{"category":"coding","cohort":{"bank_hash":"fce29b4d66cfddeab39632d451935271a37df595374b40eacd300201e3726291","created":"2026-10-07T05:45:33.170758+00:00","id":"ad732efdaeacda89112acf6d78781f9edaa820755bd6fda5aa42e2419256b672","initiated_trials":36,"protocol_hash":"90b114dd8a70ecbf3aecc35f58cea1ba85c786554aee9835a38a1c518293da05","required_trials":36,"status":"complete"},"disclosure":"No supplied credits or affiliate influence recorded. Declare actual provider relationships before release.","models":[{"admission_status":"genuine_configuration_canary_required_before_scoring","configuration":"gpt-6-luna-standard","effort":"medium","enabled":true,"endpoint":"https://api.openai.com/v1/responses","id":"gpt-6-luna","input_usd_per_million":0.1,"key_env":"KINGY_BEST_OPENAI_KEY","label":"GPT-6 Luna","max_output_tokens":8192,"output_usd_per_million":0.5,"price_source":"https://developers.openai.com/api/docs/pricing","processing_profile":"standard","prompt_cache":"none","provider":"openai","service_tier":"default","source":"https://developers.openai.com/api/docs/models/gpt-6-luna","surface":"OpenAI direct API, Standard global","timeout_seconds":180}],"protocol":{"activation":"Prepared under offline-audit instruction. No paid execution until explicit user authorization lifts the hold; original approved definitions and evidence are retained.","attempts_per_trial":1,"categories":["coding","math"],"coding_artifact_contract":"Python source only; restricted module members and safe built-ins stated explicitly; input mutation is rejected on every task; return JSON-serializable values. Python None represents null. Ordered mapping tasks enforce dictionary key order.","coding_numeric_contract":"Numeric strings are rejected for coding; integer expectations use exact numerical equality. Floating values retain the declared tolerance.","coding_scope":"Python function implementation on twelve specified software maintenance tasks; no IDE, repository editing, browsing or agent harness.","cohort_window_hours":72,"cost":"Retain estimated consumption, billed reconciliation, fee holds and cash purchases separately. All cohort attempts count in cost numerators. Zero successes means unavailable cost per success. No cost winner until metering/account pricing is reconciled.","daily_policy":"Bootstrap through the ordinary host event, then fixed daily 09:00 UTC cadence. Failures retry after one hour while retaining last success. Existing WordPress system cron dispatches a feature-scoped event; no competing coordinator.","grader_hash":"62980813a06357fd89c914858db26855ffe2bd65a7e18f326d827fb6610e7e23","grader_version":"deterministic-v2","launch_policy":"A reviewed official new model, major capability change, retirement or silent alias change affecting admitted entrants queues one category rerun. Source-body change alone queues review, not a declared launch or paid call. Newly admitted entrants require scoped approval.","math_abs_tolerance":1e-06,"math_artifact_contract":"Exactly one JSON object with finite numeric values, unique requested fields and no Markdown fences or surrounding prose. Discrete fields declared in integer_fields use exact numeric equality; remaining fields use declared tolerances. Booleans and numeric strings are rejected. Finite decimal answers need no trailing-zero padding.","math_relative_tolerance":1e-08,"math_scope":"Twelve applied quantitative problems with declared numeric/JSON answers; explanation quality is not scored.","monthly_policy":"Due one calendar month after latest completed cohort, or first complete run once authorized. UTC scheduling.","output_cap_tokens":8192,"publication":"Only complete matched cohorts with genuine provider evidence, replayed grades, consistent declared identities and current approved protocol may produce rankings. Transport failure counts as failed initiated trial only when its accounting/outcome is settled; unresolved outcomes block rankings.","repetitions":3,"retention_months":12,"retry_policy":"No automatic paid retries, including transient errors. Uncertain submissions retain reservations and block further paid dispatch. A replacement trial requires a declared new cohort revision and explicit reason.","review":"Offline review completed October 4, 2026. Inactive draft; original definitions, evidence and paid hold remain unchanged. Added v2 edge cases are future checks only and must never be applied to original outputs.","scoring":"No LLM judge. Deterministic tests only. Human task-bank approval required before scored execution.","subjective_policy":"No quality ranking until a separately approved blind-vote protocol and real sufficient audience participation. A new output/model version needs a new round.","tasks_per_category":12,"thinking":"Provider-specific medium settings; equal output cap, not equal internal compute.","version":"tested-best-v2-reviewed","weighting":"Each task contributes equally. Task passes only if every frozen check passes. Three repetitions are all retained; no best-output selection."},"schema":"kingy-best-evidence-v1","trials":[{"answer":"def solve(data):\n    total = data[\"total\"]\n    weights = data[\"weights\"]\n    if not weights:\n        return []\n    weight_sum = sum(weights)\n    if weight_sum == 0:\n        return [0 for _ in weights]\n\n    shares = []\n    remainders = []\n    for weight in weights:\n        share, remainder = divmod(total * weight, weight_sum)\n        shares.append(share)\n        remainders.append(remainder)\n\n    leftover = total - sum(shares)\n    order = sorted(range(len(weights)), key=lambda i: (-remainders[i], i))\n    for i in range(leftover):\n        shares[order[i]] += 1\n    return shares","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.00017,"finished_at":"2026-10-07T05:47:53.613467+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true},{"case":6,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"6f5030ca56474368542c2b29a8e7e9e440c09d0ccaf53dfa32d4ad8c66c59210","latency_seconds":2.911502,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is {total:nonnegative integer,weights:[nonnegative integer,...]}. Allocate proportionally using largest remainders: floor each share, then give leftover cents to largest fractional remainders, ties to lower index. 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Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is {total:nonnegative integer,weights:[nonnegative integer,...]}. Allocate proportionally using largest remainders: floor each share, then give leftover cents to largest fractional remainders, ties to lower index. If weights empty return []; if all zero return zeros.","raw_response_hash":"eb5686b9b648bce7b5afe7090e4690a751a4a2ef6fab7b754fa899213b453e4c","repetition":2,"request_hash":"20924534b87fe63a4760a01fabb57f50b7f14173d5e5fe950165110a9d878e26","request_id":"req_158aacaa66db474facd88315a943cfaa","sent_at":"2026-10-07T05:48:51.497977+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-allocate","trial_id":"067039fedda739681baabc0428e15ee24204a2c40bf9b56d4f7c9167b73d1bbd","usage":{"input_tokens":397,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":213,"output_tokens_details":{"reasoning_tokens":79},"total_tokens":610}},{"answer":"def solve(data):\n    weights = data[\"weights\"]\n    if not weights:\n        return []\n    weight_sum = sum(weights)\n    if weight_sum == 0:\n        return [0 for _ in weights]\n\n    shares = [divmod(data[\"total\"] * weight, weight_sum) for weight in weights]\n    result = [share[0] for share in shares]\n    leftover = data[\"total\"] - sum(result)\n    order = sorted(range(len(weights)), key=lambda i: (-shares[i][1], i))\n    for i in order[:leftover]:\n        result[i] += 1\n    return result","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000158,"finished_at":"2026-10-07T05:51:49.958772+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true},{"case":6,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"6f5030ca56474368542c2b29a8e7e9e440c09d0ccaf53dfa32d4ad8c66c59210","latency_seconds":2.86994,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is {total:nonnegative integer,weights:[nonnegative integer,...]