{"as_of":"2026-08-08T18:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:355fc75c64a540af7063f639a099f2a40c04bc523aee795fb859ad38b421dc82","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":"2026-08-06T20:41:59.959329Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":17,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2305.06599","last_updated":"2023-09-07T11:39:07Z","snapshot_observed_at":"2026-08-03T16:58:05.952045Z","submitted_at":"2023-05-11T06:43:37Z","title":"Structured Chain-of-Thought Prompting for Code Generation","version":3},"cited_work":{"arxiv_id":"2305.06599","doi":"10.48550/arxiv.2305.06599","metadata_source":"arxiv_reference","pith_arxiv_id":"2305.06599","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Enabling programming thinking in large language models toward code generation","venue":"arXiv (Cornell University)","work_id":"a96b7963-b8de-4aa1-b8f7-2120b97c525c","year":2023},"citing_paper":{"arxiv_id":"2402.07927","last_updated":"2025-03-16T06:23:34Z","snapshot_observed_at":"2026-08-05T17:55:26.008016Z","submitted_at":"2024-02-05T19:49:13Z","title":"A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-12T21:52:09.938550Z"},"links":{"cited_paper":"/paper/2305.06599","citing_paper":"/paper/2402.07927"},"observation_digest":"sha256:5ebb58f4ddefd9ec7addfc49c0d651b60646c8b3f1e706f400fbeac495c77b95","observation_id":"cf43333d-b782-42d1-8b7d-5143aa0b1c95","resolution":{"observed_at":"2026-05-12T21:52:10.440072Z","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":"2305.06599","last_updated":"2023-09-07T11:39:07Z","snapshot_observed_at":"2026-08-03T16:58:05.952045Z","submitted_at":"2023-05-11T06:43:37Z","title":"Structured Chain-of-Thought Prompting for Code Generation","version":3},"cited_work":{"arxiv_id":"2305.06599","doi":"10.48550/arxiv.2305.06599","metadata_source":"arxiv_reference","pith_arxiv_id":"2305.06599","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Enabling programming thinking in large language models toward code generation","venue":"arXiv (Cornell University)","work_id":"a96b7963-b8de-4aa1-b8f7-2120b97c525c","year":2023},"citing_paper":{"arxiv_id":"2404.01535","last_updated":"2026-05-07T23:29:27Z","snapshot_observed_at":"2026-08-02T07:17:07.567055Z","submitted_at":"2024-04-01T23:55:05Z","title":"Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-24T02:19:23.135463Z"},"links":{"cited_paper":"/paper/2305.06599","citing_paper":"/paper/2404.01535"},"observation_digest":"sha256:e79f492ffd6319d71f5b61f9c821fadc2937c1fac1f65f297eafe50d5e0745a3","observation_id":"12afd70d-5f50-4f06-bf81-1f6f0b6d11dc","resolution":{"observed_at":"2026-05-24T02:23:46.110081Z","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":"2305.06599","last_updated":"2023-09-07T11:39:07Z","snapshot_observed_at":"2026-08-03T16:58:05.952045Z","submitted_at":"2023-05-11T06:43:37Z","title":"Structured Chain-of-Thought Prompting for Code Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.06599","snapshot_observed_at":"2026-08-06T20:41:59.959329Z","title":"Structured chain-of-thought prompting for code generation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02128","last_updated":"2025-08-22T02:50:24Z","snapshot_observed_at":"2026-08-06T20:35:09.779397Z","submitted_at":"2025-07-02T20:25:47Z","title":"CROP: Circuit Retrieval and Optimization with Parameter Guidance using LLMs","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T20:41:59.959329Z"},"links":{"cited_paper":"/paper/2305.06599","citing_paper":"/paper/2507.02128"},"observation_digest":"sha256:1a2b29e8f5ff6dc440d070b57bdaa4da7f9f40ba63267ae9c0ce54d071bedc76","observation_id":"5c632d75-3f32-458f-8451-b893496fc0bb","resolution":{"observed_at":"2026-08-06T20:41:59.959329Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.06599","last_updated":"2023-09-07T11:39:07Z","snapshot_observed_at":"2026-08-03T16:58:05.952045Z","submitted_at":"2023-05-11T06:43:37Z","title":"Structured Chain-of-Thought Prompting for Code Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.06599","snapshot_observed_at":"2026-08-05T15:17:26.694869Z","title":"Structured Chain-of-Thought Prompting for Code Generation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.20030","last_updated":"2025-08-27T16:33:51Z","snapshot_observed_at":"2026-08-06T07:49:00.648428Z","submitted_at":"2025-08-27T16:33:51Z","title":"Large