{"as_of":"2026-08-08T22:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:54b182d6ce04266b639cb67218b92fef853d4c7de6aa448504e1ec096fcecff9","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T04:54:20.105337Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-29T14:33:31.671093Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.21199","last_updated":"2024-12-31T08:20:42Z","snapshot_observed_at":"2026-08-06T14:11:44.657165Z","submitted_at":"2024-12-30T18:58:58Z","title":"HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.21199","snapshot_observed_at":"2026-08-07T04:54:20.105337Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.09396","last_updated":"2025-06-11T04:55:00Z","snapshot_observed_at":"2026-08-07T04:46:52.638527Z","submitted_at":"2025-06-11T04:55:00Z","title":"Reasoning as a Resource: Optimizing Fast and Slow Thinking in Code Generation Models","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T04:54:20.105337Z"},"links":{"cited_paper":"/paper/2412.21199","citing_paper":"/paper/2506.09396"},"observation_digest":"sha256:8b4272e477ba950bfc9c0500e688122726d5b2e024012a587b2f3ffa9b80e501","observation_id":"c1dc0812-c573-420c-a324-23f2aca5ef02","resolution":{"observed_at":"2026-08-07T04:54:20.105337Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.21199","last_updated":"2024-12-31T08:20:42Z","snapshot_observed_at":"2026-08-06T14:11:44.657165Z","submitted_at":"2024-12-30T18:58:58Z","title":"HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.21199","snapshot_observed_at":"2026-08-07T00:59:57.050750Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.12278","last_updated":"2025-06-13T23:56:17Z","snapshot_observed_at":"2026-08-08T11:44:12.274478Z","submitted_at":"2025-06-13T23:56:17Z","title":"Can LLMs Generate High-Quality Test Cases for Algorithm Problems? TestCase-Eval: A Systematic Evaluation of Fault Coverage and Exposure","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-07T00:59:57.050750Z"},"links":{"cited_paper":"/paper/2412.21199","citing_paper":"/paper/2506.12278"},"observation_digest":"sha256:cc57a6086c18a70a24419cdd277df5b3927a7fc75d448a95b12256b3799f917d","observation_id":"87e3b6f0-bf66-4c79-a1bc-9e4cc155d542","resolution":{"observed_at":"2026-08-07T00:59:57.050750Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.21199","last_updated":"2024-12-31T08:20:42Z","snapshot_observed_at":"2026-08-06T14:11:44.657165Z","submitted_at":"2024-12-30T18:58:58Z","title":"HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.21199","snapshot_observed_at":"2026-08-03T23:43:15.114501Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.04355","last_updated":"2025-11-06T13:38:03Z","snapshot_observed_at":"2026-08-03T23:43:13.915869Z","submitted_at":"2025-11-06T13:38:03Z","title":"Where Do LLMs Still Struggle? An In-Depth Analysis of Code Generation Benchmarks","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-03T23:43:15.114501Z"},"links":{"cited_paper":"/paper/2412.21199","citing_paper":"/paper/2511.04355"},"observation_digest":"sha256:ffeb4e0284e4916307b8c144c792521622011141195a7ba2bc55d8969acb1ae3","observation_id":"3b213d7f-3344-4e7e-9e2a-b143a710c997","resolution":{"observed_at":"2026-08-03T23:43:15.114501Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.21199","last_updated":"2024-12-31T08:20:42Z","snapshot_observed_at":"2026-08-06T14:11:44.657165Z","submitted_at":"2024-12-30T18:58:58Z","title":"HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation","version":2},"cited_work":{"arxiv_id":"2412.21199","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.21199","snapshot_observed_at":"2026-06-29T14:33:31.671093Z","title":"Humaneval pro and mbpp pro: Evaluating large language models on self-invoking code generation","venue":null,"work_id":"f85900bb-7565-4bf9-a7a0-f05e36feb038","year":2025},"citing_paper":{"arxiv_id":"2604.16321","last_updated":"2026-02-25T07:55:49Z","snapshot_observed_at":"2026-08-08T17:56:41.856721Z","submitted_at":"2026-02-25T07:55:49Z","title":"LLM-Based Multi-Agent Systems for Code