{"as_of":"2026-08-11T01:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:faab52d90369cb4b420f4b7d38e6410f8f59a6fe839ae89a5a35282186b282f8","coverage":[{"denominator":12,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T14:26:04.493548Z","state":"measured"},{"denominator":12,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":12,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2502.06769/citation-record","integrity":"/paper/2502.06769/integrity","json":"/paper/2502.06769/citation-record.json","paper":"/paper/2502.06769"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T14:26:04.694426Z","title":"Deep configuration performance learning: A systematic survey and taxonomy,","venue":null,"work_id":"1a6200b0-be43-491c-ad0a-02b71489ff3d","year":2024},"citing_paper":{"arxiv_id":"2502.06769","last_updated":"2025-03-18T11:12:46Z","snapshot_observed_at":"2026-08-09T19:40:49.682258Z","submitted_at":"2025-02-10T18:48:45Z","title":"Enhancing Trust in Language Model-Based Code Optimization through RLHF: A Research Design","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T14:26:04.442423Z"},"links":{"citing_paper":"/paper/2502.06769"},"observation_digest":"sha256:515749bdda6de364925b20be49b5a627c8c52db72b5223cb67b1af7c3e8d0c5e","observation_id":"9b4403e1-542b-46c9-a0a3-f3d97dfc1148","resolution":{"observed_at":"2026-08-08T14:26:04.699021Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:26:04.679991Z","title":"Rtlrewriter: Methodologies for large models aided rtl code optimization,","venue":null,"work_id":"c7725366-b1cb-4c36-8606-1473792f457e","year":2024},"citing_paper":{"arxiv_id":"2502.06769","last_updated":"2025-03-18T11:12:46Z","snapshot_observed_at":"2026-08-09T19:40:49.682258Z","submitted_at":"2025-02-10T18:48:45Z","title":"Enhancing Trust in Language Model-Based Code Optimization through RLHF: A Research Design","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T14:26:04.447288Z"},"links":{"citing_paper":"/paper/2502.06769"},"observation_digest":"sha256:8e9b187be8f302261ef1987db160f8344118eb5bc7bd48ff7c617a1ab5ad01f2","observation_id":"297f36fd-da03-4d81-9e60-2a66ee8e876e","resolution":{"observed_at":"2026-08-08T14:26:04.684557Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:26:04.664567Z","title":"Learning performance-improving code edits,","venue":null,"work_id":"4186c277-a50b-4d61-8700-bcff0ce5166c","year":2024},"citing_paper":{"arxiv_id":"2502.06769","last_updated":"2025-03-18T11:12:46Z","snapshot_observed_at":"2026-08-09T19:40:49.682258Z","submitted_at":"2025-02-10T18:48:45Z","title":"Enhancing Trust in Language Model-Based Code Optimization through RLHF: A Research Design","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T14:26:04.451878Z"},"links":{"citing_paper":"/paper/2502.06769"},"observation_digest":"sha256:032c3abb0ceb8d3cc41c72346484d3ceea5a2f32df753511526e3dc778e36b89","observation_id":"13697c76-3a37-48e0-aedd-61b34897f793","resolution":{"observed_at":"2026-08-08T14:26:04.669686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:26:04.645728Z","title":"Langprop: A code optimization framework using large language models applied to driving,","venue":null,"work_id":"06a78ae8-d501-4e3f-8cb5-3dcd480739e2","year":2024},"citing_paper":{"arxiv_id":"2502.06769","last_updated":"2025-03-18T11:12:46Z","snapshot_observed_at":"2026-08-09T19:40:49.682258Z","submitted_at":"2025-02-10T18:48:45Z","title":"Enhancing Trust in Language Model-Based Code Optimization through RLHF: A Research Design","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T14:26:04.456375Z"},"links":{"citing_paper":"/paper/2502.06769"},"observation_digest":"sha256:9e283cb8403d65115638c3456ecbb6ee1a732d979958acbe56aee3b38e115ef4","observation_id":"72521d60-e29e-4cf6-a0dd-1ea1d18dc72e","resolution":{"observed_at":"2026-08-08T14:26:04.650867Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.01277","last_updated":"2025-01-03T14:55:21Z","snapshot_observed_at":"2026-08-10T22:28:51.910721Z","submitted_at":"2025-01-02T14:20:36Z","title":"Language