{"as_of":"2026-08-08T14:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:11c850921ad245a907089363c7b7719a7469b2e3bf82de751dfa2e40849d9422","coverage":[{"denominator":34,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":34,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:45:44.110888Z","state":"measured"},{"denominator":34,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":34,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.23598/citation-record","integrity":"/paper/2505.23598/integrity","json":"/paper/2505.23598/citation-record.json","paper":"/paper/2505.23598"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2308.10620","last_updated":"2024-04-10T08:41:22Z","snapshot_observed_at":"2026-07-06T16:08:29.285279Z","submitted_at":"2023-08-21T10:37:49Z","title":"Large Language Models for Software Engineering: A Systematic Literature Review","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.10620","snapshot_observed_at":"2026-08-07T12:45:40.068254Z","title":"Large language models for software engineering: A systematic literature review,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:40.068254Z"},"links":{"cited_paper":"/paper/2308.10620","citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:d283359b9180726f917549b0951c55ff46ffec4b86c25c7957f8c494c4f97b8f","observation_id":"32113471-a8b7-4923-9850-9af0f4475126","resolution":{"observed_at":"2026-08-07T12:45:40.068254Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.14554","last_updated":"2024-12-27T10:17:12Z","snapshot_observed_at":"2026-08-05T04:45:40.605747Z","submitted_at":"2024-12-19T06:10:40Z","title":"The Current Challenges of Software Engineering in the Era of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.14554","snapshot_observed_at":"2026-08-07T12:45:40.184121Z","title":"The current challenges of software engineering in the era of large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:40.184121Z"},"links":{"cited_paper":"/paper/2412.14554","citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:4a46bed51bd4f2507d5263bc8baf0982265b4c315f1d09e24c3525ff746906c2","observation_id":"4f742216-4b78-4527-a46f-d1860c26499b","resolution":{"observed_at":"2026-08-07T12:45:40.184121Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.14655","last_updated":"2025-04-20T15:28:16Z","snapshot_observed_at":"2026-08-07T16:00:46.114758Z","submitted_at":"2025-04-20T15:28:16Z","title":"LeetCodeDataset: A Temporal Dataset for Robust Evaluation and Efficient Training of Code LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.14655","snapshot_observed_at":"2026-08-07T12:45:40.247629Z","title":"Leetcodedataset: A temporal dataset for robust evaluation and efficient training of code llms,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:40.247629Z"},"links":{"cited_paper":"/paper/2504.14655","citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:90e79655c9f9aace2d3950bd535153cfb0335215ef72915bef2f82bc8fa84879","observation_id":"c3ecf66b-b835-4348-9251-46c8cdb2961b","resolution":{"observed_at":"2026-08-07T12:45:40.247629Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:45:47.997103Z","title":"Math word problem solving on math leaderboard,","venue":null,"work_id":"569b8e1f-fc27-4495-a4c9-4250b54c7d91","year":2025},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:40.367337Z"},"links":{"citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:4a6c55592244114583fc12dd7ef224d08f2c6ded35c442160e683c814fd6bfb8","observation_id":"3f95a9a8-de2b-49fc-860b-37194df0a11e","resolution":{"observed_at":"2026-08-07T12:45:48.092206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:45:40.473842Z","title":"A performance study of llm-generated code on leetcode,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:40.473842Z"},"links":{"citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:e6b6324df055148901c134475e60db6b18238e39426c527f1eee49dfe2679919","observation_id":"3b1f3e53-1f7c-4506-944b-f059231ae39c","resolution":{"observed_at":"2026-08-07T12:45:40.473842Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.01716","last_updated":"2021-12-03T05:01:47Z","snapshot_observed_at":"2026-07-06T12:14:56.180425Z","submitted_at":"2021-12-03T05:01:47Z","title":"Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.01716","snapshot_observed_at":"2026-08-07T12:45:40.600743Z","title":"Reduced, reused and recycled: The life of a dataset in machine learning research,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:40.600743Z"},"links":{"cited_paper":"/paper/2112.01716","citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:0cfa945d43740b3fedea02527ae93c47840a879dcb98fb78b5300b87f7f9fec4","observation_id":"1745ef85-11e2-42cf-b5f8-97d61dd8a9e7","resolution":{"observed_at":"2026-08-07T12:45:40.600743Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:45:47.796739Z","title":"Leetcode