{"as_of":"2026-08-07T19:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c9291fe15d2659022733794a976d3b7780f95db059a4c43bfba9e6ca529688e2","coverage":[{"denominator":16,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":16,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T01:01:39.786265Z","state":"measured"},{"denominator":16,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":16,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2607.25970/citation-record","integrity":"/paper/2607.25970/integrity","json":"/paper/2607.25970/citation-record.json","paper":"/paper/2607.25970"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T01:01:38.557382Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.25970","last_updated":"2026-07-28T16:52:31Z","snapshot_observed_at":"2026-08-07T15:44:57.874753Z","submitted_at":"2026-07-28T16:52:31Z","title":"Reinforcement Learning for Code Optimization","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T01:01:38.557382Z"},"links":{"citing_paper":"/paper/2607.25970"},"observation_digest":"sha256:6f4d33551131736de39f449b205b2d86745034ca568da384d906f133730946b6","observation_id":"47eb9480-f748-4819-87c3-aaacc48b522a","resolution":{"observed_at":"2026-08-01T01:01:38.557382Z","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":"2026-08-01T01:01:38.450779Z","title":null,"venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2607.25970","last_updated":"2026-07-28T16:52:31Z","snapshot_observed_at":"2026-08-07T15:44:57.874753Z","submitted_at":"2026-07-28T16:52:31Z","title":"Reinforcement Learning for Code Optimization","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T01:01:38.450779Z"},"links":{"citing_paper":"/paper/2607.25970"},"observation_digest":"sha256:e03898f54c81ea1c4ac2ce753b9283886c2c2fbb471fec2830637b731e1cda98","observation_id":"c8f66e68-a287-475b-a550-f5dc63e48d6a","resolution":{"observed_at":"2026-08-01T01:01:38.450779Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-01T01:01:38.728223Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.25970","last_updated":"2026-07-28T16:52:31Z","snapshot_observed_at":"2026-08-07T15:44:57.874753Z","submitted_at":"2026-07-28T16:52:31Z","title":"Reinforcement Learning for Code Optimization","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T01:01:38.728223Z"},"links":{"citing_paper":"/paper/2607.25970"},"observation_digest":"sha256:521d99cd9a367b0b8fc8689091a5064722e2684a51015536ec08b1522b4936c2","observation_id":"0c22044c-d835-4eac-90e7-bb3c57a29f09","resolution":{"observed_at":"2026-08-01T01:01:38.728223Z","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":"2026-08-01T01:01:38.641622Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.25970","last_updated":"2026-07-28T16:52:31Z","snapshot_observed_at":"2026-08-07T15:44:57.874753Z","submitted_at":"2026-07-28T16:52:31Z","title":"Reinforcement Learning for Code Optimization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T01:01:38.641622Z"},"links":{"citing_paper":"/paper/2607.25970"},"observation_digest":"sha256:ad3431b25ee23532f3e72508676fcdba680364230df1ea56ec6bbc064135a28c","observation_id":"a0e68cb8-55df-4db2-9520-f2e4f4f46272","resolution":{"observed_at":"2026-08-01T01:01:38.641622Z","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":"2026-08-01T01:01:38.939184Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.25970","last_updated":"2026-07-28T16:52:31Z","snapshot_observed_at":"2026-08-07T15:44:57.874753Z","submitted_at":"2026-07-28T16:52:31Z","title":"Reinforcement Learning for Code Optimization","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T01:01:38.939184Z"},"links":{"citing_paper":"/paper/2607.25970"},"observation_digest":"sha256:05d58a770bf12d646d5a6acef2faf864da7ff221e8aab15f85c137c3eed7cadc","observation_id":"b48e65eb-5c38-461b-a214-cab25860d208","resolution":{"observed_at":"2026-08-01T01:01:38.939184Z","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":"2026-08-01T01:01:38.851724Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.25970","last_updated":"2026-07-28T16:52:31Z","snapshot_observed_at":"2026-08-07T15:44:57.874753Z","submitted_at":"2026-07-28T16:52:31Z","title":"Reinforcement