}. Allocate proportionally using largest remainders: floor each share, then give leftover cents to largest fractional remainders, ties to lower index. If weights empty return []; if all zero return zeros.","raw_response_hash":"921d194a8a2269010692b30e9e951c80cffb764e1986686a5cc2705dd35903fc","repetition":3,"request_hash":"20924534b87fe63a4760a01fabb57f50b7f14173d5e5fe950165110a9d878e26","request_id":"req_c6e72c5b91bf47e4b306e0825d114adf","sent_at":"2026-10-07T05:51:47.088832+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-allocate","trial_id":"93ad0db69172840731b52dc81a6ea30bdcdd4ca10919ce064d163202593ed647","usage":{"input_tokens":397,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":235,"output_tokens_details":{"reasoning_tokens":101},"total_tokens":632}},{"answer":"import csv\nimport io\nimport re\n\ndef solve(data):\n    totals = {}\n    header_seen = False\n    for row in csv.reader(io.StringIO(data)):\n        if not row:\n            continue\n        if not header_seen:\n            header_seen = True\n            continue\n        if len(row) != 2:\n            continue\n        customer, cents = row\n        if customer.strip() == \"\":\n            continue\n        cents = cents.strip()\n        if re.fullmatch(r\"[+-]?[0-9]+\", cents) is None:\n            continue\n        totals[customer] = totals.get(customer, 0) + int(cents)\n    return totals","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000415,"finished_at":"2026-10-07T05:46:43.175207+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"514825fdf0f8f010cccd775b5916d114475bd2f64f5caaee4068d62ffafebf03","latency_seconds":7.724057,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is CSV text with header customer,cents, including quoted fields. Treat a customer containing only whitespace as blank. Trim surrounding whitespace from cents, then accept only an optional ASCII + or - followed by one or more ASCII digits 0-9. Ignore blank customers and invalid cents. Sum by customer and return a dictionary, including zero totals. Preserve customer spelling. CSV quoting is valid and the header is exactly customer,cents. Ignore rows missing either field or containing extra fields. Ignore empty lines. Whitespace means Python str.strip() whitespace. No dictionary key order is required.","raw_response_hash":"b76468d00da67f27698620cceee2a0a4324f2b895364e437bf8d2137f8aa2627","repetition":1,"request_hash":"605a7075d7617588068e7c31668c82dfce5530af2ecb763ad193b0647356d00a","request_id":"req_31caf53f7d0e4d2cae2f001c2e24e4c0","sent_at":"2026-10-07T05:46:35.451150+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-csv","trial_id":"da686d9e1b1bdbd20f6f8dbb1a03eba811c6720b1e771a3dc00609e148b8d3d4","usage":{"input_tokens":456,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":737,"output_tokens_details":{"reasoning_tokens":595},"total_tokens":1193}},{"answer":"import csv\nimport io\nimport re\n\ndef solve(data):\n    totals = {}\n    pattern = re.compile(r\"[+-]?[0-9]+\")\n    reader = csv.reader(io.StringIO(data))\n    next(reader)\n    for row in reader:\n        if len(row) != 2:\n            continue\n        customer, cents = row\n        cents = cents.strip()\n        if not customer.strip() or not pattern.fullmatch(cents):\n            continue\n        totals[customer] = totals.get(customer, 0) + int(cents)\n    return totals","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000287,"finished_at":"2026-10-07T05:49:51.441058+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"514825fdf0f8f010cccd775b5916d114475bd2f64f5caaee4068d62ffafebf03","latency_seconds":5.619249,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is CSV text with header customer,cents, including quoted fields. Treat a customer containing only whitespace as blank. Trim surrounding whitespace from cents, then accept only an optional ASCII + or - followed by one or more ASCII digits 0-9. Ignore blank customers and invalid cents. Sum by customer and return a dictionary, including zero totals. Preserve customer spelling. CSV quoting is valid and the header is exactly customer,cents. Ignore rows missing either field or containing extra fields. Ignore empty lines. Whitespace means Python str.strip() whitespace. No dictionary key order is required.","raw_response_hash":"74732be07f2fffe20d624cfac956f3cb359c785ba418b35e02c122ccb5d3e10c","repetition":2,"request_hash":"605a7075d7617588068e7c31668c82dfce5530af2ecb763ad193b0647356d00a","request_id":"req_185534c182f7405aa5f8a0c06e1d1043","sent_at":"2026-10-07T05:49:45.821809+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-csv","trial_id":"4ef2e0b2f1942b70555260690248718308dfa0082c3e253cdd9e4d486b8bb547","usage":{"input_tokens":456,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":482,"output_tokens_details":{"reasoning_tokens":361},"total_tokens":938}},{"answer":"import csv\nimport io\nimport re\n\ndef solve(data):\n    rows = csv.reader(io.StringIO(data))\n    header = next(rows, None)\n    while header == []:\n        header = next(rows, None)\n\n    totals = {}\n    if header != [\"customer\", \"cents\"]:\n        return totals\n\n    for row in rows:\n        if len(row) != 2:\n            continue\n        customer, cents = row\n        if customer.strip() == \"\":\n            continue\n        cents = cents.strip()\n        if re.fullmatch(r\"[+-]?[0-9]+\", cents) is None:\n            continue\n        totals[customer] = totals.get(customer, 0) + int(cents)\n\n    return totals","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000486,"finished_at":"2026-10-07T05:51:09.555646+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"514825fdf0f8f010cccd775b5916d114475bd2f64f5caaee4068d62ffafebf03","latency_seconds":9.070426,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is CSV text with header customer,cents, including quoted fields. Treat a customer containing only whitespace as blank. Trim surrounding whitespace from cents, then accept only an optional ASCII + or - followed by one or more ASCII digits 0-9. Ignore blank customers and invalid cents. Sum by customer and return a dictionary, including zero totals. Preserve customer spelling. CSV quoting is valid and the header is exactly customer,cents. Ignore rows missing either field or containing extra fields. Ignore empty lines. Whitespace means Python str.strip() whitespace. No dictionary key order is required.","raw_response_hash":"c10c73c6c84cd73a6462928208b72c3b74fd6478e53d53eb30ece3ff53d39358","repetition":3,"request_hash":"605a7075d7617588068e7c31668c82dfce5530af2ecb763ad193b0647356d00a","request_id":"req_87a480eadc19417981c333a26d2c2271","sent_at":"2026-10-07T05:51:00.485220+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-csv","trial_id":"1acbafe47a283737168d070f755c8305f4af085837b71804974e4202762bd1a9","usage":{"input_tokens":456,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":879,"output_tokens_details":{"reasoning_tokens":724},"total_tokens":1335}},{"answer":"def solve(data):\n    retained = {}\n    for item in data:\n        identifier = item[\"id\"]\n        if identifier not in retained or item[\"version\"] >= retained[identifier][\"version\"]:\n            retained[identifier] = item\n    return [retained[identifier] for identifier in sorted(retained)]","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000126,"finished_at":"2026-10-07T05:46:31.284267+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"8e2989420ce8c0c1ec828ca1b96988dc8f579529013af062454476199c3f3614","latency_seconds":2.281792,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is a list of {id, version, value}. Keep the highest version for each id; on equal version keep the last occurrence. Return the retained objects sorted lexicographically by id. Do not mutate the input. IDs are strings, versions are integers, and values are arbitrary JSON.","raw_response_hash":"ff0a7c3c7061080729541eb01e9aaa9f201c8469ddeb27dba5b225ecc3fe5b97","repetition":1,"request_hash":"f845f1728db1a6dd8e711abef8995177027bb747c2b66d5b670808795f77d107","request_id":"req_021ea29f5ff9468c8fc29f178899faeb","sent_at":"2026-10-07T05:46:29.002475+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-dedup","trial_id":"12bdd68acf5d6dbdf5c9b98483bfa6456ead31c5f749c06088f99626113412c6","usage":{"input_tokens":399,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":171,"output_tokens_details":{"reasoning_tokens":103},"total_tokens":570}},{"answer":"def solve(data):\n    retained = {}\n    for item in data:\n        