{"as_of":"2026-08-18T06:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b8f83334514d8767601426bcc88609fd263cbd2e2f25f266a07637bb766b0e55","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":"2026-08-17T06:30:58.91139+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":"2026-08-16T12:27:30.386240Z","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":"2026-06-28T23:42:49.909870Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.02167","last_updated":"2025-05-25T07:45:42Z","snapshot_observed_at":"2026-08-17T15:57:40.683282Z","submitted_at":"2024-10-03T03:12:51Z","title":"Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.02167","snapshot_observed_at":"2026-08-16T12:27:30.386240Z","title":"Training nonlinear transformers for chain-of-thought inference: A theoretical generalization analysis","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.12991","last_updated":"2025-05-30T14:14:36Z","snapshot_observed_at":"2026-08-17T22:23:56.499791Z","submitted_at":"2025-04-17T14:59:29Z","title":"A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-16T12:27:30.386240Z"},"links":{"cited_paper":"/paper/2410.02167","citing_paper":"/paper/2504.12991"},"observation_digest":"sha256:4beb98d8fd4425f73ec9ffd77b99403c31addb31df8b43417cbe4e654385faed","observation_id":"c7c79e20-81ab-452a-9b04-425cc2007592","resolution":{"observed_at":"2026-08-16T12:27:30.386240Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02167","last_updated":"2025-05-25T07:45:42Z","snapshot_observed_at":"2026-08-17T15:57:40.683282Z","submitted_at":"2024-10-03T03:12:51Z","title":"Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.02167","snapshot_observed_at":"2026-08-16T04:39:55.057589Z","title":"Training nonlinear transformers for chain-of-thought inference: A theoretical generalization analysis","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00926","last_updated":"2025-05-28T23:17:46Z","snapshot_observed_at":"2026-08-16T23:22:20.646457Z","submitted_at":"2025-05-02T00:07:35Z","title":"How Transformers Learn Regular Language Recognition: A Theoretical Study on Training Dynamics and Implicit Bias","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T04:39:55.057589Z"},"links":{"cited_paper":"/paper/2410.02167","citing_paper":"/paper/2505.00926"},"observation_digest":"sha256:d4e521752994991f2ddd625748adf586bc09e3e584589e503067ffb9ae077f45","observation_id":"a87cda34-ab35-4ad1-91e6-5dfdb3acde01","resolution":{"observed_at":"2026-08-16T04:39:55.057589Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02167","last_updated":"2025-05-25T07:45:42Z","snapshot_observed_at":"2026-08-17T15:57:40.683282Z","submitted_at":"2024-10-03T03:12:51Z","title":"Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.02167","snapshot_observed_at":"2026-08-04T13:29:31.110979Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.00399","last_updated":"2026-07-07T00:49:52Z","snapshot_observed_at":"2026-08-14T17:41:09.541667Z","submitted_at":"2025-10-01T01:25:01Z","title":"How Can Mamba Learn In Context with Outliers and Generalize Provably?","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T13:29:31.110979Z"},"links":{"cited_paper":"/paper/2410.02167","citing_paper":"/paper/2510.00399"},"observation_digest":"sha256:8a924d832c63465cfe95c851a7657f1062d445883f51a52a04ac11eafafaac7a","observation_id":"ad87d678-5dd5-4b33-b52d-34cff4bcd6ea","resolution":{"observed_at":"2026-08-04T13:29:31.110979Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02167","last_updated":"2025-05-25T07:45:42Z","snapshot_observed_at":"2026-08-17T15:57:40.683282Z","submitted_at":"2024-10-03T03:12:51Z","title":"Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis","version":3},"cited_work":{"arxiv_id":"2410.02167","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.02167","snapshot_observed_at":"2026-06-28T23:42:49.909870Z","title":"arXiv preprint arXiv:2410.02167 , year=","venue":null,"work_id":"f692bcfa-1518-4e48-949b-d102a5483b1c","year":null},"citing_paper":{"arxiv_id":"2606.00183","last_updated":"2026-05-29T14:58:03Z","snapshot_observed_at":"2026-08-13T00:33:18.445193Z","submitted_at":"2026-05-29T14:58:03Z","title":"Agentic Transformers Provably Learn to Search via Reinforcement Learning","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-06-28T23:26:28.158991Z"},"links":{"cited_paper":"/paper/2410.02167","citing_paper":"/paper/2606.00183"},"observation_digest":"sha256:6ba92a491b3efac1a3cf394394c386fe86897e45bdec691a49e792b4694f6a5c","observation_id":"39c00a81-aed5-45f4-ab03-fa863ea8ead9","resolution":{"observed_at":"2026-06-28T23:42:49.911181Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2410.02167/citation-record","integrity":"/paper/2410.02167/integrity","json":"/paper/2410.02167/citation-record.json","paper":"/paper/2410.02167"},"outbound":[],"paper":{"arxiv_id":"2410.02167","last_updated":"2025-05-25T07:45:42Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T15:57:40.683282Z","submitted_at":"2024-10-03T03:12:51Z","title":"Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis"},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2410.02167."}