Learning for Code Optimization","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T01:01:38.851724Z"},"links":{"citing_paper":"/paper/2607.25970"},"observation_digest":"sha256:b4562ab42b75f669eae68cb2007923003d35a9205950337114ea6b8b41cdb370","observation_id":"7af23251-6972-4c13-8603-d2bed0ac687b","resolution":{"observed_at":"2026-08-01T01:01:38.851724Z","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":"2026-08-01T01:01:39.018947Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.25970","last_updated":"2026-07-28T16:52:31Z","snapshot_observed_at":"2026-08-07T15:44:57.874753Z","submitted_at":"2026-07-28T16:52:31Z","title":"Reinforcement Learning for Code Optimization","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T01:01:39.018947Z"},"links":{"citing_paper":"/paper/2607.25970"},"observation_digest":"sha256:6ed2d2bf2960f48bc284d19aab5462b190edb3a94955cca77644c68919496475","observation_id":"18316f2a-a716-4f0a-8e0d-fe5391880b93","resolution":{"observed_at":"2026-08-01T01:01:39.018947Z","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":"2026-08-01T01:01:39.121212Z","title":"(b) Keep only tests with¯dt < a","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.25970","last_updated":"2026-07-28T16:52:31Z","snapshot_observed_at":"2026-08-07T15:44:57.874753Z","submitted_at":"2026-07-28T16:52:31Z","title":"Reinforcement Learning for Code Optimization","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T01:01:39.121212Z"},"links":{"citing_paper":"/paper/2607.25970"},"observation_digest":"sha256:9686a831cf97eb07d88c21f04a581a1aaf0025147f3e0bf34d3b694107e329e4","observation_id":"6f9a3cbb-c09f-456c-bd90-9fdc2bf93078","resolution":{"observed_at":"2026-08-01T01:01:39.121212Z","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":"2026-08-01T01:01:39.229154Z","title":"(b) Keep only tests with|ut| + |vt|< L","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.25970","last_updated":"2026-07-28T16:52:31Z","snapshot_observed_at":"2026-08-07T15:44:57.874753Z","submitted_at":"2026-07-28T16:52:31Z","title":"Reinforcement Learning for Code Optimization","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T01:01:39.229154Z"},"links":{"citing_paper":"/paper/2607.25970"},"observation_digest":"sha256:ba57ad726554a3571d45f2e805940d0c75ffa5b7a805a72bb74afa5473a55769","observation_id":"209af1f3-c4e5-40f9-9033-d1ec2e3d1323","resolution":{"observed_at":"2026-08-01T01:01:39.229154Z","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":"2026-08-01T01:01:39.310772Z","title":"LetTD = {t∈T:D t usable}and letS r(TD)be the⌊r|T D|/100⌋slowest tests inT D under ¯dt","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.25970","last_updated":"2026-07-28T16:52:31Z","snapshot_observed_at":"2026-08-07T15:44:57.874753Z","submitted_at":"2026-07-28T16:52:31Z","title":"Reinforcement Learning for Code Optimization","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T01:01:39.310772Z"},"links":{"citing_paper":"/paper/2607.25970"},"observation_digest":"sha256:891889241e9cd53b85dbba4bede930e7a71c4bfbb0be9d5a6492ad8068427697","observation_id":"be28d81d-a175-4b5e-8764-3286870dd2be","resolution":{"observed_at":"2026-08-01T01:01:39.310772Z","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":"2026-08-01T01:01:39.411890Z","title":"(b) Set the same intended optimization timeout for every retained test","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.25970","last_updated":"2026-07-28T16:52:31Z","snapshot_observed_at":"2026-08-07T15:44:57.874753Z","submitted_at":"2026-07-28T16:52:31Z","title":"Reinforcement Learning for Code Optimization","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T01:01:39.411890Z"},"links":{"citing_paper":"/paper/2607.25970"},"observation_digest":"sha256:9776c563de4a53fe17f0b6c52d50e99172d4e157f9d32c661932414bad16c34c","observation_id":"c2ec4d88-24cc-4ee0-9fcc-2cf4f9e7708f","resolution":{"observed_at":"2026-08-01T01:01:39.411890Z","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":"2026-08-01T01:01:39.515151Z","title":"(b) Set the intended timeout of each test to a percentile of its own reference