item_id = item[\"id\"]\n        if item_id not in retained or item[\"version\"] >= retained[item_id][\"version\"]:\n            retained[item_id] = item\n    return sorted(retained.values(), key=lambda item: item[\"id\"])","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000146,"finished_at":"2026-10-07T05:50:09.920889+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"8e2989420ce8c0c1ec828ca1b96988dc8f579529013af062454476199c3f3614","latency_seconds":2.624921,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is a list of {id, version, value}. Keep the highest version for each id; on equal version keep the last occurrence. Return the retained objects sorted lexicographically by id. Do not mutate the input. IDs are strings, versions are integers, and values are arbitrary JSON.","raw_response_hash":"30520734b1ada5e97955ef93d44b4dd8576a963fc11026caee0ddce04292f623","repetition":2,"request_hash":"f845f1728db1a6dd8e711abef8995177027bb747c2b66d5b670808795f77d107","request_id":"req_b16402f79ebf49b6825509b3458b3124","sent_at":"2026-10-07T05:50:07.295968+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-dedup","trial_id":"30774dfe75fe2594cd0fdda57ee1c84df64e9533b493a0f2b9a46d2b4bd506a2","usage":{"input_tokens":399,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":211,"output_tokens_details":{"reasoning_tokens":141},"total_tokens":610}},{"answer":"def solve(data):\n    retained = {}\n    for item in data:\n        identifier = item[\"id\"]\n        version = item[\"version\"]\n        if identifier not in retained or version >= retained[identifier][0]:\n            retained[identifier] = (version, item)\n    return [retained[identifier][1] for identifier in sorted(retained)]","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000128,"finished_at":"2026-10-07T05:51:26.596458+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"8e2989420ce8c0c1ec828ca1b96988dc8f579529013af062454476199c3f3614","latency_seconds":2.425351,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is a list of {id, version, value}. Keep the highest version for each id; on equal version keep the last occurrence. Return the retained objects sorted lexicographically by id. Do not mutate the input. IDs are strings, versions are integers, and values are arbitrary JSON.","raw_response_hash":"5e36979627dfd29b08c214839d76a443feb4cb0c164875479e8f83dbafe82f7d","repetition":3,"request_hash":"f845f1728db1a6dd8e711abef8995177027bb747c2b66d5b670808795f77d107","request_id":"req_814134f462a743d5ba491b953eec504b","sent_at":"2026-10-07T05:51:24.171107+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-dedup","trial_id":"f1c0321190657c9505805fea7ac78ca73fdd9468f0275a1ebd9a759d384a55c5","usage":{"input_tokens":399,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":175,"output_tokens_details":{"reasoning_tokens":98},"total_tokens":574}},{"answer":"import heapq\n\ndef solve(data):\n    nodes = set(data)\n    for prerequisites in data.values():\n        nodes.update(prerequisites)\n\n    indegree = {name: 0 for name in nodes}\n    children = {name: [] for name in nodes}\n\n    for name, prerequisites in data.items():\n        for prerequisite in set(prerequisites):\n            indegree[name] += 1\n            children[prerequisite].append(name)\n\n    ready = [name for name in nodes if indegree[name] == 0]\n    heapq.heapify(ready)\n    order = []\n\n    while ready:\n        name = heapq.heappop(ready)\n        order.append(name)\n        for child in children[name]:\n            indegree[child] -= 1\n            if indegree[child] == 0:\n                heapq.heappush(ready, child)\n\n    return order if len(order) == len(nodes) else None","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.00031,"finished_at":"2026-10-07T05:47:43.573700+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true},{"case":6,"passed":true},{"case":7,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"c0acf4d01cd36ae5228ff629d4d1a0661d4393234a46bc808188ce5bf1e01f56","latency_seconds":5.264611,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is a dict mapping each named job to prerequisite names. Include names referenced only as prerequisites. Return the lexicographically smallest valid topological order, choosing the smallest ready name at every step. Return null for any cycle. Job and prerequisite names are strings; prerequisites are lists. Repeated names in a prerequisite list denote the same dependency. Referenced-only names have no prerequisites. A self-dependency is a cycle. Return Python None for a cycle.","raw_response_hash":"a29f7807f600878cf80d8c36a3b95f4797fb73826c7ab06bb2fe58aa4f22732b","repetition":1,"request_hash":"dab9307ba07b18d440ab146a523bc2ec013117e46ea82caf2d2bde4f0e8a237d","request_id":"req_555b7e07eb664b888bfcc99f5f0f679a","sent_at":"2026-10-07T05:47:38.309089+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-dependencies","trial_id":"f254c3bd7d90097c9b8935c211744e9de95e1c757bd006b054a0b4e9508f9a2b","usage":{"input_tokens":431,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":532,"output_tokens_details":{"reasoning_tokens":333},"total_tokens":963}},{"answer":"from heapq import heapify, heappop, heappush\n\ndef solve(data):\n    dependencies = {}\n    nodes = set(data)\n\n    for name, prerequisites in data.items():\n        unique_prerequisites = set(prerequisites)\n        dependencies[name] = unique_prerequisites\n        nodes.update(unique_prerequisites)\n\n    for name in nodes:\n        if name not in dependencies:\n            dependencies[name] = set()\n\n    outgoing = {name: [] for name in nodes}\n    indegree = {}\n\n    for name in nodes:\n        indegree[name] = len(dependencies[name])\n        for prerequisite in dependencies[name]:\n            outgoing[prerequisite].append(name)\n\n    ready = [name for name in nodes if indegree[name] == 0]\n    heapify(ready)\n    order = []\n\n    while ready:\n        name = heappop(ready)\n        order.append(name)\n        for dependent in outgoing[name]:\n            indegree[dependent] -= 1\n            if indegree[dependent] == 0:\n                heappush(ready, dependent)\n\n    return order if len(order) == len(nodes) else None","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.00044,"finished_at":"2026-10-07T05:49:36.062900+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true},{"case":6,"passed":true},{"case":7,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"c0acf4d01cd36ae5228ff629d4d1a0661d4393234a46bc808188ce5bf1e01f56","latency_seconds":6.95543,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is a dict mapping each named job to prerequisite names. Include names referenced only as prerequisites. Return the lexicographically smallest valid topological order, choosing the smallest ready name at every step. Return null for any cycle. Job and prerequisite names are strings; prerequisites are lists. Repeated names in a prerequisite list denote the same dependency. Referenced-only names have no prerequisites. A self-dependency is a cycle. Return Python None for a cycle.","raw_response_hash":"3f9f85e7084cef739f85ab62ca029a37a890b918213b4a21101efd605787ad30","repetition":2,"request_hash":"dab9307ba07b18d440ab146a523bc2ec013117e46ea82caf2d2bde4f0e8a237d","request_id":"req_7df49ca5b59d4a6eb040e2159b89da35","sent_at":"2026-10-07T05:49:29.107470+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-dependencies","trial_id":"b27448bdf9340eca06762376a2a01811573c2da2007f2e7a1b7a89f554df7341","usage":{"input_tokens":431,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":792,"output_tokens_details":{"reasoning_tokens":552},"total_tokens":1223}},{"answer":"import heapq\nfrom collections import defaultdict\n\ndef solve(data):\n    names = set(data)\n    for prerequisites in data.values():\n        names.update(prerequisites)\n\n    indegree = {name: 0 for name in names}\n    dependents = defaultdict(list)\n\n    for job, prerequisites in data.items():\n        unique_prerequisites = set(prerequisites)\n        indegree[job] = len(unique_prerequisites)\n        for prerequisite in unique_prerequisites:\n            dependents[prerequisite].append(job)\n\n    ready = [name for name in names if indegree[name] == 0]\n    heapq.heapify(ready)\n\n    order = []\n    while ready:\n        name = heapq.heappop(ready)\n        