Generation: A Multi-Vocal Literature Review","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-15T19:52:49.324500Z"},"links":{"cited_paper":"/paper/2412.21199","citing_paper":"/paper/2604.16321"},"observation_digest":"sha256:784af004b2c51fb521bfc05dff012634f59c2e5a7bc9e464cef22f70882f025e","observation_id":"ce9a34e2-15bc-46cd-893c-47c92f4f07ed","resolution":{"observed_at":"2026-05-15T19:56:33.775143Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.21199","last_updated":"2024-12-31T08:20:42Z","snapshot_observed_at":"2026-08-06T14:11:44.657165Z","submitted_at":"2024-12-30T18:58:58Z","title":"HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation","version":2},"cited_work":{"arxiv_id":"2412.21199","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.21199","snapshot_observed_at":"2026-06-29T14:33:31.671093Z","title":"Humaneval pro and mbpp pro: Evaluating large language models on self-invoking code generation","venue":null,"work_id":"f85900bb-7565-4bf9-a7a0-f05e36feb038","year":2025},"citing_paper":{"arxiv_id":"2604.16646","last_updated":"2026-04-17T19:02:54Z","snapshot_observed_at":"2026-07-06T23:03:57.199731Z","submitted_at":"2026-04-17T19:02:54Z","title":"Agentic Frameworks for Reasoning Tasks: An Empirical Study","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-10T08:24:51.573913Z"},"links":{"cited_paper":"/paper/2412.21199","citing_paper":"/paper/2604.16646"},"observation_digest":"sha256:f01b819d117bae9bd5bace3af9fb97a47826cb508cf63fc2667ad3f2c68fae00","observation_id":"d81c616b-3c83-48c7-8893-5aa8fa3fb860","resolution":{"observed_at":"2026-05-10T09:08:26.182245Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.21199","last_updated":"2024-12-31T08:20:42Z","snapshot_observed_at":"2026-08-06T14:11:44.657165Z","submitted_at":"2024-12-30T18:58:58Z","title":"HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation","version":2},"cited_work":{"arxiv_id":"2412.21199","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.21199","snapshot_observed_at":"2026-06-29T14:33:31.671093Z","title":"Humaneval pro and mbpp pro: Evaluating large language models on self-invoking code generation","venue":null,"work_id":"f85900bb-7565-4bf9-a7a0-f05e36feb038","year":2025},"citing_paper":{"arxiv_id":"2604.27763","last_updated":"2026-04-30T11:52:50Z","snapshot_observed_at":"2026-07-06T23:13:11.623453Z","submitted_at":"2026-04-30T11:52:50Z","title":"Intent2Tx: Benchmarking LLMs for Translating Natural Language Intents into Ethereum Transactions","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-07T05:49:57.957009Z"},"links":{"cited_paper":"/paper/2412.21199","citing_paper":"/paper/2604.27763"},"observation_digest":"sha256:476bc3b606a1e6838fa337a2a4321c4a29162924babb9b1fc4e4409a1e12e068","observation_id":"ae7d3cc9-89ac-4a6d-bc98-253c89ec33fc","resolution":{"observed_at":"2026-05-12T10:31:29.331881Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.21199","last_updated":"2024-12-31T08:20:42Z","snapshot_observed_at":"2026-08-06T14:11:44.657165Z","submitted_at":"2024-12-30T18:58:58Z","title":"HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation","version":2},"cited_work":{"arxiv_id":"2412.21199","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.21199","snapshot_observed_at":"2026-06-29T14:33:31.671093Z","title":"Humaneval pro and mbpp pro: Evaluating large language models on self-invoking code generation","venue":null,"work_id":"f85900bb-7565-4bf9-a7a0-f05e36feb038","year":2025},"citing_paper":{"arxiv_id":"2605.08366","last_updated":"2026-05-08T18:21:44Z","snapshot_observed_at":"2026-07-06T23:20:38.385189Z","submitted_at":"2026-05-08T18:21:44Z","title":"SWE Atlas: Benchmarking Coding Agents Beyond Issue