{"as_of":"2026-08-19T07:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7e1e147bce574322d21d8b0afd8da6c74f8c2cafcefc360e22fc4ceb24e4ab1e","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T17:57:44.342506Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T10:59:45.797413Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2312.02179","last_updated":"2023-11-28T17:47:32Z","snapshot_observed_at":"2026-08-17T18:54:53.211703Z","submitted_at":"2023-11-28T17:47:32Z","title":"Training Chain-of-Thought via Latent-Variable Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.02179","snapshot_observed_at":"2026-08-11T17:57:44.342506Z","title":"Hoffman, David Dohan, Sholto Douglas, Tuan Anh Le, Aaron Parisi, Pavel Sountsov, Charles Sutton, Sharad Vikram, and Rif A","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.08347","last_updated":"2024-12-11T12:41:36Z","snapshot_observed_at":"2026-08-19T05:13:40.782176Z","submitted_at":"2024-12-11T12:41:36Z","title":"SmolTulu: Higher Learning Rate to Batch Size Ratios Can Lead to Better Reasoning in SLMs","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-11T17:57:44.342506Z"},"links":{"cited_paper":"/paper/2312.02179","citing_paper":"/paper/2412.08347"},"observation_digest":"sha256:099370d428dc22c2131e51f24512ae93cafe691540eb3b0979a97fc03ffe3755","observation_id":"f77ac710-09ba-49a2-bf35-993535cc4845","resolution":{"observed_at":"2026-08-11T17:57:44.342506Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.02179","last_updated":"2023-11-28T17:47:32Z","snapshot_observed_at":"2026-08-17T18:54:53.211703Z","submitted_at":"2023-11-28T17:47:32Z","title":"Training Chain-of-Thought via Latent-Variable Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.02179","snapshot_observed_at":"2026-08-07T12:32:22.326511Z","title":"Hoffman, David Dohan, Sholto Douglas, Tuan Anh Le, Aaron Parisi, Pavel Sountsov, Charles Sutton, Sharad Vikram, and Rif A","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24273","last_updated":"2026-08-09T16:14:48Z","snapshot_observed_at":"2026-08-13T23:25:57.636484Z","submitted_at":"2025-05-30T06:49:00Z","title":"How Much Backtracking is Enough? Exploring the Interplay of SFT and RL in Enhancing LLM Reasoning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T12:32:22.326511Z"},"links":{"cited_paper":"/paper/2312.02179","citing_paper":"/paper/2505.24273"},"observation_digest":"sha256:41e722a720dafe41fc1c5548f1779818d13be3e1bf90d08cad18a061f4cc26f4","observation_id":"121364d5-9d93-4987-bb49-e7e873699f47","resolution":{"observed_at":"2026-08-07T12:32:22.326511Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.02179","last_updated":"2023-11-28T17:47:32Z","snapshot_observed_at":"2026-08-17T18:54:53.211703Z","submitted_at":"2023-11-28T17:47:32Z","title":"Training Chain-of-Thought via Latent-Variable Inference","version":1},"cited_work":{"arxiv_id":"2312.02179","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2312.02179","snapshot_observed_at":"2026-07-04T10:59:45.797413Z","title":"Training Chain-of-Thought via Latent-Variable Inference","venue":null,"work_id":"1dc7e5c9-18d3-4876-b8d7-f3e1919fcb86","year":2023},"citing_paper":{"arxiv_id":"2602.15861","last_updated":"2026-04-22T03:42:41Z","snapshot_observed_at":"2026-08-18T15:27:00.301114Z","submitted_at":"2026-01-26T09:56:31Z","title":"CAST: Achieving Stable LLM-based Text Analysis for Data Analytics","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-16T11:39:13.719923Z"},"links":{"cited_paper":"/paper/2312.02179","citing_paper":"/paper/2602.15861"},"observation_digest":"sha256:0bab0efcfa61ff397343b5ac3fa2f9d0be0730bd5a7a2bfa0e36f597909ffee4","observation_id":"947a1816-a962-4ed0-8c1a-7480558b3505","resolution":{"observed_at":"2026-05-16T11:40:53.175843Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.02179","last_updated":"2023-11-28T17:47:32Z","snapshot_observed_at":"2026-08-17T18:54:53.211703Z","submitted_at":"2023-11-28T17:47:32Z","title":"Training Chain-of-Thought via Latent-Variable Inference","version":1},"cited_work":{"arxiv_id":"2312.02179","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2312.02179","snapshot_observed_at":"2026-07-04T10:59:45.797413Z","title":"Training Chain-of-Thought via Latent-Variable Inference","venue":null,"work_id":"1dc7e5c9-18d3-4876-b8d7-f3e1919fcb86","year":2023},"citing_paper":{"arxiv_id":"2605.10810","last_updated":"2026-05-15T15:01:38Z","snapshot_observed_at":"2026-08-12T16:29:47.459756Z","submitted_at":"2026-05-11T16:32:06Z","title":"Likelihood scoring for continuations of mathematical text: a self-supervised benchmark with tests for shortcut vulnerabilities","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-19T17:20:39.969447Z"},"links":{"cited_paper":"/paper/2312.02179","citing_paper":"/paper/2605.10810"},"observation_digest":"sha256:4a13684d85d410831a23b3c916f3b67fbed12a8ab1f5d757515f7b7860d7f2aa","observation_id":"c380f5f3-a7fd-4121-a9d9-989a848e34bc","resolution":{"observed_at":"2026-05-19T17:22:42.011138Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.02179","last_updated":"2023-11-28T17:47:32Z","snapshot_observed_at":"2026-08-17T18:54:53.211703Z","submitted_at":"2023-11-28T17:47:32Z","title":"Training Chain-of-Thought via Latent-Variable Inference","version":1},"cited_work":{"arxiv_id":"2312.02179","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2312.02179","snapshot_observed_at":"2026-07-04T10:59:45.797413Z","title":"Training Chain-of-Thought via Latent-Variable Inference","venue":null,"work_id":"1dc7e5c9-18d3-4876-b8d7-f3e1919fcb86","year":2023},"citing_paper":{"arxiv_id":"2606.22992","last_updated":"2026-06-22T08:11:35Z","snapshot_observed_at":"2026-08-17T03:34:15.497912Z","submitted_at":"2026-06-22T08:11:35Z","title":"Predicate Importance Estimation and Decoupled Rationale-Score Distillation for Entity Alignment","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-06-26T08:20:40.103291Z"},"links":{"cited_paper":"/paper/2312.02179","citing_paper":"/paper/2606.22992"},"observation_digest":"sha256:1bd1dde72b36649da822a9538d2de569573755dbf15eea0eebef7d84d686420f","observation_id":"40fd3522-5350-416d-b12f-16adab9966e3","resolution":{"observed_at":"2026-07-04T10:59:45.799139Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2312.02179/citation-record","integrity":"/paper/2312.02179/integrity","json":"/paper/2312.02179/citation-record.json","paper":"/paper/2312.02179"},"outbound":[],"paper":{"arxiv_id":"2312.02179","last_updated":"2023-11-28T17:47:32Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T18:54:53.211703Z","submitted_at":"2023-11-28T17:47:32Z","title":"Training Chain-of-Thought via Latent-Variable Inference"},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2312.02179."}