order.append(name)\n        for job in dependents[name]:\n            indegree[job] -= 1\n            if indegree[job] == 0:\n                heapq.heappush(ready, job)\n\n    return order if len(order) == len(names) else None","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000325,"finished_at":"2026-10-07T05:51:47.021986+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true},{"case":6,"passed":true},{"case":7,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"c0acf4d01cd36ae5228ff629d4d1a0661d4393234a46bc808188ce5bf1e01f56","latency_seconds":5.106019,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is a dict mapping each named job to prerequisite names. Include names referenced only as prerequisites. Return the lexicographically smallest valid topological order, choosing the smallest ready name at every step. Return null for any cycle. Job and prerequisite names are strings; prerequisites are lists. Repeated names in a prerequisite list denote the same dependency. Referenced-only names have no prerequisites. A self-dependency is a cycle. Return Python None for a cycle.","raw_response_hash":"d6b62672242e0eb8dd9bb087b6e9d94f522b820b3792f94352440b6c165b1f8c","repetition":3,"request_hash":"dab9307ba07b18d440ab146a523bc2ec013117e46ea82caf2d2bde4f0e8a237d","request_id":"req_94f794a9ae794cfd9fb5a2f9eff1f1ee","sent_at":"2026-10-07T05:51:41.915967+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-dependencies","trial_id":"7be39ecaad52e970171f8f5a2ca55d1c7d80526c12dd26fd6825a036d55ddf8f","usage":{"input_tokens":431,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":562,"output_tokens_details":{"reasoning_tokens":346},"total_tokens":993}},{"answer":"def solve(data):\n    remaining_to_sell = data[\"sold\"]\n    remaining_units = 0\n    remaining_value_cents = 0\n\n    for quantity, unit_cents in data[\"lots\"]:\n        sold_from_lot = min(quantity, remaining_to_sell)\n        remaining_to_sell -= sold_from_lot\n        leftover = quantity - sold_from_lot\n        remaining_units += leftover\n        remaining_value_cents += leftover * unit_cents\n\n    return {\n        \"remaining_units\": remaining_units,\n        \"remaining_value_cents\": remaining_value_cents,\n        \"unfilled_units\": remaining_to_sell,\n    }","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000154,"finished_at":"2026-10-07T05:48:23.741253+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"78fadaee3ffc5f9a819d5f90dd5f010d5aaa14fdf7bc4e768b2c7bfab55ed8c6","latency_seconds":2.71502,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is {lots:[[quantity,unit_cents],...], sold:nonnegative integer}. Sell in lot order. Return {remaining_units,remaining_value_cents,unfilled_units}. All quantities/prices nonnegative; unfilled is sales beyond available inventory. Quantities and unit prices are integers.","raw_response_hash":"2997823c5a082b23e64e6eae090b00a9ee93afdc22cf35c1c461f6ad431f37e4","repetition":1,"request_hash":"5c6872fab8424fa7f72f6357de149b95751b2d85831eb56f32fe27fd575fa2f2","request_id":"req_7d49c1b3412849c3ad6833aef9441ec5","sent_at":"2026-10-07T05:48:21.026233+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-fifo","trial_id":"e67a256042040ac76cfbb9a0f949441cc2c1de7754e79c20d129586c1024b150","usage":{"input_tokens":401,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":227,"output_tokens_details":{"reasoning_tokens":94},"total_tokens":628}},{"answer":"def solve(data):\n    remaining_to_sell = data[\"sold\"]\n    remaining_units = 0\n    remaining_value_cents = 0\n\n    for quantity, unit_cents in data[\"lots\"]:\n        sold_from_lot = min(quantity, remaining_to_sell)\n        remaining_to_sell -= sold_from_lot\n        units_left = quantity - sold_from_lot\n        remaining_units += units_left\n        remaining_value_cents += units_left * unit_cents\n\n    return {\n        \"remaining_units\": remaining_units,\n        \"remaining_value_cents\": remaining_value_cents,\n        \"unfilled_units\": remaining_to_sell,\n    }","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000169,"finished_at":"2026-10-07T05:50:01.062401+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"78fadaee3ffc5f9a819d5f90dd5f010d5aaa14fdf7bc4e768b2c7bfab55ed8c6","latency_seconds":6.057629,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is {lots:[[quantity,unit_cents],...], sold:nonnegative integer}. Sell in lot order. Return {remaining_units,remaining_value_cents,unfilled_units}. All quantities/prices nonnegative; unfilled is sales beyond available inventory. Quantities and unit prices are integers.","raw_response_hash":"b868e820799b2a0e612ac7acdb522dedb24e6b16727894fc286353478424e855","repetition":2,"request_hash":"5c6872fab8424fa7f72f6357de149b95751b2d85831eb56f32fe27fd575fa2f2","request_id":"req_78c013bba5d04eb1b40ec4142551d0b4","sent_at":"2026-10-07T05:49:55.004772+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-fifo","trial_id":"020f96ec45a9a470cdd74d5bf792e1935bbaf034b859076e9a906e605e53587d","usage":{"input_tokens":401,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":257,"output_tokens_details":{"reasoning_tokens":121},"total_tokens":658}},{"answer":"def solve(data):\n    to_sell = data[\"sold\"]\n    remaining_units = 0\n    remaining_value_cents = 0\n\n    for quantity, unit_cents in data[\"lots\"]:\n        sold_from_lot = min(quantity, to_sell)\n        to_sell -= sold_from_lot\n        left = quantity - sold_from_lot\n        remaining_units += left\n        remaining_value_cents += left * unit_cents\n\n    return {\n        \"remaining_units\": remaining_units,\n        \"remaining_value_cents\": remaining_value_cents,\n        \"unfilled_units\": to_sell,\n    }","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000171,"finished_at":"2026-10-07T05:50:19.657387+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"78fadaee3ffc5f9a819d5f90dd5f010d5aaa14fdf7bc4e768b2c7bfab55ed8c6","latency_seconds":3.461882,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is {lots:[[quantity,unit_cents],...], sold:nonnegative integer}. Sell in lot order. Return {remaining_units,remaining_value_cents,unfilled_units}. All quantities/prices nonnegative; unfilled is sales beyond available inventory. Quantities and unit prices are integers.","raw_response_hash":"05f67b76968d5a522d987b6d1c48775a758b4384a613cbd6a5a78d975c81e784","repetition":3,"request_hash":"5c6872fab8424fa7f72f6357de149b95751b2d85831eb56f32fe27fd575fa2f2","request_id":"req_ccc0dbb86db14b6497a3411de2e4f99c","sent_at":"2026-10-07T05:50:16.195505+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-fifo","trial_id":"5fc8cc0e8ec89c0867181e359127657162d21872e7e0755d9096c5c1741ee141","usage":{"input_tokens":401,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":260,"output_tokens_details":{"reasoning_tokens":131},"total_tokens":661}},{"answer":"def solve(data):\n    intervals = sorted((start, end) for start, end in data if start < end)\n    merged = []\n    for start, end in intervals:\n        if merged and start <= merged[-1][1]:\n            if end > merged[-1][1]:\n                merged[-1][1] = end\n        else:\n            merged.append([start, end])\n    return merged","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000133,"finished_at":"2026-10-07T05:48:02.156500+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"4aec54ac4c6e4155595065b2ec3b1ff67f8bacfca22078e5a6400b8016d3c0a8","latency_seconds":3.159504,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is a list of [start,end] integer half-open intervals, with start<=end. Drop empty intervals; merge overlapping or touching intervals. Return sorted merged intervals without mutating input.","raw_response_hash":"3ce6ed89e17ef624f2e46aaab9d822f261ba29296c87738e6de747e6c3dc28e4","repetition":1,"request_hash":"d705889a0597db89c942a9940d399b0baee309a7a9e8029f517426b30c0fbd29","request_id":"req_21e8cb92163f4ac99c9ca2ba3aa4e039","sent_at":"2026-10-07T05:47:58.996996+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-intervals","trial_id":"514db1948af51911869c89553466d01727db080243bb4fd9fbd3a59a1dc52e1a","usage":{"input_tokens":378,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":189,"output_tokens_details":{"reasoning_tokens":101},"total_tokens":567}},{"answer":"def solve(data):\n    intervals = sorted((start, end) for start, end in data if start < end)\n    merged = []\n    for start, end in intervals:\n        if merged and start <= merged[-1][1]:\n            merged[-1][1] = max(merged[-1][1], end)\n        else:\n            merged.append([start, end])\n    return merged","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000115,"finished_at":"2026-10-07T05:49:42.282662+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"4aec54ac4c6e4155595065b2ec3b1ff67f8bacfca22078e5a6400b8016d3c0a8","latency_seconds":5.857295,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is a list of [start,end] integer half-open intervals, with start<=end. Drop empty intervals; merge overlapping or touching intervals. Return sorted merged intervals without mutating input.","raw_response_hash":"759c406774a41d5293ce50510c64f33ce8c16fa6aa1777b14b08a93312b88560","repetition":2,"request_hash":"d705889a0597db89c942a9940d399b0baee309a7a9e8029f517426b30c0fbd29","request_id":"req_f7852bde5b3744979d9eac7e7ad2c55e","sent_at":"2026-10-07T05:49:36.425367+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-intervals","trial_id":"9ca2bdae6968adb909433aab2472160293d123e87ad281709064d5362f4ece36","usage":{"input_tokens":378,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":154,"output_tokens_details":{"reasoning_tokens":68},"total_tokens":532}},{"answer":"def solve(data):\n    intervals = sorted((start, end) for start, end in data if start < end)\n    merged = []\n    for start, end in intervals:\n        if merged and start <= merged[-1][1]:\n            merged[-1][1] = max(merged[-1][1], end)\n        else:\n            merged.append([start, end])\n    return merged","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000118,"finished_at":"2026-10-07T05:50:22.361945+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"4aec54ac4c6e4155595065b2ec3b1ff67f8bacfca22078e5a6400b8016d3c0a8","latency_seconds":2.356215,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is a list of [start,end] integer half-open intervals, with start<=end. Drop empty intervals; merge overlapping or touching intervals. Return sorted merged intervals without mutating input.","raw_response_hash":"0df3bd1af0c3860e249d792446d9b02ac91644d6cd8c6b7f4d13ec07aea55ae2","repetition":3,"request_hash":"d705889a0597db89c942a9940d399b0baee309a7a9e8029f517426b30c0fbd29","request_id":"req_439ca793f8af48a591e56458d8535bd5","sent_at":"2026-10-07T05:50:20.005730+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-intervals","trial_id":"f4f914b9001c0e24845c681bc1e55d36e6993b3a98c623155aabac70bc383b50","usage":{"input_tokens":378,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":160,"output_tokens_details":{"reasoning_tokens":74},"total_tokens":538}},{"answer":"import math\n\ndef solve(data):\n    values = data[\"values\"]\n    if not values:\n        return None\n    ordered = sorted(values)\n    rank = max(1, math.ceil(data[\"p\"] / 100 * len(ordered)))\n    return ordered[rank - 1]","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000106,"finished_at":"2026-10-07T05:46:23.564149+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true},{"case":6,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"839970ac4cfb85175031c9224c924157d559f9fa5b058e59cc07db856bfe68dd","latency_seconds":2.817104,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is {values:[finite numbers,...],p:number in [0,100]}. Return nearest-rank percentile: sorted values at 1-based rank max(1,ceil(p/100*n)). Return null for no values. Do not interpolate or mutate input.","raw_response_hash":"205d7cf27b3a7e2ab784a1ff724d2fc64a6a1de1fc7d9e949e1b392dbdbad9e0","repetition":1,"request_hash":"125e03d025ac1b27d3aa8ca71a5761086a2bb0275ca5a09987949ddb5fa6b9c3","request_id":"req_5533dcc86c3d440a8ff82fed95ef0269","sent_at":"2026-10-07T05:46:20.747045+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-percentile","trial_id":"02e90d042b058b2a8601f1ed2b2a6363d7542e40d40ec4ddeae23806883c5218","usage":{"input_tokens":394,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":133,"output_tokens_details":{"reasoning_tokens":68},"total_tokens":527}},{"answer":"import math\n\ndef solve(data):\n    values = data[\"values\"]\n    if not values:\n        return None\n    ordered = sorted(values)\n    rank = max(1, math.ceil(data[\"p\"] / 100 * len(ordered)))\n    return ordered[rank - 1]","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.00011,"finished_at":"2026-10-07T05:49:22.014390+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true},{"case":6,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"839970ac4cfb85175031c9224c924157d559f9fa5b058e59cc07db856bfe68dd","latency_seconds":2.183306,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is {values:[finite numbers,...],p:number in [0,100]}. Return nearest-rank percentile: sorted values at 1-based rank max(1,ceil(p/100*n)). Return null for no values. Do not interpolate or mutate input.","raw_response_hash":"21cfcad97386251b44bca18992835b57cc9eefc217f35044baf59f44fdd9049b","repetition":2,"request_hash":"125e03d025ac1b27d3aa8ca71a5761086a2bb0275ca5a09987949ddb5fa6b9c3","request_id":"req_ca4084bdcade4af08f7509b8b90823a2","sent_at":"2026-10-07T05:49:19.831084+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-percentile","trial_id":"0f4345a965eea465b4b1df47a96994fb392be82e75f2145dc13d764fc98d2e5d","usage":{"input_tokens":394,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":141,"output_tokens_details":{"reasoning_tokens":76},"total_tokens":535}},{"answer":"import math\n\ndef solve(data):\n    values = data[\"values\"]\n    if not values:\n        return None\n    rank = max(1, math.ceil(data[\"p\"] / 100 * len(values)))\n    return sorted(values)[rank - 1]","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000113,"finished_at":"2026-10-07T05:51:11.648344+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true},{"case":6,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"839970ac4cfb85175031c9224c924157d559f9fa5b058e59cc07db856bfe68dd","latency_seconds":2.033383,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is {values:[finite numbers,...],p:number in [0,100]}. Return nearest-rank percentile: sorted values at 1-based rank max(1,ceil(p/100*n)). Return null for no values. Do not interpolate or mutate input.","raw_response_hash":"28351de6ec2a153b0223dd0facbd23dbd12c0bb9d40d5dd3c521a0facac82d87","repetition":3,"request_hash":"125e03d025ac1b27d3aa8ca71a5761086a2bb0275ca5a09987949ddb5fa6b9c3","request_id":"req_f18a24be8950487d968abf10cf88178c","sent_at":"2026-10-07T05:51:09.614961+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-percentile","trial_id":"222ccfc9e2a133d08f86ad0f44fc3f002d7546aeab9b55a263986d94b8213ddf","usage":{"input_tokens":394,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":146,"output_tokens_details":{"reasoning_tokens":87},"total_tokens":540}},{"answer":"from collections import deque\n\ndef solve(data):\n    accepted = {}\n    result = []\n    limit = data[\"limit\"]\n    window = data[\"window\"]\n\n    for event in data[\"events\"]:\n        user = event[\"user\"]\n        time = event[\"time\"]\n        times = accepted.get(user)\n        if times is None:\n            times = deque()\n            accepted[user] = times\n\n        while times and times[0] <= time - window:\n            times.popleft()\n\n        if len(times) < limit:\n            result.append(True)\n            times.append(time)\n        else:\n            result.append(False)\n\n    return result","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000137,"finished_at":"2026-10-07T05:46:50.998556+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"533a0d4b7de63a201cf0cb2808b07b2933e5c1ac268098215b30c55a85eb6151","latency_seconds":3.400036,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is {limit:positive integer,window:positive integer,events:[{user,time},...]