Resolution","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-12T02:28:07.557119Z"},"links":{"cited_paper":"/paper/2412.21199","citing_paper":"/paper/2605.08366"},"observation_digest":"sha256:5b413c274b5076ce8ea6e60771c0a38d02ba61056f86a8f3ff9c5fc4440d3fab","observation_id":"4524c26e-f802-4aa7-888b-6e408c19af54","resolution":{"observed_at":"2026-05-12T07:36:57.029369Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.21199","last_updated":"2024-12-31T08:20:42Z","snapshot_observed_at":"2026-08-06T14:11:44.657165Z","submitted_at":"2024-12-30T18:58:58Z","title":"HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation","version":2},"cited_work":{"arxiv_id":"2412.21199","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.21199","snapshot_observed_at":"2026-06-29T14:33:31.671093Z","title":"Humaneval pro and mbpp pro: Evaluating large language models on self-invoking code generation","venue":null,"work_id":"f85900bb-7565-4bf9-a7a0-f05e36feb038","year":2025},"citing_paper":{"arxiv_id":"2605.18073","last_updated":"2026-05-18T08:55:30Z","snapshot_observed_at":"2026-07-06T23:28:59.503300Z","submitted_at":"2026-05-18T08:55:30Z","title":"A-ProS: Towards Reliable Autonomous Programming Through Multi-Model Feedback","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-20T09:22:06.285118Z"},"links":{"cited_paper":"/paper/2412.21199","citing_paper":"/paper/2605.18073"},"observation_digest":"sha256:b91b355ed6a0c04753e6d22eed7caca262216ea157d6933e9d632d38918bdcec","observation_id":"60f2ccbc-e987-460d-a2fc-12697d43e592","resolution":{"observed_at":"2026-05-20T09:23:10.609159Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.21199","last_updated":"2024-12-31T08:20:42Z","snapshot_observed_at":"2026-08-06T14:11:44.657165Z","submitted_at":"2024-12-30T18:58:58Z","title":"HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation","version":2},"cited_work":{"arxiv_id":"2412.21199","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.21199","snapshot_observed_at":"2026-06-29T14:33:31.671093Z","title":"Humaneval pro and mbpp pro: Evaluating large language models on self-invoking code generation","venue":null,"work_id":"f85900bb-7565-4bf9-a7a0-f05e36feb038","year":2025},"citing_paper":{"arxiv_id":"2605.30394","last_updated":"2026-05-28T13:48:23Z","snapshot_observed_at":"2026-07-06T23:39:43.967889Z","submitted_at":"2026-05-28T13:48:23Z","title":"CodeGolf Bench: A Multi-Language Benchmark for Evaluating Concise Code Generation Capabilities of Large Language Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-29T06:25:56.404246Z"},"links":{"cited_paper":"/paper/2412.21199","citing_paper":"/paper/2605.30394"},"observation_digest":"sha256:afb54e7e061d8aa00c27ced68adafb5e65ec0803d849b64ed83de7f723fdf5ec","observation_id":"7d49d5d0-ece7-4be7-bb7e-e28556ea6543","resolution":{"observed_at":"2026-06-29T14:33:31.672803Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.21199","last_updated":"2024-12-31T08:20:42Z","snapshot_observed_at":"2026-08-06T14:11:44.657165Z","submitted_at":"2024-12-30T18:58:58Z","title":"HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.21199","snapshot_observed_at":"2026-07-12T04:43:45.592808Z","title":"arXiv preprint arXiv:2412.21199 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.03126","last_updated":"2026-07-07T03:50:51Z","snapshot_observed_at":"2026-08-02T17:17:56.686166Z","submitted_at":"2026-07-03T09:14:27Z","title":"ACPO: Adaptive Credit Policy Optimization via Fine-Grained Surrogate Entropy","version":2},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-07-12T04:43:45.592808Z"},"links":{"cited_paper":"/paper/2412.21199","citing_paper":"/paper/2607.03126"},"observation_digest":"sha256:085b425f6339c3aacc48a8a08cd12e8af05f0ee121bfbc78b5ec37c21bce2f6c","observation_id":"9e3c6695-b408-4083-a1cc-6c9803c17d38","resolution":{"observed_at":"2026-07-12T04:43:45.592808Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2412.21199/citation-record","integrity":"/paper/2412.21199/integrity","json":"/paper/2412.21199/citation-record.json","paper":"/paper/2412.21199"},"outbound":[],"paper":{"arxiv_id":"2412.21199","last_updated":"2024-12-31T08:20:42Z","latest_version":2,"primary_category":"cs.SE","snapshot_observed_at":"2026-08-06T14:11:44.657165Z","submitted_at":"2024-12-30T18:58:58Z","title":"HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2412.21199."}