Models for Code Optimization: Survey, Challenges and Future Directions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.01277","snapshot_observed_at":"2026-08-08T14:26:04.461244Z","title":"Language models for code opti- mization: Survey, challenges and future directions,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2502.06769","last_updated":"2025-03-18T11:12:46Z","snapshot_observed_at":"2026-08-09T19:40:49.682258Z","submitted_at":"2025-02-10T18:48:45Z","title":"Enhancing Trust in Language Model-Based Code Optimization through RLHF: A Research Design","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T14:26:04.461244Z"},"links":{"cited_paper":"/paper/2501.01277","citing_paper":"/paper/2502.06769"},"observation_digest":"sha256:c1e3510fe80e47eb3e66c6cc73cb2d0b96ecbeb62d4d9c53d60365c15e29d531","observation_id":"d8dd18d7-1f4a-4adb-a5b6-175ac634c9b1","resolution":{"observed_at":"2026-08-08T14:26:04.461244Z","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":"2026-08-08T14:26:04.630728Z","title":"A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,","venue":null,"work_id":"1a39b3b7-c16a-4c73-b986-b105f20fc185","year":2024},"citing_paper":{"arxiv_id":"2502.06769","last_updated":"2025-03-18T11:12:46Z","snapshot_observed_at":"2026-08-09T19:40:49.682258Z","submitted_at":"2025-02-10T18:48:45Z","title":"Enhancing Trust in Language Model-Based Code Optimization through RLHF: A Research Design","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T14:26:04.466562Z"},"links":{"citing_paper":"/paper/2502.06769"},"observation_digest":"sha256:6682d1d1d91aa38383df21965df11659123dbb51c30f8a4a3ea3b25e65f60855","observation_id":"4338d966-b2b6-4cf4-88ce-916b273688b4","resolution":{"observed_at":"2026-08-08T14:26:04.635851Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10705","last_updated":"2024-11-13T15:46:08Z","snapshot_observed_at":"2026-08-07T14:55:26.591545Z","submitted_at":"2024-02-16T14:04:56Z","title":"AutoSAT: Automatically Optimize SAT Solvers via Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10705","snapshot_observed_at":"2026-08-08T14:26:04.471454Z","title":"Au- tosat: Automatically optimize sat solvers via large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.06769","last_updated":"2025-03-18T11:12:46Z","snapshot_observed_at":"2026-08-09T19:40:49.682258Z","submitted_at":"2025-02-10T18:48:45Z","title":"Enhancing Trust in Language Model-Based Code Optimization through RLHF: A Research Design","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T14:26:04.471454Z"},"links":{"cited_paper":"/paper/2402.10705","citing_paper":"/paper/2502.06769"},"observation_digest":"sha256:902b9d3d2fccbb88b2386a4d74f4118bcd997a691348f363e35597cdb9d583d7","observation_id":"c1e0671c-5a67-4511-9cb0-cfa087527d1c","resolution":{"observed_at":"2026-08-08T14:26:04.471454Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.03578","last_updated":"2024-11-18T06:22:38Z","snapshot_observed_at":"2026-07-06T20:01:45.826971Z","submitted_at":"2024-11-18T06:22:38Z","title":"PerfCodeGen: Improving Performance of LLM Generated Code with Execution Feedback","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.03578","snapshot_observed_at":"2026-08-08T14:26:04.476313Z","title":"Perfcodegen: Improving performance of llm generated code with execution feedback,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.06769","last_updated":"2025-03-18T11:12:46Z","snapshot_observed_at":"2026-08-09T19:40:49.682258Z","submitted_at":"2025-02-10T18:48:45Z","title":"Enhancing Trust in Language Model-Based Code Optimization through RLHF: A Research Design","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T14:26:04.476313Z"},"links":{"cited_paper":"/paper/2412.03578","citing_paper":"/paper/2502.06769"},"observation_digest":"sha256:e93767dd38d8e6b71a77b9185697bd9e6ac80c1fe2aa32ad64540a015800c17f","observation_id":"cbb93eb0-0f5f-4c94-8038-8c6e5f73a952","resolution":{"observed_at":"2026-08-08T14:26:04.476313Z","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":"2026-08-08T14:26:04.616540Z","title":"Research