Language Models (LLMs) for Electronic Design Automation (EDA)","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T15:17:26.694869Z"},"links":{"cited_paper":"/paper/2305.06599","citing_paper":"/paper/2508.20030"},"observation_digest":"sha256:f86220841f05ca1ddf85ab0718a72fe09917afbffa8443b79978bd89b7e814e4","observation_id":"82b761f9-5b33-4886-9cdc-177c4aee7fda","resolution":{"observed_at":"2026-08-05T15:17:26.694869Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.06599","last_updated":"2023-09-07T11:39:07Z","snapshot_observed_at":"2026-08-03T16:58:05.952045Z","submitted_at":"2023-05-11T06:43:37Z","title":"Structured Chain-of-Thought Prompting for Code Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.06599","snapshot_observed_at":"2026-08-05T05:07:43.233440Z","title":"Structured chain-of-thought prompting for code generation.arXiv preprint arXiv:2305.06599v3, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.05741","last_updated":"2025-09-06T15:07:59Z","snapshot_observed_at":"2026-08-08T07:46:11.746371Z","submitted_at":"2025-09-06T15:07:59Z","title":"Enhancing Factual Accuracy and Citation Generation in LLMs via Multi-Stage Self-Verification","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T05:07:43.233440Z"},"links":{"cited_paper":"/paper/2305.06599","citing_paper":"/paper/2509.05741"},"observation_digest":"sha256:2486d808d68b7ca6f1a6c95dc7a5f76e5cd852305efb819dc3f7ab269d8d899c","observation_id":"a3be3b86-b42b-4156-9e8a-b17d396557a0","resolution":{"observed_at":"2026-08-05T05:07:43.233440Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.06599","last_updated":"2023-09-07T11:39:07Z","snapshot_observed_at":"2026-08-03T16:58:05.952045Z","submitted_at":"2023-05-11T06:43:37Z","title":"Structured Chain-of-Thought Prompting for Code Generation","version":3},"cited_work":{"arxiv_id":"2305.06599","doi":"10.48550/arxiv.2305.06599","metadata_source":"arxiv_reference","pith_arxiv_id":"2305.06599","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Enabling programming thinking in large language models toward code generation","venue":"arXiv (Cornell University)","work_id":"a96b7963-b8de-4aa1-b8f7-2120b97c525c","year":2023},"citing_paper":{"arxiv_id":"2603.08715","last_updated":"2026-04-11T19:48:04Z","snapshot_observed_at":"2026-07-06T22:48:26.306802Z","submitted_at":"2026-02-04T01:52:30Z","title":"VeriInteresting: An Empirical Study of Model Prompt Interactions in Verilog Code Generation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-16T07:44:40.750566Z"},"links":{"cited_paper":"/paper/2305.06599","citing_paper":"/paper/2603.08715"},"observation_digest":"sha256:d67a6d38a2461caca63db889f0890814b23132a3c130cafc3b0b087bd32a5fd2","observation_id":"de01d0f6-9f22-4ae1-b43c-04a96799cb3b","resolution":{"observed_at":"2026-05-16T07:47:32.979811Z","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":"2305.06599","last_updated":"2023-09-07T11:39:07Z","snapshot_observed_at":"2026-08-03T16:58:05.952045Z","submitted_at":"2023-05-11T06:43:37Z","title":"Structured Chain-of-Thought Prompting for Code Generation","version":3},"cited_work":{"arxiv_id":"2305.06599","doi":"10.48550/arxiv.2305.06599","metadata_source":"arxiv_reference","pith_arxiv_id":"2305.06599","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Enabling programming thinking in large language models toward code generation","venue":"arXiv (Cornell University)","work_id":"a96b7963-b8de-4aa1-b8f7-2120b97c525c","year":2023},"citing_paper":{"arxiv_id":"2603.28653","last_updated":"2026-04-13T14:35:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-03-30T16:40:11Z","title":"BACE: LLM-based Code Generation through Bayesian Anchored Co-Evolution of Code and Test Populations","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-14T01:11:09.504337Z"},"links":{"cited_paper":"/paper/2305.06599","citing_paper":"/paper/2603.28653"},"observation_digest":"sha256:706f4a60982a0c7f346043d1dad1c093d1e1458de20afc4c9bb806b349684104","observation_id":"a602a918-d3b2-413c-bc87-9fce945d5436","resolution":{"observed_at":"2026-05-14T01:13:34.003205Z","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":"2305.06599","last_updated":"2023-09-07T11:39:07Z","snapshot_observed_at":"2026-08-03T16:58:05.952045Z","submitted_at":"2023-05-11T06:43:37Z","title":"Structured