}. Times are nondecreasing integer seconds. Process in input order. Accept only when fewer than limit previously accepted events for that user have times strictly greater than time-window. Rejections do not consume allowance. Return a list of booleans. User IDs are strings.","raw_response_hash":"4db503487964cc6cb1a582622e8dbc58d9782de3655ab210671006a38f546625","repetition":1,"request_hash":"d92b40a92d01fcb599b0e9db6465861b553ba0cafc89897be7eb98baf4924028","request_id":"req_96c3c897080f4eafaaf26217ef4df576","sent_at":"2026-10-07T05:46:47.598520+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-rate","trial_id":"d800362c95939698f884a6e77670b71e652ec46f3e29fecbcd69ceba78754e87","usage":{"input_tokens":414,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":191,"output_tokens_details":{"reasoning_tokens":60},"total_tokens":605}},{"answer":"from collections import defaultdict, deque\n\ndef solve(data):\n    limit = data[\"limit\"]\n    window = data[\"window\"]\n    accepted = defaultdict(deque)\n    result = []\n\n    for event in data[\"events\"]:\n        user = event[\"user\"]\n        time = event[\"time\"]\n        times = accepted[user]\n\n        while times and times[0] <= time - window:\n            times.popleft()\n\n        if len(times) < limit:\n            result.append(True)\n            times.append(time)\n        else:\n            result.append(False)\n\n    return result","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000154,"finished_at":"2026-10-07T05:48:58.548527+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"533a0d4b7de63a201cf0cb2808b07b2933e5c1ac268098215b30c55a85eb6151","latency_seconds":2.898199,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is {limit:positive integer,window:positive integer,events:[{user,time},...]}. Times are nondecreasing integer seconds. Process in input order. Accept only when fewer than limit previously accepted events for that user have times strictly greater than time-window. Rejections do not consume allowance. Return a list of booleans. User IDs are strings.","raw_response_hash":"e53f04869b41ff953ad36c6bc6a7c82031306e1f07f7f66bc7ec4e92ee73e039","repetition":2,"request_hash":"d92b40a92d01fcb599b0e9db6465861b553ba0cafc89897be7eb98baf4924028","request_id":"req_0da481f345ba4664b54ed3b461a560ed","sent_at":"2026-10-07T05:48:55.650328+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-rate","trial_id":"d259e30b6e3ad72f0a64ffdb4fe4594eda8ffb822392b3651965a26be16254eb","usage":{"input_tokens":414,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":225,"output_tokens_details":{"reasoning_tokens":108},"total_tokens":639}},{"answer":"from collections import defaultdict, deque\n\ndef solve(data):\n    accepted = defaultdict(deque)\n    result = []\n\n    for event in data[\"events\"]:\n        user = event[\"user\"]\n        time = event[\"time\"]\n        history = accepted[user]\n        cutoff = time - data[\"window\"]\n\n        while history and history[0] <= cutoff:\n            history.popleft()\n\n        if len(history) < data[\"limit\"]:\n            result.append(True)\n            history.append(time)\n        else:\n            result.append(False)\n\n    return result","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000191,"finished_at":"2026-10-07T05:51:31.613892+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"533a0d4b7de63a201cf0cb2808b07b2933e5c1ac268098215b30c55a85eb6151","latency_seconds":3.454594,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is {limit:positive integer,window:positive integer,events:[{user,time},...]}. Times are nondecreasing integer seconds. Process in input order. Accept only when fewer than limit previously accepted events for that user have times strictly greater than time-window. Rejections do not consume allowance. Return a list of booleans. User IDs are strings.","raw_response_hash":"fab4707f9b632c27f2c1256bea4565da5d9163ae2adb6c8d06e8f11d75b3ccf6","repetition":3,"request_hash":"d92b40a92d01fcb599b0e9db6465861b553ba0cafc89897be7eb98baf4924028","request_id":"req_c087b91b69dd4929ae98b3a4565faa0b","sent_at":"2026-10-07T05:51:28.159298+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-rate","trial_id":"f89862ebeee6df754fff4827abe0a37f8b5c2099b3b2df8a041b4c55aced9254","usage":{"input_tokens":414,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":299,"output_tokens_details":{"reasoning_tokens":187},"total_tokens":713}},{"answer":"def solve(data):\n    balances = {}\n    for invoice in data[\"invoices\"]:\n        ident = invoice[\"id\"]\n        if ident not in balances:\n            balances[ident] = 0\n        balances[ident] += invoice[\"cents\"]\n\n    for payment in data[\"payments\"]:\n        ident = payment[\"id\"]\n        if ident not in balances:\n            balances[ident] = 0\n        balances[ident] -= payment[\"cents\"]\n\n    return {ident: balances[ident] for ident in sorted(balances) if balances[ident] != 0}","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000197,"finished_at":"2026-10-07T05:47:38.237089+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"5bb2ddfd9af3a43eb58562d1612d3c8489f2861a0b6553f540e7f26e1a3d6a9d","latency_seconds":3.788555,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input has invoices and payments lists of {id, cents}. Sum amounts by id; payments subtract from invoices. Return a dict of nonzero outstanding amounts with dictionary iteration keys sorted lexicographically by id, including negative overpayments. IDs are strings. Empty lists are valid. Amounts in cents are integers.","raw_response_hash":"10303b6df9dc78d31c8c8b3973f80369871b3d0f793a611806a7eab403944b31","repetition":1,"request_hash":"2849bcb02daa1db72ecfabf2b052e4e754e288c85d208034ae2c7b86f62c5358","request_id":"req_c251ed5e596747d28cf5e3307c0c90a5","sent_at":"2026-10-07T05:47:34.448534+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-reconcile","trial_id":"a12cef858d316ce7e30d04d071e21d52791e79fdef0c3a598d47b54368145127","usage":{"input_tokens":402,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":312,"output_tokens_details":{"reasoning_tokens":187},"total_tokens":714}},{"answer":"def solve(data):\n    balances = {}\n    for invoice in data[\"invoices\"]:\n        invoice_id = invoice[\"id\"]\n        balances[invoice_id] = balances.get(invoice_id, 0) + invoice[\"cents\"]\n    for payment in data[\"payments\"]:\n        payment_id = payment[\"id\"]\n        balances[payment_id] = balances.get(payment_id, 0) - payment[\"cents\"]\n    return {invoice_id: balances[invoice_id] for invoice_id in sorted(balances) if balances[invoice_id] != 0}","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000164,"finished_at":"2026-10-07T05:49:45.483119+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"5bb2ddfd9af3a43eb58562d1612d3c8489f2861a0b6553f540e7f26e1a3d6a9d","latency_seconds":3.136625,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input has invoices and payments lists of {id, cents}. Sum amounts by id; payments subtract from invoices. Return a dict of nonzero outstanding amounts with dictionary iteration keys sorted lexicographically by id, including negative overpayments. IDs are strings. Empty lists are valid. Amounts in cents are integers.","raw_response_hash":"316ac3b292a6e227969d15b4ad26dee08f33bba73fc4d27e40b584c165075682","repetition":2,"request_hash":"2849bcb02daa1db72ecfabf2b052e4e754e288c85d208034ae2c7b86f62c5358","request_id":"req_90d2ba73156c42a5a0d4beb8012d6f91","sent_at":"2026-10-07T05:49:42.346494+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-reconcile","trial_id":"05974b4c9ef1169c2126e03d223364067e628f529c239b1fa5da393b7858d0a3","usage":{"input_tokens":402,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":247,"output_tokens_details":{"reasoning_tokens":128},"total_tokens":649}},{"answer":"def solve(data):\n    totals = {}\n    for invoice in data[\"invoices\"]:\n        key = invoice[\"id\"]\n        totals[key] = totals.get(key, 0) + invoice[\"cents\"]\n    for payment in data[\"payments\"]:\n        key = payment[\"id\"]\n        totals[key] = totals.get(key, 0) - payment[\"cents\"]\n    return {key: totals[key] for key in sorted(totals) if totals[key] != 0}","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000121,"finished_at":"2026-10-07T05:51:00.158875+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"5bb2ddfd9af3a43eb58562d1612d3c8489f2861a0b6553f540e7f26e1a3d6a9d","latency_seconds":2.299353,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input has invoices and payments lists of {id, cents}. Sum amounts by id; payments subtract from invoices. Return a dict of nonzero outstanding amounts with dictionary iteration keys sorted lexicographically by id, including negative overpayments. IDs are strings. Empty lists are valid. Amounts in cents are