{"as_of":"2026-08-14T06:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e66e9eab8fbf8e15837bdf00aaabdcfa0b55f8e7ca9ad42463200b0f4f9d736b","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":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T13:21:05.597420Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-07T15:42:26.911183Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.00658","last_updated":"2024-10-15T09:16:38Z","snapshot_observed_at":"2026-08-13T04:29:11.649710Z","submitted_at":"2024-02-01T15:18:33Z","title":"Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.00658","snapshot_observed_at":"2026-08-12T13:21:05.597420Z","title":"Chen, and Shafiq Joty","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.16345","last_updated":"2025-02-14T09:32:11Z","snapshot_observed_at":"2026-08-12T21:31:53.512967Z","submitted_at":"2024-11-25T12:44:02Z","title":"Preference Optimization for Reasoning with Pseudo Feedback","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T13:21:05.597420Z"},"links":{"cited_paper":"/paper/2402.00658","citing_paper":"/paper/2411.16345"},"observation_digest":"sha256:64a3f3575ebf148c1e9b8cdb9b439147e6970b8446f73f916090338212345278","observation_id":"5709a708-bbc7-4505-815a-6730cf786dff","resolution":{"observed_at":"2026-08-12T13:21:05.597420Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.00658","last_updated":"2024-10-15T09:16:38Z","snapshot_observed_at":"2026-08-13T04:29:11.649710Z","submitted_at":"2024-02-01T15:18:33Z","title":"Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.00658","snapshot_observed_at":"2026-08-12T10:39:36.879146Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.19039","last_updated":"2024-11-28T10:35:16Z","snapshot_observed_at":"2026-08-13T16:36:49.167890Z","submitted_at":"2024-11-28T10:35:16Z","title":"Mars-PO: Multi-Agent Reasoning System Preference Optimization","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-12T10:39:36.879146Z"},"links":{"cited_paper":"/paper/2402.00658","citing_paper":"/paper/2411.19039"},"observation_digest":"sha256:82c12a86d7b0d47f664f874932e4bf02c02fd77466a49fdb3ac67987ca834800","observation_id":"da187040-5038-43de-a5f9-d2fffd4447d7","resolution":{"observed_at":"2026-08-12T10:39:36.879146Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.00658","last_updated":"2024-10-15T09:16:38Z","snapshot_observed_at":"2026-08-13T04:29:11.649710Z","submitted_at":"2024-02-01T15:18:33Z","title":"Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.00658","snapshot_observed_at":"2026-08-11T11:40:13.597847Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.15118","last_updated":"2025-06-06T12:13:42Z","snapshot_observed_at":"2026-08-13T12:39:00.588330Z","submitted_at":"2024-12-19T17:59:42Z","title":"Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generation","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-11T11:40:13.597847Z"},"links":{"cited_paper":"/paper/2402.00658","citing_paper":"/paper/2412.15118"},"observation_digest":"sha256:2928f9c9c8c60f84272bff61aaffef2a9d553aceb1fb674800e46a4f9fd2daf6","observation_id":"7fd0cef1-7967-4e70-b4d2-e75dd1d7d11b","resolution":{"observed_at":"2026-08-11T11:40:13.597847Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.00658","last_updated":"2024-10-15T09:16:38Z","snapshot_observed_at":"2026-08-13T04:29:11.649710Z","submitted_at":"2024-02-01T15:18:33Z","title":"Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing","version":3},"cited_work":{"arxiv_id":"2402.00658","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.00658","snapshot_observed_at":"2026-08-07T15:42:26.911183Z","title":"Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing","venue":"cs.AI","work_id":"0787ec82-e2b6-40ef-a5b0-81a31a0967d7","year":2024},"citing_paper":{"arxiv_id":"2505.14107","last_updated":"2025-05-29T08:24:00Z","snapshot_observed_at":"2026-08-13T20:12:53.983350Z","submitted_at":"2025-05-20T09:14:53Z","title":"DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models","version":4},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:22.042716Z"},"links":{"cited_paper":"/paper/2402.00658","citing_paper":"/paper/2505.14107"},"observation_digest":"sha256:304779678884ebb8cc0725850caffdc3ac9b2d7fd7c7c7239b04ac018dffb0d8","observation_id":"7749b063-e0fb-4649-b3c8-43bf035ed78e","resolution":{"observed_at":"2026-08-07T15:42:27.076360Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.00658","last_updated":"2024-10-15T09:16:38Z","snapshot_observed_at":"2026-08-13T04:29:11.649710Z","submitted_at":"2024-02-01T15:18:33Z","title":"Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.00658","snapshot_observed_at":"2026-08-07T14:11:57.753578Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.19683","last_updated":"2025-05-26T08:44:53Z","snapshot_observed_at":"2026-08-13T22:06:58.555085Z","submitted_at":"2025-05-26T08:44:53Z","title":"Large Language Models for Planning: A Comprehensive and Systematic Survey","version":1},"reference_index":111,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:57.753578Z"},"links":{"cited_paper":"/paper/2402.00658","citing_paper":"/paper/2505.19683"},"observation_digest":"sha256:cd365da0aaa8547f3fdf05b9d7d93def9657a3f645e2db6566294db34d5ea419","observation_id":"0c4735c7-4597-42d8-a28f-021c161a5a18","resolution":{"observed_at":"2026-08-07T14:11:57.753578Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.00658","last_updated":"2024-10-15T09:16:38Z","snapshot_observed_at":"2026-08-13T04:29:11.649710Z","submitted_at":"2024-02-01T15:18:33Z","title":"Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.00658","snapshot_observed_at":"2026-07-11T13:53:36.775836Z","title":"arXiv preprint arXiv:2402.00658 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04763","last_updated":"2026-07-26T14:17:18Z","snapshot_observed_at":"2026-08-02T10:24:43.977557Z","submitted_at":"2026-07-06T07:56:53Z","title":"Multi-Turn On-Policy Distillation with Prefix Replay","version":1},"reference_index":92,"source":"arxiv_source","source_observed_at":"2026-07-11T13:53:36.775836Z"},"links":{"cited_paper":"/paper/2402.00658","citing_paper":"/paper/2607.04763"},"observation_digest":"sha256:d0320418ca941cb86b470fa488fee4d50b294394c98674618066954f2b4d15ef","observation_id":"990160fb-6307-4daa-9858-00d3fdbbe47c","resolution":{"observed_at":"2026-07-11T13:53:36.775836Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.00658","last_updated":"2024-10-15T09:16:38Z","snapshot_observed_at":"2026-08-13T04:29:11.649710Z","submitted_at":"2024-02-01T15:18:33Z","title":"Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.00658","snapshot_observed_at":"2026-08-02T08:40:41.861888Z","title":"arXiv preprint arXiv:2402.00658 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04763","last_updated":"2026-07-26T14:17:18Z","snapshot_observed_at":"2026-08-02T10:24:43.977557Z","submitted_at":"2026-07-06T07:56:53Z","title":"Multi-Turn On-Policy Distillation with Prefix Replay","version":3},"reference_index":93,"source":"arxiv_source","source_observed_at":"2026-08-02T08:40:41.861888Z"},"links":{"cited_paper":"/paper/2402.00658","citing_paper":"/paper/2607.04763"},"observation_digest":"sha256:99c28d2d48ac28f361da5e009b957c16621bd1db066690f25509c12b9a23483a","observation_id":"594a8fd3-7461-4573-91c8-b2fdff369f9e","resolution":{"observed_at":"2026-08-02T08:40:41.861888Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2402.00658/citation-record","integrity":"/paper/2402.00658/integrity","json":"/paper/2402.00658/citation-record.json","paper":"/paper/2402.00658"},"outbound":[],"paper":{"arxiv_id":"2402.00658","last_updated":"2024-10-15T09:16:38Z","latest_version":3,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-13T04:29:11.649710Z","submitted_at":"2024-02-01T15:18:33Z","title":"Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2402.00658."}