durations","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.25970","last_updated":"2026-07-28T16:52:31Z","snapshot_observed_at":"2026-08-07T15:44:57.874753Z","submitted_at":"2026-07-28T16:52:31Z","title":"Reinforcement Learning for Code Optimization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T01:01:39.515151Z"},"links":{"citing_paper":"/paper/2607.25970"},"observation_digest":"sha256:7091f75f48c2a25047224089dda6789ad60638cb73099815b58903625538698f","observation_id":"09320bac-2572-4dd8-a092-65e02e9d9fe3","resolution":{"observed_at":"2026-08-01T01:01:39.515151Z","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":"2026-08-01T01:01:39.585325Z","title":"more-correctness","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2607.25970","last_updated":"2026-07-28T16:52:31Z","snapshot_observed_at":"2026-08-07T15:44:57.874753Z","submitted_at":"2026-07-28T16:52:31Z","title":"Reinforcement Learning for Code Optimization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T01:01:39.585325Z"},"links":{"citing_paper":"/paper/2607.25970"},"observation_digest":"sha256:c551392e9d2a8043545eca930e49efcdf5103d5ddf432688c12f7c6f5890fef3","observation_id":"81b718dc-264f-4eb7-a702-9b50725c23ca","resolution":{"observed_at":"2026-08-01T01:01:39.585325Z","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":"2026-08-01T01:01:39.685915Z","title":"Compare their algorithms, data structures, and implementation choices","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.25970","last_updated":"2026-07-28T16:52:31Z","snapshot_observed_at":"2026-08-07T15:44:57.874753Z","submitted_at":"2026-07-28T16:52:31Z","title":"Reinforcement Learning for Code Optimization","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T01:01:39.685915Z"},"links":{"citing_paper":"/paper/2607.25970"},"observation_digest":"sha256:c13cb0ee181e8cf653b8a36243b2e1f83726ce50bad9c15f428552ab0a1ac064","observation_id":"a891d3c1-7651-4f2e-81db-bcc1469189c9","resolution":{"observed_at":"2026-08-01T01:01:39.685915Z","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":"2026-08-01T01:01:39.786265Z","title":"predicted_faster","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2607.25970","last_updated":"2026-07-28T16:52:31Z","snapshot_observed_at":"2026-08-07T15:44:57.874753Z","submitted_at":"2026-07-28T16:52:31Z","title":"Reinforcement Learning for Code Optimization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T01:01:39.786265Z"},"links":{"citing_paper":"/paper/2607.25970"},"observation_digest":"sha256:3c84bc955fa199ce8d2a177bb05d7ff0b9086a3a8fd38b2401acedd0e6816d80","observation_id":"ac6e30e0-0676-4e99-a1bb-7638c32d90c2","resolution":{"observed_at":"2026-08-01T01:01:39.786265Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.06647","last_updated":"2025-02-19T04:16:24Z","snapshot_observed_at":"2026-07-06T18:28:24.821865Z","submitted_at":"2024-06-10T04:19:20Z","title":"How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmark","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.06647","snapshot_observed_at":"2026-08-01T01:01:38.353080Z","title":"good” or “bad","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.25970","last_updated":"2026-07-28T16:52:31Z","snapshot_observed_at":"2026-08-07T15:44:57.874753Z","submitted_at":"2026-07-28T16:52:31Z","title":"Reinforcement Learning for Code Optimization","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-01T01:01:38.353080Z"},"links":{"cited_paper":"/paper/2406.06647","citing_paper":"/paper/2607.25970"},"observation_digest":"sha256:76c26184aa1c110708ff40362d596abad88d3447b7d0e7afbcfad019859b743f","observation_id":"494ef898-6c9f-4e1c-a8d7-d80367638d85","resolution":{"observed_at":"2026-08-01T01:01:38.353080Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.25970","last_updated":"2026-07-28T16:52:31Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T15:44:57.874753Z","submitted_at":"2026-07-28T16:52:31Z","title":"Reinforcement Learning for Code Optimization"},"reference_resolution":{"displayed":16,"state_counts":{"malformed_identifier":2,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":14,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":16},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2607.25970."}