{"as_of":"2026-08-10T22:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d657ed18aad3f4bdd57b9916cad5bd14f1df58b3e75c0523b72a617cdecfc8cd","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":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T17:37:40.653083Z","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":"2026-06-28T23:42:49.971036Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2408.14511","last_updated":"2024-08-28T14:13:41Z","snapshot_observed_at":"2026-08-10T18:20:53.238548Z","submitted_at":"2024-08-25T04:07:18Z","title":"Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.14511","snapshot_observed_at":"2026-08-09T17:37:40.653083Z","title":"Unveiling the statistical foundations of chain-of-thought prompting methods","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.01694","last_updated":"2025-03-01T10:27:24Z","snapshot_observed_at":"2026-08-09T20:41:13.212163Z","submitted_at":"2025-02-02T18:19:14Z","title":"Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-09T17:37:40.653083Z"},"links":{"cited_paper":"/paper/2408.14511","citing_paper":"/paper/2502.01694"},"observation_digest":"sha256:09451f2fccf958ad90daba7dc3574b47c23087955e020ab3e32c27aebb83c600","observation_id":"5a07c5ea-c178-4f41-9fb7-c849e8acde89","resolution":{"observed_at":"2026-08-09T17:37:40.653083Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.14511","last_updated":"2024-08-28T14:13:41Z","snapshot_observed_at":"2026-08-10T18:20:53.238548Z","submitted_at":"2024-08-25T04:07:18Z","title":"Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.14511","snapshot_observed_at":"2026-08-07T15:22:13.111877Z","title":"Efficient Learning of Typical Finite Automata from Random Walks","venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"2505.15927","last_updated":"2025-05-21T18:28:54Z","snapshot_observed_at":"2026-08-07T15:08:15.075772Z","submitted_at":"2025-05-21T18:28:54Z","title":"CoT Information: Improved Sample Complexity under Chain-of-Thought Supervision","version":1},"reference_index":2305,"source":"pdf_text","source_observed_at":"2026-08-07T15:22:13.111877Z"},"links":{"cited_paper":"/paper/2408.14511","citing_paper":"/paper/2505.15927"},"observation_digest":"sha256:52e804863fdbfcb72d2efacdffbee1c3df66cfbe32aa727ce616007293140660","observation_id":"991ff95d-50e5-4e9e-9e51-95dfff229baf","resolution":{"observed_at":"2026-08-07T15:22:13.111877Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.14511","last_updated":"2024-08-28T14:13:41Z","snapshot_observed_at":"2026-08-10T18:20:53.238548Z","submitted_at":"2024-08-25T04:07:18Z","title":"Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.14511","snapshot_observed_at":"2026-08-05T22:10:17.474987Z","title":", Zhang, F","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.07571","last_updated":"2025-08-19T11:54:07Z","snapshot_observed_at":"2026-08-07T21:59:26.623348Z","submitted_at":"2025-08-11T03:05:36Z","title":"Towards Theoretical Understanding of Transformer Test-Time Computing: Investigation on In-Context Linear Regression","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-05T22:10:17.474987Z"},"links":{"cited_paper":"/paper/2408.14511","citing_paper":"/paper/2508.07571"},"observation_digest":"sha256:053296508b780ee7671f10eab03ec56252f156b4d3ac995e74b222f3d37a68b9","observation_id":"e39cf4a8-d83e-42c7-ae6d-5d18a02680d0","resolution":{"observed_at":"2026-08-05T22:10:17.474987Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.14511","last_updated":"2024-08-28T14:13:41Z","snapshot_observed_at":"2026-08-10T18:20:53.238548Z","submitted_at":"2024-08-25T04:07:18Z","title":"Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.14511","snapshot_observed_at":"2026-08-03T22:09:27.590448Z","title":"Baihe Huang, Shanda Li, Tianhao Wu, Yiming Yang, Ameet Talwalkar, Kannan Ramchandran, Michael I","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.12309","last_updated":"2026-06-30T05:35:04Z","snapshot_observed_at":"2026-08-10T10:02:52.834910Z","submitted_at":"2025-11-15T17:45:42Z","title":"Optimal Self-Consistency for Efficient Reasoning with Large Language Models","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-03T22:09:27.590448Z"},"links":{"cited_paper":"/paper/2408.14511","citing_paper":"/paper/2511.12309"},"observation_digest":"sha256:d083bcc2cce8a422ce5b5e1f0dfceca1263423d243e203b5dde383e82023245a","observation_id":"d8c87431-3a4a-4265-a19f-cbd086685744","resolution":{"observed_at":"2026-08-03T22:09:27.590448Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.14511","last_updated":"2024-08-28T14:13:41Z","snapshot_observed_at":"2026-08-10T18:20:53.238548Z","submitted_at":"2024-08-25T04:07:18Z","title":"Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.14511","snapshot_observed_at":"2026-08-03T20:57:10.684434Z","title":"Unveiling