dataset,","venue":null,"work_id":"8211248c-bf5d-4cb6-a768-b828b18a6b5d","year":2025},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:40.722941Z"},"links":{"citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:bb828fd657a82f786189ba488f243a7d17a8f87056b04b87a9e61f17c93c42bf","observation_id":"4ab20e1e-e862-4cfd-9f89-edea607bd9d5","resolution":{"observed_at":"2026-08-07T12:45:47.885113Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2103.03874","last_updated":"2021-11-08T21:30:18Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-03-05T18:59:39Z","title":"Measuring Mathematical Problem Solving With the MATH Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.03874","snapshot_observed_at":"2026-08-07T12:45:40.821937Z","title":"Measuring mathematical problem solving with the math dataset,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:40.821937Z"},"links":{"cited_paper":"/paper/2103.03874","citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:0636403926dc7d7bb4de197fa9d1f32aa5921c2cd97e5e2b7222f2ecce2fd4e7","observation_id":"ba28657e-6d91-4f2d-9d5f-655ba170c91b","resolution":{"observed_at":"2026-08-07T12:45:40.821937Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.10628","last_updated":"2023-10-16T17:51:29Z","snapshot_observed_at":"2026-07-06T16:34:04.003157Z","submitted_at":"2023-10-16T17:51:29Z","title":"Data Contamination Through the Lens of Time","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.10628","snapshot_observed_at":"2026-08-07T12:45:40.967359Z","title":"Data contamination through the lens of time,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:40.967359Z"},"links":{"cited_paper":"/paper/2310.10628","citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:cbe86f9eae76978c0040972874c293913c7ba16cf9f0f4a5a868494b7544a6cd","observation_id":"db0cddfe-a008-45a4-ae18-3d9528a862a4","resolution":{"observed_at":"2026-08-07T12:45:40.967359Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.14337","last_updated":"2021-04-07T17:49:17Z","snapshot_observed_at":"2026-08-03T23:24:34.241550Z","submitted_at":"2021-04-07T17:49:17Z","title":"Dynabench: Rethinking Benchmarking in NLP","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.14337","snapshot_observed_at":"2026-08-07T12:45:41.067226Z","title":"Dynabench: Rethinking benchmarking in nlp,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:41.067226Z"},"links":{"cited_paper":"/paper/2104.14337","citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:5eabd2e7b35c22c38a0425b7b6243fafd01435d06c30d9fec3806af25204ddd1","observation_id":"dc4387f9-e218-4af0-a674-4f10df6c376e","resolution":{"observed_at":"2026-08-07T12:45:41.067226Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.09927","last_updated":"2024-12-08T19:15:26Z","snapshot_observed_at":"2026-08-03T19:23:12.561275Z","submitted_at":"2024-09-16T02:04:33Z","title":"Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.09927","snapshot_observed_at":"2026-08-07T12:45:41.204856Z","title":"Towards data contamination detection for modern large language models: Limitations, inconsistencies, and oracle challenges,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:41.204856Z"},"links":{"cited_paper":"/paper/2409.09927","citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:7d6aeee066f1c2a4ba87c806853e3c894b2b0c5c30672b04b539b527c07c996c","observation_id":"78c3158d-32db-4027-9bb4-19bb3fdb2eed","resolution":{"observed_at":"2026-08-07T12:45:41.204856Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00699","last_updated":"2025-07-09T19:56:02Z","snapshot_observed_at":"2026-07-06T17:53:40.016965Z","submitted_at":"2024-03-31T14:32:02Z","title":"A Comprehensive Survey of Contamination Detection Methods in Large Language Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.00699","snapshot_observed_at":"2026-08-07T12:45:41.334513Z","title":"A comprehensive survey of contamination detection methods in large language models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:41.334513Z"},"links":{"cited_paper":"/paper/2404.00699","citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:5fd413cd15190211fe7e30612af1a8f559200af9dbd8c57d779ee7368653d558","observation_id":"d4b4295c-012c-4306-a64f-9614f6f4ede3","resolution":{"observed_at":"2026-08-07T12:45:41.334513Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.09754","last_updated":"2024-10-03T02:19:14Z","snapshot_observed_at":"2026-07-06T18:00:21.200922Z","submitted_at":"2024-04-15T12:55:08Z","title":"Resilience