integers.","raw_response_hash":"094a279ad6c0dafd70322785a3847d63d2e2c3b7b5c0ec13b78dfa58d6b062cc","repetition":3,"request_hash":"2849bcb02daa1db72ecfabf2b052e4e754e288c85d208034ae2c7b86f62c5358","request_id":"req_f0c2035a12c942ddb320b414f0110c87","sent_at":"2026-10-07T05:50:57.859522+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-reconcile","trial_id":"f566b2ef6d23984b42e8a952933f23d05912e35e99aa04e686dbad0b5ac5042a","usage":{"input_tokens":402,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":160,"output_tokens_details":{"reasoning_tokens":55},"total_tokens":562}},{"answer":"def solve(data):\n    def clean(value):\n        if isinstance(value, dict):\n            result = {}\n            for key, item in value.items():\n                if key.lower() not in (\"password\", \"token\", \"secret\"):\n                    result[key] = clean(item)\n            return result\n        if isinstance(value, list):\n            return [clean(item) for item in value]\n        return value\n\n    return clean(data)","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.0002,"finished_at":"2026-10-07T05:46:47.261914+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"5fe2879269711526d2a2b021b2812953952522fa10952716f0dd5df910e467d6","latency_seconds":3.989014,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is arbitrary JSON. Recursively remove dictionary keys whose lowercase spelling equals password, token or secret. Preserve other values and list order; never mutate the original.","raw_response_hash":"4e549860b9e48324688f17da0024bcb2979195634b992ab8dfa52b719bc8a1d6","repetition":1,"request_hash":"be1d52f9c4f9fb09f25aebe2e20547435a3c1bacab43085ac4ed026bf42262b0","request_id":"req_8d4245358624414f852e517903fad67b","sent_at":"2026-10-07T05:46:43.272900+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-redact","trial_id":"44017b585fafbd513e37e14007343deca53f314f675fe33d05cd89c0ae894114","usage":{"input_tokens":372,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":325,"output_tokens_details":{"reasoning_tokens":235},"total_tokens":697}},{"answer":"def solve(data):\n    def clean(value):\n        if isinstance(value, dict):\n            return {\n                key: clean(item)\n                for key, item in value.items()\n                if key.lower() not in (\"password\", \"token\", \"secret\")\n            }\n        if isinstance(value, list):\n            return [clean(item) for item in value]\n        return value\n\n    return clean(data)","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000225,"finished_at":"2026-10-07T05:49:27.559325+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"5fe2879269711526d2a2b021b2812953952522fa10952716f0dd5df910e467d6","latency_seconds":5.452301,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is arbitrary JSON. Recursively remove dictionary keys whose lowercase spelling equals password, token or secret. Preserve other values and list order; never mutate the original.","raw_response_hash":"f724b7adedebe083534ac583c91948d318054259c4263d01c41ee07bd5e292a5","repetition":2,"request_hash":"be1d52f9c4f9fb09f25aebe2e20547435a3c1bacab43085ac4ed026bf42262b0","request_id":"req_01fbd0851da64a109739c5ed1e3d5b01","sent_at":"2026-10-07T05:49:22.107024+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-redact","trial_id":"2c1b1c57a76c95fbbb9d50dcacd2ba92df5ba03b8c17b88a2e33e8442d478d14","usage":{"input_tokens":372,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":374,"output_tokens_details":{"reasoning_tokens":289},"total_tokens":746}},{"answer":"def solve(data):\n    blocked = (\"password\", \"token\", \"secret\")\n\n    def clean(value):\n        if isinstance(value, dict):\n            return {\n                key: clean(item)\n                for key, item in value.items()\n                if key.lower() not in blocked\n            }\n        if isinstance(value, list):\n            return [clean(item) for item in value]\n        return value\n\n    return clean(data)","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000199,"finished_at":"2026-10-07T05:51:56.224391+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"5fe2879269711526d2a2b021b2812953952522fa10952716f0dd5df910e467d6","latency_seconds":3.431746,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is arbitrary JSON. Recursively remove dictionary keys whose lowercase spelling equals password, token or secret. Preserve other values and list order; never mutate the original.","raw_response_hash":"87242cf25936f10e9000ccd19f5e58dce1a9fbd4df75400b1c12eda8247f7fbb","repetition":3,"request_hash":"be1d52f9c4f9fb09f25aebe2e20547435a3c1bacab43085ac4ed026bf42262b0","request_id":"req_e687de1a2bdd4f7585249698c056ca3e","sent_at":"2026-10-07T05:51:52.792645+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-redact","trial_id":"61960ddb3f381db87f41577102becaf8ebb9b1745bf744c082e2eb3b26aed0e3","usage":{"input_tokens":372,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":322,"output_tokens_details":{"reasoning_tokens":232},"total_tokens":694}},{"answer":"def solve(data):\n    totals = {}\n    for day, count in data[\"events\"]:\n        totals[day] = totals.get(day, 0) + count\n\n    return [\n        sum(totals.get(day, 0) for day in range(query - 6, query + 1))\n        for query in data[\"query_days\"]\n    ]","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000161,"finished_at":"2026-10-07T05:48:20.944164+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"6d90993932f5f35b4118b5f7ebe1ddae6ba6f7a3bef4a5460375483928cd217c","latency_seconds":3.432053,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is {events:[[day,count],...], query_days:[integer,...]}. Return totals for each query day covering inclusive days q-6 through q. Events can be unsorted, repeated or negative-day; count is nonnegative. Query order must be preserved. Days, query days and counts are integers.","raw_response_hash":"8eb80e2dcab2e9f7444a0fb41f8e5cc7cafe6f19384d66c2b4b495d47cdbdf7d","repetition":1,"request_hash":"b51c06ad07e39aa6aedcf0f5d7827134539c569464f00b37d9444a9e2965ba1f","request_id":"req_6ff7de70a1424851a186b1ff2e6f4e3b","sent_at":"2026-10-07T05:48:17.512111+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-rolling","trial_id":"05472f76aa8bf669d495a1ebdd3e52422025e3bf80ca37f2d56e55ffeb657449","usage":{"input_tokens":403,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":240,"output_tokens_details":{"reasoning_tokens":161},"total_tokens":643}},{"answer":"def solve(data):\n    return [\n        sum(count for day, count in data[\"events\"] if query_day - 6 <= day <= query_day)\n        for query_day in data[\"query_days\"]\n    ]","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000147,"finished_at":"2026-10-07T05:48:51.421737+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"6d90993932f5f35b4118b5f7ebe1ddae6ba6f7a3bef4a5460375483928cd217c","latency_seconds":3.49467,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is {events:[[day,count],...], query_days:[integer,...]}. Return totals for each query day covering inclusive days q-6 through q. Events can be unsorted, repeated or negative-day; count is nonnegative. Query order must be preserved. Days, query days and counts are integers.","raw_response_hash":"8e7a1e85bf4df3fe4790d7f70efe82925385a2aa7cc569f18b09be7c55a67a66","repetition":2,"request_hash":"b51c06ad07e39aa6aedcf0f5d7827134539c569464f00b37d9444a9e2965ba1f","request_id":"req_d4570b39d2be49b1bed72738bd2d14d4","sent_at":"2026-10-07T05:48:47.927067+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-rolling","trial_id":"a50c8a5cc032b130707f707b47d48d8104e7c3663585e24ed7e9c2cc8586c965","usage":{"input_tokens":403,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":213,"output_tokens_details":{"reasoning_tokens":164},"total_tokens":616}},{"answer":"def solve(data):\n    totals = {}\n    for day, count in data[\"events\"]:\n        totals[day] = totals.get(day, 0) + count\n\n    days = sorted(totals)\n    prefix = [0]\n    for day in days:\n        prefix.append(prefix[-1] + totals[day])\n\n    result = []\n    for query in data[\"query_days\"]:\n        left_value = query - 6\n        low, high = 0, len(days)\n        while low < high:\n            middle = (low + high) // 2\n            if days[middle] < left_value:\n                low = middle + 1\n            else:\n                high = middle\n        left = low\n\n        low, high = 0, len(days)\n        while low < high:\n            middle = (low + high) // 2\n            if days[middle] <= query:\n                low = middle + 1\n            else:\n                high = middle\n        right = low\n        result.append(prefix[right] - prefix[left])\n\n    return result","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000372,"finished_at":"2026-10-07T05:50:31.634150+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"6d90993932f5f35b4118b5f7ebe1ddae6ba6f7a3bef4a5460375483928cd217c","latency_seconds":7.555833,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is {events:[[day,count],...], query_days:[integer,...]