{"as_of":"2026-08-14T01:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c150d6bc532b9bdbbd6a07e1b27441304dcd41c7897470661bc490116ff0454d","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T11:19:44.499905Z","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":"2026-08-07T00:13:42.562561Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2403.03348","last_updated":"2024-06-09T14:24:54Z","snapshot_observed_at":"2026-08-13T04:02:39.524255Z","submitted_at":"2024-03-05T22:21:45Z","title":"Learning to Maximize Mutual Information for Chain-of-Thought Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.03348","snapshot_observed_at":"2026-08-07T11:19:44.499905Z","title":"Learning to maximize mutual information for chain-of-thought distillation.arXiv preprint arXiv:2403.03348, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.02867","last_updated":"2025-06-04T15:00:58Z","snapshot_observed_at":"2026-08-08T18:19:35.851376Z","submitted_at":"2025-06-03T13:31:10Z","title":"Demystifying Reasoning Dynamics with Mutual Information: Thinking Tokens are Information Peaks in LLM Reasoning","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T11:19:44.499905Z"},"links":{"cited_paper":"/paper/2403.03348","citing_paper":"/paper/2506.02867"},"observation_digest":"sha256:1b0770c08d116b9f3038cef525c65801ff89cd80ecc8f5299b1379d3a469a240","observation_id":"00f0903a-b181-4972-8e12-f84f99f42b42","resolution":{"observed_at":"2026-08-07T11:19:44.499905Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03348","last_updated":"2024-06-09T14:24:54Z","snapshot_observed_at":"2026-08-13T04:02:39.524255Z","submitted_at":"2024-03-05T22:21:45Z","title":"Learning to Maximize Mutual Information for Chain-of-Thought Distillation","version":3},"cited_work":{"arxiv_id":"2403.03348","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.03348","snapshot_observed_at":"2026-08-07T00:13:42.562561Z","title":"Learning to Maximize Mutual Information for Chain-of-Thought Distillation","venue":"cs.CL","work_id":"006c9cf9-37a1-4f97-9d8e-9ff6ffe78f90","year":2024},"citing_paper":{"arxiv_id":"2506.14728","last_updated":"2025-06-17T17:08:32Z","snapshot_observed_at":"2026-08-13T00:22:10.253455Z","submitted_at":"2025-06-17T17:08:32Z","title":"AgentDistill: Training-Free Agent Distillation with Generalizable MCP Boxes","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T00:13:37.512272Z"},"links":{"cited_paper":"/paper/2403.03348","citing_paper":"/paper/2506.14728"},"observation_digest":"sha256:260ad557eecc037ba88e2118afec38e7cc9231c723f6eda991a44f8e5b22f839","observation_id":"89e91865-13aa-42c3-a132-9c4650471936","resolution":{"observed_at":"2026-08-07T00:13:42.642732Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03348","last_updated":"2024-06-09T14:24:54Z","snapshot_observed_at":"2026-08-13T04:02:39.524255Z","submitted_at":"2024-03-05T22:21:45Z","title":"Learning to Maximize Mutual Information for Chain-of-Thought Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.03348","snapshot_observed_at":"2026-08-04T15:25:34.631211Z","title":"arXiv preprint arXiv:2403.03348(2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.13817","last_updated":"2026-07-08T17:47:16Z","snapshot_observed_at":"2026-08-13T14:24:50.458613Z","submitted_at":"2025-09-24T05:33:48Z","title":"What's on My Network? Using Large Language Models to Identify Real-World IoT Devices at Scale","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-04T15:25:34.631211Z"},"links":{"cited_paper":"/paper/2403.03348","citing_paper":"/paper/2510.13817"},"observation_digest":"sha256:355ec38f407b0d297f106b6e65a4bddd84e14a779027667a0f11af2dfc212ab0","observation_id":"ecb79843-687b-4764-b9b8-0314d3a4a3ff","resolution":{"observed_at":"2026-08-04T15:25:34.631211Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03348","last_updated":"2024-06-09T14:24:54Z","snapshot_observed_at":"2026-08-13T04:02:39.524255Z","submitted_at":"2024-03-05T22:21:45Z","title":"Learning to Maximize Mutual Information for Chain-of-Thought Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.03348","snapshot_observed_at":"2026-08-01T18:49:26.750264Z","title":"arXiv preprint arXiv:2403.03348(2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.17188","last_updated":"2026-07-19T11:00:59Z","snapshot_observed_at":"2026-08-13T20:22:15.952538Z","submitted_at":"2026-07-19T11:00:59Z","title":"Is Your Model Thinking or Just Stagnating? PUMA: Diagnosing Reasoning Pathology via Phase-Momentum Alignment","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-01T18:49:26.750264Z"},"links":{"cited_paper":"/paper/2403.03348","citing_paper":"/paper/2607.17188"},"observation_digest":"sha256:d94c429c5c9bf80225262fa09e62367d7c026e6692feae7d4803b20cf93369ee","observation_id":"7825b69d-f3df-400c-8a72-caf5f1d0c892","resolution":{"observed_at":"2026-08-01T18:49:26.750264Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2403.03348/citation-record","integrity":"/paper/2403.03348/integrity","json":"/paper/2403.03348/citation-record.json","paper":"/paper/2403.03348"},"outbound":[],"paper":{"arxiv_id":"2403.03348","last_updated":"2024-06-09T14:24:54Z","latest_version":3,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-13T04:02:39.524255Z","submitted_at":"2024-03-05T22:21:45Z","title":"Learning to Maximize Mutual Information for Chain-of-Thought Distillation"},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2403.03348."}