of Large Language Models for Noisy Instructions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.09754","snapshot_observed_at":"2026-08-07T12:45:41.427522Z","title":"Resilience of large language models for noisy instructions,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:41.427522Z"},"links":{"cited_paper":"/paper/2404.09754","citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:5998f72f5e204a3cafe380d91bcdeb1cafb4ff3d49ceec6f170a03fa1e19f96c","observation_id":"2b258087-4b1d-4172-b964-beb4201d1fb9","resolution":{"observed_at":"2026-08-07T12:45:41.427522Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:45:41.565070Z","title":"Measuring massive multitask language understanding,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:41.565070Z"},"links":{"citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:00e5fa33333d3f25377ea7cfa7809021229294ef8bb08e606b4aaf9250604e51","observation_id":"14ec85f6-726c-4c66-92e6-cf86351984fe","resolution":{"observed_at":"2026-08-07T12:45:41.565070Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:45:47.532029Z","title":"Impact of noise on llm-models performance in abstraction and reasoning corpus (arc) tasks with model temperature considerations,","venue":null,"work_id":"78e0992e-ea6b-4785-8c8f-7063962ba523","year":null},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:41.803492Z"},"links":{"citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:ae634876aa10249190f7ffe740484f92a73cb36a8bb830c84910d041f00fc35c","observation_id":"7ff095dd-c6d6-4c15-9c27-6d4ea8b62f07","resolution":{"observed_at":"2026-08-07T12:45:47.643527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1803.05457","last_updated":"2018-03-14T18:04:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2018-03-14T18:04:21Z","title":"Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.05457","snapshot_observed_at":"2026-08-07T12:45:42.040015Z","title":"Think you have solved question answering? try arc, the ai2 reasoning challenge,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:42.040015Z"},"links":{"cited_paper":"/paper/1803.05457","citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:04eb5b8383d1ac51a370b9dfae3575c81c71b9bc451c344c1b56fec17f0af4e4","observation_id":"9310f242-bb3b-4610-b5d4-0dfc4510c825","resolution":{"observed_at":"2026-08-07T12:45:42.040015Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.02846","last_updated":"2021-09-07T03:59:22Z","snapshot_observed_at":"2026-08-06T08:08:54.702382Z","submitted_at":"2021-09-07T03:59:22Z","title":"Datasets: A Community Library for Natural Language Processing","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.02846","snapshot_observed_at":"2026-08-07T12:45:42.162519Z","title":"Datasets: A community library for natural language processing,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:42.162519Z"},"links":{"cited_paper":"/paper/2109.02846","citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:3e0738ceb15fc0930213e361b16e6f9b35b96aa283853651bf0dc005b0feb2e3","observation_id":"ec7ef6f0-768c-4196-b2de-863cf6d6e03b","resolution":{"observed_at":"2026-08-07T12:45:42.162519Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:45:47.278602Z","title":"Leetcode problemset,","venue":null,"work_id":"dcae1e7f-cb0e-4a7e-b559-9b0c751baba6","year":2025},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:42.206375Z"},"links":{"citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:5be21e3d49a60efed7ff1adf3b91ff5f747c42d987e2cc04fc16ea640520e20c","observation_id":"0342647e-a376-43a7-b107-d8ebe46ad4b9","resolution":{"observed_at":"2026-08-07T12:45:47.370907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:45:47.114238Z","title":"Math augmented dataset,","venue":null,"work_id":"6ac37b07-d4cf-41e5-8306-ecd811a7767e","year":2021},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:42.330482Z"},"links":{"citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:969d50ccd75db650f937fc5432af7bc04829e726e4654e9984c495e229616685","observation_id":"80b652f8-998e-4fca-b228-c76a1f4664fe","resolution":{"observed_at":"2026-08-07T12:45:47.191620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2504.15440","last_updated":"2025-04-21T21:12:28Z","snapshot_observed_at":"2026-08-07T16:00:26.895191Z","submitted_at":"2025-04-21T21:12:28Z","title":"Demand for LLMs: Descriptive Evidence on Substitution, Market Expansion, and Multihoming","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.15440","snapshot_observed_at":"2026-08-07T12:45:42.479403Z","title":"Demand for llms: Descriptive evidence on substitution, market expansion, and