design canvas,","venue":null,"work_id":"a418b984-5e9c-41bc-a861-61699bb4ce1f","year":2024},"citing_paper":{"arxiv_id":"2502.06769","last_updated":"2025-03-18T11:12:46Z","snapshot_observed_at":"2026-08-09T19:40:49.682258Z","submitted_at":"2025-02-10T18:48:45Z","title":"Enhancing Trust in Language Model-Based Code Optimization through RLHF: A Research Design","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T14:26:04.480903Z"},"links":{"citing_paper":"/paper/2502.06769"},"observation_digest":"sha256:865418881a2769d60828c3e3c11c2cf2b8bc09bb98833944dd727b5ef47e0851","observation_id":"c287eeda-5f77-4604-bf00-2986c732d8db","resolution":{"observed_at":"2026-08-08T14:26:04.620921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:26:04.602183Z","title":"Innovate uk knowledge transfer partnership,","venue":null,"work_id":"c7d233f3-8fdb-47bc-aecb-966974b68c30","year":2025},"citing_paper":{"arxiv_id":"2502.06769","last_updated":"2025-03-18T11:12:46Z","snapshot_observed_at":"2026-08-09T19:40:49.682258Z","submitted_at":"2025-02-10T18:48:45Z","title":"Enhancing Trust in Language Model-Based Code Optimization through RLHF: A Research Design","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T14:26:04.485253Z"},"links":{"citing_paper":"/paper/2502.06769"},"observation_digest":"sha256:07dfb5d66934e1aa99be225c82620158fd36594257f6c38361c1451c0972bca0","observation_id":"e1f7abfd-77be-4413-b544-ca97eb607779","resolution":{"observed_at":"2026-08-08T14:26:04.606656Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T14:26:04.581930Z","title":"Artemis: Ai-powered code optimization platform,","venue":null,"work_id":"ca7b64d5-219d-43ff-9bcc-b2a9ab5d5d2f","year":2025},"citing_paper":{"arxiv_id":"2502.06769","last_updated":"2025-03-18T11:12:46Z","snapshot_observed_at":"2026-08-09T19:40:49.682258Z","submitted_at":"2025-02-10T18:48:45Z","title":"Enhancing Trust in Language Model-Based Code Optimization through RLHF: A Research Design","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T14:26:04.489012Z"},"links":{"citing_paper":"/paper/2502.06769"},"observation_digest":"sha256:2c0fbb785bcc7012f13eac3b242de31443f2d358cebd037a440d5f559ce1aec0","observation_id":"76ae32c9-82a9-4f18-9e5c-6136db9f79af","resolution":{"observed_at":"2026-08-08T14:26:04.589665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2204.05862","last_updated":"2022-04-12T15:02:38Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-04-12T15:02:38Z","title":"Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.05862","snapshot_observed_at":"2026-08-08T14:26:04.493548Z","title":"Training a helpful and harmless assistant with reinforcement learning from human feedback,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.06769","last_updated":"2025-03-18T11:12:46Z","snapshot_observed_at":"2026-08-09T19:40:49.682258Z","submitted_at":"2025-02-10T18:48:45Z","title":"Enhancing Trust in Language Model-Based Code Optimization through RLHF: A Research Design","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T14:26:04.493548Z"},"links":{"cited_paper":"/paper/2204.05862","citing_paper":"/paper/2502.06769"},"observation_digest":"sha256:d71b701ea5b4bfeb639c6ef3c107bd5e89d20b2f0e9947aeea3a1edb5c37447f","observation_id":"853c23be-9fa1-48a5-a233-aa9ffe6d4462","resolution":{"observed_at":"2026-08-08T14:26:04.493548Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.06769","last_updated":"2025-03-18T11:12:46Z","latest_version":2,"primary_category":"cs.SE","snapshot_observed_at":"2026-08-09T19:40:49.682258Z","submitted_at":"2025-02-10T18:48:45Z","title":"Enhancing Trust in Language Model-Based Code Optimization through RLHF: A Research Design"},"reference_resolution":{"displayed":12,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":0,"verified_fuzzy":8},"total_outbound_references":12},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2502.06769."}