{"as_of":"2026-08-15T22:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:429ff2424f46c42ca4a8288e6930bcd29465c88002d3ef291674163843e62f3f","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T10:58:18.292106Z","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":"2026-05-11T20:36:09.448730Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2306.15063","last_updated":"2023-11-08T18:12:03Z","snapshot_observed_at":"2026-08-13T11:08:53.773366Z","submitted_at":"2023-06-26T21:05:20Z","title":"Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.15063","snapshot_observed_at":"2026-08-12T10:58:18.292106Z","title":"Pretraining task diversity and the emergence of non-bayesian in-context learning for regression","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.00104","last_updated":"2024-12-12T16:10:51Z","snapshot_observed_at":"2026-08-15T10:33:35.715149Z","submitted_at":"2024-11-27T22:12:29Z","title":"Differential learning kinetics govern the transition from memorization to generalization during in-context learning","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T10:58:18.292106Z"},"links":{"cited_paper":"/paper/2306.15063","citing_paper":"/paper/2412.00104"},"observation_digest":"sha256:f7d3705f8301686aa31a35f56d893db89e06a2fdcf9114b4e7ee12552d3da6fe","observation_id":"d30c2c46-d72c-4084-ac9d-b27a64b68b46","resolution":{"observed_at":"2026-08-12T10:58:18.292106Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.15063","last_updated":"2023-11-08T18:12:03Z","snapshot_observed_at":"2026-08-13T11:08:53.773366Z","submitted_at":"2023-06-26T21:05:20Z","title":"Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.15063","snapshot_observed_at":"2026-08-09T17:09:15.379020Z","title":"Pretraining task diversity and the emergence of non-bayesian in-context learning for regression, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.01697","last_updated":"2025-05-21T17:50:43Z","snapshot_observed_at":"2026-08-14T14:40:58.119601Z","submitted_at":"2025-02-03T00:12:40Z","title":"BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation","version":3},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-09T17:09:15.379020Z"},"links":{"cited_paper":"/paper/2306.15063","citing_paper":"/paper/2502.01697"},"observation_digest":"sha256:b3a190299d31ac9c913ab2e0fcaa1de465436542b4e50b2ecab056625f67d309","observation_id":"e102adaa-4e9b-4eef-9476-d01d69925c11","resolution":{"observed_at":"2026-08-09T17:09:15.379020Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.15063","last_updated":"2023-11-08T18:12:03Z","snapshot_observed_at":"2026-08-13T11:08:53.773366Z","submitted_at":"2023-06-26T21:05:20Z","title":"Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression","version":2},"cited_work":{"arxiv_id":"2306.15063","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.15063","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"a8ab5376-f49f-4919-b8c6-a73f36d3a917","year":2023},"citing_paper":{"arxiv_id":"2604.23371","last_updated":"2026-04-25T16:35:25Z","snapshot_observed_at":"2026-07-06T23:09:38.799345Z","submitted_at":"2026-04-25T16:35:25Z","title":"When Context Sticks: Studying Interference in In-Context Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-08T08:31:14.231710Z"},"links":{"cited_paper":"/paper/2306.15063","citing_paper":"/paper/2604.23371"},"observation_digest":"sha256:1504bdcbbec9c83e41f0aa76a1ef56aef6915d881a8b7d83b1aee7fef9762dad","observation_id":"d54542d1-ddb7-4b7a-9e9b-06dde46c850c","resolution":{"observed_at":"2026-05-11T20:36:09.453225Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2306.15063/citation-record","integrity":"/paper/2306.15063/integrity","json":"/paper/2306.15063/citation-record.json","paper":"/paper/2306.15063"},"outbound":[],"paper":{"arxiv_id":"2306.15063","last_updated":"2023-11-08T18:12:03Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T11:08:53.773366Z","submitted_at":"2023-06-26T21:05:20Z","title":"Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression"},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2306.15063."}