multihoming,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:42.479403Z"},"links":{"cited_paper":"/paper/2504.15440","citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:9c3fa88c9078b95fd0a2ad3148e2f8bd62d285e5a4dae6d01127972089be023a","observation_id":"2e370611-a598-4830-90dc-6f6eeac107cf","resolution":{"observed_at":"2026-08-07T12:45:42.479403Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:45:46.906063Z","title":"Openrouter: Unified api and playground for large lan- guage models,","venue":null,"work_id":"f6da8a11-87a0-4a7e-874b-41e8e8a67917","year":2025},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:42.626771Z"},"links":{"citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:ce75bd0969ec6908b78122689358ad36a1106c9605c14d8df06978d616f3f684","observation_id":"a89e5806-0e9d-4826-b762-d17d37bb3a16","resolution":{"observed_at":"2026-08-07T12:45:46.990726Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:45:46.727078Z","title":"Claude 3 model card october ad- dendum,","venue":null,"work_id":"b0a9863f-9bd6-49fd-985b-a9cc1de46d07","year":2024},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:42.776857Z"},"links":{"citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:24cd136d3bb8129469e64ca1810984be29a8f42c75cda8e73dd6cec59baad3b9","observation_id":"0cf8612f-703d-4c01-a6a4-ca824f8a2d19","resolution":{"observed_at":"2026-08-07T12:45:46.814161Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:45:46.491966Z","title":"Introducing deepseek-v3,","venue":null,"work_id":"47a583bc-c98a-4c50-8999-55ef4609f3fb","year":2024},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:42.920903Z"},"links":{"citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:6dd23617bd50970968a6fcf1c8435ca6b75fb0cee45a5d671aeba4774f211c8d","observation_id":"32ba1988-750b-4b99-962c-356c99ce6e07","resolution":{"observed_at":"2026-08-07T12:45:46.577130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:45:46.272369Z","title":"Gemini 2.0 flash,","venue":null,"work_id":"937b2ae9-5aa7-4a50-a379-964cbc5e2345","year":2025},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:43.036742Z"},"links":{"citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:b107f531e04c08392c4789ac22bc709bca861d3233cef8f24de25c3a93a78613","observation_id":"26899376-76be-4aef-8469-b302ef4dfce0","resolution":{"observed_at":"2026-08-07T12:45:46.356669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:45:46.156328Z","title":"Llama 3.3 70b instruct,","venue":null,"work_id":"7f77a841-2920-461a-8bd3-4b0f3b3a18bd","year":2024},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:43.145514Z"},"links":{"citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:d0d3f62c548b711baa084485d67a7753a8f12946596c4005aad23600595f6036","observation_id":"2d0e6f62-328a-46b6-8b69-7dc52f4de629","resolution":{"observed_at":"2026-08-07T12:45:46.199596Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:45:45.989477Z","title":"Gpt-4o-mini,","venue":null,"work_id":"129fdcc6-4fd8-4649-add5-2c8633de3900","year":2024},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:43.267076Z"},"links":{"citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:39baf21856461f4cb00a40cf0247f59b734813d9d08955788d28c26c8db5a977","observation_id":"2b1fb22a-a6f0-45bf-9af2-07c425797197","resolution":{"observed_at":"2026-08-07T12:45:46.078530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2407.02395","last_updated":"2024-07-04T08:59:31Z","snapshot_observed_at":"2026-07-06T18:40:22.951929Z","submitted_at":"2024-07-02T16:13:21Z","title":"Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.02395","snapshot_observed_at":"2026-08-07T12:45:43.375530Z","title":"Is your ai-generated code really safe? evaluating large language models on secure code generation with codeseceval,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:43.375530Z"},"links":{"cited_paper":"/paper/2407.02395","citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:cf6f99608479f8b432830803e0b0ca80c8e235b94c4801cced0f3c07a877b327","observation_id":"ec566a92-e435-4bc6-8eb7-23f91e20f791","resolution":{"observed_at":"2026-08-07T12:45:43.375530Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:45:45.831740Z","title":"Keeping an eye on dangerous python modules,","venue":null,"work_id":"95ce21b7-3138-4753-b6ff-0526ce139e2c","year":2023},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:43.496835Z"},"links":{"citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:2967c442129797e1f2ff0bc7be3250c226fb55c5a2539a40df42094394698494","observation_id":"5d0b8d27-9cf4-43d6-86a3-e1ebd65050b8","resolution":{"observed_at":"2026-08-07T12:45:45.894471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:45:43.638653Z","title":"A