the statistical foundations of chain-of-thought prompting methods, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.17852","last_updated":"2026-08-06T09:30:47Z","snapshot_observed_at":"2026-08-09T23:09:22.568411Z","submitted_at":"2025-11-22T00:38:43Z","title":"Transformers with RL or SFT Provably Learn Sparse Boolean Functions, But Differently","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-03T20:57:10.684434Z"},"links":{"cited_paper":"/paper/2408.14511","citing_paper":"/paper/2511.17852"},"observation_digest":"sha256:c4bf20c29975d3f65f1f4bd239e21e79562b26532f214f6222cf97f999bf8306","observation_id":"07b6708d-f5a0-471d-a5d6-9402c6712df4","resolution":{"observed_at":"2026-08-03T20:57:10.684434Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.14511","last_updated":"2024-08-28T14:13:41Z","snapshot_observed_at":"2026-08-10T18:20:53.238548Z","submitted_at":"2024-08-25T04:07:18Z","title":"Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods","version":2},"cited_work":{"arxiv_id":"2408.14511","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.14511","snapshot_observed_at":"2026-06-28T23:42:49.971036Z","title":"arXiv preprint arXiv:2408.14511 , year=","venue":null,"work_id":"f0690bab-7d39-4c94-9809-cdfd967dcd98","year":2024},"citing_paper":{"arxiv_id":"2605.21260","last_updated":"2026-05-20T14:51:20Z","snapshot_observed_at":"2026-07-29T20:35:26.564819Z","submitted_at":"2026-05-20T14:51:20Z","title":"On the Cost and Benefit of Chain of Thought: A Learning-Theoretic Perspective","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-21T05:09:37.588841Z"},"links":{"cited_paper":"/paper/2408.14511","citing_paper":"/paper/2605.21260"},"observation_digest":"sha256:a47a5779d289768aefe286a56d8ff388eea24b6330909bbb51e0515c5b3d556e","observation_id":"410f35f2-f8a6-49ec-96b6-d874071044b8","resolution":{"observed_at":"2026-05-21T05:13:58.670659Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.14511","last_updated":"2024-08-28T14:13:41Z","snapshot_observed_at":"2026-08-10T18:20:53.238548Z","submitted_at":"2024-08-25T04:07:18Z","title":"Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods","version":2},"cited_work":{"arxiv_id":"2408.14511","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.14511","snapshot_observed_at":"2026-06-28T23:42:49.971036Z","title":"arXiv preprint arXiv:2408.14511 , year=","venue":null,"work_id":"f0690bab-7d39-4c94-9809-cdfd967dcd98","year":2024},"citing_paper":{"arxiv_id":"2606.00183","last_updated":"2026-05-29T14:58:03Z","snapshot_observed_at":"2026-07-06T23:40:56.510371Z","submitted_at":"2026-05-29T14:58:03Z","title":"Agentic Transformers Provably Learn to Search via Reinforcement Learning","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-06-28T23:26:28.158991Z"},"links":{"cited_paper":"/paper/2408.14511","citing_paper":"/paper/2606.00183"},"observation_digest":"sha256:193c7b423c4826d248239a1c1f1795ae22c3f049dc7ff107551be4864232261b","observation_id":"b50d5328-d296-42fc-8b19-c1f56eebd572","resolution":{"observed_at":"2026-06-28T23:42:49.972232Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.14511","last_updated":"2024-08-28T14:13:41Z","snapshot_observed_at":"2026-08-10T18:20:53.238548Z","submitted_at":"2024-08-25T04:07:18Z","title":"Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.14511","snapshot_observed_at":"2026-08-02T02:24:36.178641Z","title":"arXiv preprint arXiv:2408.14511 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.14371","last_updated":"2026-07-15T21:17:42Z","snapshot_observed_at":"2026-08-07T13:04:16.701334Z","submitted_at":"2026-07-15T21:17:42Z","title":"Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-02T02:24:36.178641Z"},"links":{"cited_paper":"/paper/2408.14511","citing_paper":"/paper/2607.14371"},"observation_digest":"sha256:234e656c6acaa3386d411ebf80a32f4e046952e5301b6946084384ff8f4181d5","observation_id":"d5d415da-b896-4aa1-a52d-ff78dba5072d","resolution":{"observed_at":"2026-08-02T02:24:36.178641Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2408.14511/citation-record","integrity":"/paper/2408.14511/integrity","json":"/paper/2408.14511/citation-record.json","paper":"/paper/2408.14511"},"outbound":[],"paper":{"arxiv_id":"2408.14511","last_updated":"2024-08-28T14:13:41Z","latest_version":2,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-10T18:20:53.238548Z","submitted_at":"2024-08-25T04:07:18Z","title":"Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2408.14511."}