports the five snapshots, and is checked by the independent evaluator and perturbation tests. The saved reference report records 100/100 with all checks passing. The review environment lacked a Python runtime and SQLite command-line client, so I did not execute uploaded programs or independently query the databases; this is a reviewer-tooling limitation, not a submission defect.\n\nLicense keys, accounts, tokens, and other credentials are not required from the contributor.  \nConfidential, proprietary, or internal material may be evaluated as submitted evidence without requiring additional disclosure.\n\n**Special Screening:**\n\n`synthetic_defect_fixture` — The task text and reviewer-only fixture generator affirmatively show deliberately injected conflicts, invalid revisions, orphan operations, tombstones, replays, and tie cases as the core exercise. There is no affirmative evidence that the material is AI-generated, and public-benchmark status was not established.\n\n**Recommended Changes:**\n\nThe revision appropriately adds the contributor rights and terms authorization that was previously missing, while preserving the task, inputs, evaluator, and generated outputs. No contributor changes are required.","reviewed_at":"2026-09-13T17:15:37.700341Z"},"claim":{"id":"3e8fe728-aacc-4477-a932-9a2c799bf3f8","submission_id":"f9888591-da07-40fa-9c39-ea3713e60d17","submission_revision_id":"47378546-cbc1-40dc-a0c0-1571c465cfdd","builder":"builder-01","status":"active","claimed_at":"2026-10-15T15:26:23.283233Z","delivered_at":null,"ended_at":null,"delivery_note":null,"end_reason":null}},"revision_is_current":true}