Chain-of-Thought Prompting for Code Generation","version":3},"cited_work":{"arxiv_id":"2305.06599","doi":"10.48550/arxiv.2305.06599","metadata_source":"arxiv_reference","pith_arxiv_id":"2305.06599","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Enabling programming thinking in large language models toward code generation","venue":"arXiv (Cornell University)","work_id":"a96b7963-b8de-4aa1-b8f7-2120b97c525c","year":2023},"citing_paper":{"arxiv_id":"2604.21598","last_updated":"2026-04-28T05:10:28Z","snapshot_observed_at":"2026-07-06T23:08:10.140279Z","submitted_at":"2026-04-23T12:21:03Z","title":"You Don't Need Public Tests to Generate Correct Code","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-09T20:57:43.802195Z"},"links":{"cited_paper":"/paper/2305.06599","citing_paper":"/paper/2604.21598"},"observation_digest":"sha256:e48df1863a812045a038e7c1ad10f6a502ab56bf998c44a11552aefac4bed6b4","observation_id":"db717a98-b08f-4b69-90e0-04b05badc4a6","resolution":{"observed_at":"2026-05-09T20:58:06.506489Z","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":"2305.06599","last_updated":"2023-09-07T11:39:07Z","snapshot_observed_at":"2026-08-03T16:58:05.952045Z","submitted_at":"2023-05-11T06:43:37Z","title":"Structured Chain-of-Thought Prompting for Code Generation","version":3},"cited_work":{"arxiv_id":"2305.06599","doi":"10.48550/arxiv.2305.06599","metadata_source":"arxiv_reference","pith_arxiv_id":"2305.06599","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Enabling programming thinking in large language models toward code generation","venue":"arXiv (Cornell University)","work_id":"a96b7963-b8de-4aa1-b8f7-2120b97c525c","year":2023},"citing_paper":{"arxiv_id":"2605.23931","last_updated":"2026-04-22T19:29:16Z","snapshot_observed_at":"2026-08-04T01:53:41.615824Z","submitted_at":"2026-04-22T19:29:16Z","title":"BODHI: Precise OS Kernel Specification Inference","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-05T02:06:43.318079Z"},"links":{"cited_paper":"/paper/2305.06599","citing_paper":"/paper/2605.23931"},"observation_digest":"sha256:267e7036d55f2992c1ef2d12aad29b8a87937279bdf2159750b36ae6468a091a","observation_id":"4f00e22f-a9a1-4933-8f23-0ef265983932","resolution":{"observed_at":"2026-07-05T02:10:36.453392Z","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":"2305.06599","last_updated":"2023-09-07T11:39:07Z","snapshot_observed_at":"2026-08-03T16:58:05.952045Z","submitted_at":"2023-05-11T06:43:37Z","title":"Structured Chain-of-Thought Prompting for Code Generation","version":3},"cited_work":{"arxiv_id":"2305.06599","doi":"10.48550/arxiv.2305.06599","metadata_source":"arxiv_reference","pith_arxiv_id":"2305.06599","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Enabling programming thinking in large language models toward code generation","venue":"arXiv (Cornell University)","work_id":"a96b7963-b8de-4aa1-b8f7-2120b97c525c","year":2023},"citing_paper":{"arxiv_id":"2606.08992","last_updated":"2026-06-08T03:42:08Z","snapshot_observed_at":"2026-08-01T21:35:10.152517Z","submitted_at":"2026-06-08T03:42:08Z","title":"SpaceVLN: A Zero-Shot Vision-and-Language Navigation Agent with Online Spatial Cognitive Memory and Reasoning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-27T16:49:46.634302Z"},"links":{"cited_paper":"/paper/2305.06599","citing_paper":"/paper/2606.08992"},"observation_digest":"sha256:ca7c4c3f56090bd1dd889af562f948e823f4e1162e7e99498c77d96ba70520d8","observation_id":"bf92abdb-0e81-44ce-89bb-810cce42bdf1","resolution":{"observed_at":"2026-07-03T01:07:29.999268Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2305.06599/citation-record","integrity":"/paper/2305.06599/integrity","json":"/paper/2305.06599/citation-record.json","paper":"/paper/2305.06599"},"outbound":[],"paper":{"arxiv_id":"2305.06599","last_updated":"2023-09-07T11:39:07Z","latest_version":3,"primary_category":"cs.SE","snapshot_observed_at":"2026-08-03T16:58:05.952045Z","submitted_at":"2023-05-11T06:43:37Z","title":"Structured Chain-of-Thought Prompting for 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:2305.06599."}