mathematical theory of communication,","venue":null,"work_id":null,"year":1948},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:43.638653Z"},"links":{"citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:bcae8dbf03767a7513d6ea45dab38c1796083f15d9a56bda53d0f3ec1add52c8","observation_id":"eb6c9b61-7f27-429f-8261-7d9ad7f76aa0","resolution":{"observed_at":"2026-08-07T12:45:43.638653Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:45:45.551638Z","title":"What are bob and alice saying? [mis]communication and intermediation between language and code,","venue":null,"work_id":"c2ada070-0cc5-4b27-8e3e-3c09f1d4d283","year":2021},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:43.766764Z"},"links":{"citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:484afb8a3487d56503008555d163a3f48ca03b54edefe1f061fb49e03b23e2e9","observation_id":"804d2c58-59c5-429a-b13a-b27f5a184709","resolution":{"observed_at":"2026-08-07T12:45:45.680712Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:45:45.356950Z","title":"Zittrain, Intellectual Debt: With Great Power Comes Great Ignorance , ser","venue":null,"work_id":"7e643743-b642-443f-bdc1-4806106a06ed","year":2022},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:43.894121Z"},"links":{"citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:681297db17b6a6ca3371740e0d85cf9b995bdf76987945e4a1734774e482bf89","observation_id":"a4ec811e-cc65-4c54-adde-79bac3377ab1","resolution":{"observed_at":"2026-08-07T12:45:45.423296Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":"3657.36439","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:45:44.419448Z","title":"The systems engineering approach in times of large language models,","venue":null,"work_id":"1126f35e-94d0-4cee-9b6b-321a298b61e0","year":2025},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:44.110888Z"},"links":{"citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:877ceef5e432d66900433cf08692865201f6527f1f84a11a07be79faaa707c68","observation_id":"fedb689d-251f-472a-87ad-c2897492e599","resolution":{"observed_at":"2026-08-07T12:45:44.494686Z","resolver_source":"raw_fallback","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":"2009.03300","last_updated":"2021-01-12T18:57:11Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-09-07T17:59:25Z","title":"Measuring Massive Multitask Language Understanding","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.03300","snapshot_observed_at":"2026-08-07T12:45:41.680009Z","title":"Available: https://arxiv.org/abs/2009.03300","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:41.680009Z"},"links":{"cited_paper":"/paper/2009.03300","citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:87d73360daf306e5517df74d4a24f45e7d8ccc9be3ee358504499b5e4e15f1e2","observation_id":"db350097-0094-4912-8d0b-2cc473d66803","resolution":{"observed_at":"2026-08-07T12:45:41.680009Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.15903","last_updated":"2025-04-23T13:23:40Z","snapshot_observed_at":"2026-08-07T16:00:13.788761Z","submitted_at":"2025-04-22T13:43:58Z","title":"Impact of Noise on LLM-Models Performance in Abstraction and Reasoning Corpus (ARC) Tasks with Model Temperature Considerations","version":2},"cited_work":{"arxiv_id":"2504.15903","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.15903","snapshot_observed_at":"2026-08-07T12:45:44.724854Z","title":"Impact of Noise on LLM-Models Performance in Abstraction and Reasoning Corpus (ARC) Tasks with Model Temperature Considerations","venue":"cs.AI","work_id":"88324d80-eb0d-4029-9b93-4e14e1b08a9c","year":2025},"citing_paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:41.896216Z"},"links":{"cited_paper":"/paper/2504.15903","citing_paper":"/paper/2505.23598"},"observation_digest":"sha256:4d4c3eab91b9e4e2b247b8c95b55d28cf91424b0fd0b438af0d768b751d864f7","observation_id":"01888682-4466-44e0-9d8f-f0328c7d998f","resolution":{"observed_at":"2026-08-07T12:45:44.803019Z","resolver_source":"local_arxiv","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"}}],"paper":{"arxiv_id":"2505.23598","last_updated":"2025-05-29T16:11:18Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T12:40:08.807501Z","submitted_at":"2025-05-29T16:11:18Z","title":"LLM Performance for Code Generation on Noisy Tasks"},"reference_resolution":{"displayed":34,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":18,"verified_exact":0,"verified_fuzzy":14},"total_outbound_references":34},"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 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2505.23598."}