}. Return totals for each query day covering inclusive days q-6 through q. Events can be unsorted, repeated or negative-day; count is nonnegative. Query order must be preserved. Days, query days and counts are integers.","raw_response_hash":"8187c4d9f0057f92ef224fe0724669de97e6de6309ed49ffbe2aeaa69c1af94f","repetition":3,"request_hash":"b51c06ad07e39aa6aedcf0f5d7827134539c569464f00b37d9444a9e2965ba1f","request_id":"req_606a53720b83430d97a5c71befe512a3","sent_at":"2026-10-07T05:50:24.078317+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-rolling","trial_id":"035427801d7896b7636e5e95c9bd991e85f36e98223b73f2ac727ebbd1713f20","usage":{"input_tokens":403,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":663,"output_tokens_details":{"reasoning_tokens":437},"total_tokens":1066}},{"answer":"import unicodedata\n\ndef solve(data):\n    text = unicodedata.normalize(\"NFKD\", data).lower()\n    parts = []\n    separator = False\n\n    for char in text:\n        if unicodedata.category(char).startswith(\"M\"):\n            continue\n        if \"a\" <= char <= \"z\" or \"0\" <= char <= \"9\":\n            if separator and parts:\n                parts.append(\"-\")\n            parts.append(char)\n            separator = False\n        else:\n            separator = True\n\n    return \"\".join(parts)","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000271,"finished_at":"2026-10-07T05:47:58.913604+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true},{"case":6,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"0db7e277739efc0c22996e54593a845e7f226fb9e1655058d7aad651f513eea4","latency_seconds":4.971942,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is a string. Apply Unicode NFKD normalization, drop combining marks, lowercase, retain only ASCII a-z and digits, replace each run of other characters with one hyphen, strip edge hyphens. Return the slug.","raw_response_hash":"0268fe6e577097d8788e4eb297c459ea42df24247875e6195304cbd523857071","repetition":1,"request_hash":"02e9399b11df4400471887bab371a736ed19a47f5915ea13a5eec62ee00ebcf9","request_id":"req_d9d90dff7558451a8b13ac7989be0541","sent_at":"2026-10-07T05:47:53.941662+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-slug","trial_id":"68c943000beeea0ea8243869211aa49f0593f369814578bdd9496ba189038c99","usage":{"input_tokens":387,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":463,"output_tokens_details":{"reasoning_tokens":346},"total_tokens":850}},{"answer":"import unicodedata\n\ndef solve(data):\n    normalized = unicodedata.normalize(\"NFKD\", data)\n    text = \"\"\n    for char in normalized:\n        if unicodedata.category(char)[0] != \"M\":\n            text += char\n    text = text.lower()\n\n    slug = \"\"\n    for char in text:\n        if \"a\" <= char <= \"z\" or \"0\" <= char <= \"9\":\n            slug += char\n        elif slug and slug[-1] != \"-\":\n            slug += \"-\"\n\n    while slug and slug[0] == \"-\":\n        slug = slug[1:]\n    while slug and slug[-1] == \"-\":\n        slug = slug[:-1]\n    return slug","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000394,"finished_at":"2026-10-07T05:49:06.502401+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true},{"case":6,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"0db7e277739efc0c22996e54593a845e7f226fb9e1655058d7aad651f513eea4","latency_seconds":7.611454,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is a string. Apply Unicode NFKD normalization, drop combining marks, lowercase, retain only ASCII a-z and digits, replace each run of other characters with one hyphen, strip edge hyphens. Return the slug.","raw_response_hash":"011ddda80f5d971337e9c91ecf875033cd127b5c5bdc404a1ec24e9bd6185a42","repetition":2,"request_hash":"02e9399b11df4400471887bab371a736ed19a47f5915ea13a5eec62ee00ebcf9","request_id":"req_fe219d2547364a13adefa0d56e62c4e8","sent_at":"2026-10-07T05:48:58.890947+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-slug","trial_id":"7396d8c78b2e7426bbe0f3e828c4d3dcc3764a177046a774ffa0562867df714a","usage":{"input_tokens":387,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":709,"output_tokens_details":{"reasoning_tokens":554},"total_tokens":1096}},{"answer":"import unicodedata\n\ndef solve(data):\n    normalized = unicodedata.normalize(\"NFKD\", data)\n    result = []\n    pending_hyphen = False\n\n    for char in normalized:\n        if unicodedata.category(char)[0] == \"M\":\n            continue\n        for lowered in char.lower():\n            if \"a\" <= lowered <= \"z\" or \"0\" <= lowered <= \"9\":\n                if pending_hyphen:\n                    result.append(\"-\")\n                    pending_hyphen = False\n                result.append(lowered)\n            elif result:\n                pending_hyphen = True\n\n    return \"\".join(result)","billed_net_usd":null,"configuration_id":"196040a9d118928d840cb850729bb00f8afc85b85bdaa2fd3454bcfb02cf6367","enabled_tools":[],"estimated_net_usd":0.000439,"finished_at":"2026-10-07T05:51:41.584575+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true},{"case":6,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"0db7e277739efc0c22996e54593a845e7f226fb9e1655058d7aad651f513eea4","latency_seconds":9.906401,"model_id":"gpt-6-luna","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports are restricted module facades: math (public members); json (loads, dumps); csv (reader, DictReader); io (StringIO); re (compile, match, search, fullmatch, findall, finditer, split, sub, escape, IGNORECASE, MULTILINE, DOTALL, ASCII); collections (deque, defaultdict, Counter); heapq (heappush, heappop, heapify); unicodedata (normalize, combining, category). Other module members are unavailable. Use functions and only these safe built-ins: abs, all, any, bool, bytes, dict, divmod, enumerate, filter, float, frozenset, int, isinstance, len, list, map, max, min, next, ord, chr, pow, range, reversed, round, set, sorted, str, sum, tuple, zip, Exception, ValueError, TypeError, KeyError, IndexError, StopIteration. Import listed modules before referencing them. No custom classes, private/dunder attributes, globals/nonlocals, with statements, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Do not mutate any input, including nested containers. Return JSON-serializable values; Python None represents JSON null. Numeric strings and booleans are not accepted as numeric answers. Integer results are compared exactly; floating results use absolute tolerance 0.000001 or relative tolerance 0.00000001. Inputs follow the declared schema; no unspecified input validation is required. Input is a string. Apply Unicode NFKD normalization, drop combining marks, lowercase, retain only ASCII a-z and digits, replace each run of other characters with one hyphen, strip edge hyphens. Return the slug.","raw_response_hash":"ab211cb1c2ed39682013c944895a3851076c348a759a16c9f516c2e44a6dc6a9","repetition":3,"request_hash":"02e9399b11df4400471887bab371a736ed19a47f5915ea13a5eec62ee00ebcf9","request_id":"req_00c4341d9a90458796742d2c39649e7f","sent_at":"2026-10-07T05:51:31.678174+00:00","settings":{"effort":"medium","max_output_tokens":8192,"processing_profile":"standard","prompt_cache":"none","service_tier":"default","surface":"OpenAI direct API, Standard global","timeout_seconds":180},"state":"responded","task_id":"coding-slug","trial_id":"1b6f77e7983d7653467b0777385f0e21aa37bec8dea82b551d065e2a50202dbc","usage":{"input_tokens":387,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":800,"output_tokens_details":{"reasoning_tokens":664},"total_tokens":1187}}]}