{"as_of":"2026-08-17T08:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6eb35a45f20927f880221cd0c6f13bca7053461809d04f064cecc97b97ad1f66","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T12:27:30.485255Z","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":"2026-07-04T20:00:08.094306Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.09695","last_updated":"2024-12-04T15:35:48Z","snapshot_observed_at":"2026-08-16T13:09:58.672761Z","submitted_at":"2024-10-13T02:10:26Z","title":"Can In-context Learning Really Generalize to Out-of-distribution Tasks?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.09695","snapshot_observed_at":"2026-08-16T12:27:30.485255Z","title":"Can in-context learning really generalize to out-of-distribution tasks? arXiv preprint arXiv:2410.09695, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.12991","last_updated":"2025-05-30T14:14:36Z","snapshot_observed_at":"2026-08-16T12:15:21.847711Z","submitted_at":"2025-04-17T14:59:29Z","title":"A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-16T12:27:30.485255Z"},"links":{"cited_paper":"/paper/2410.09695","citing_paper":"/paper/2504.12991"},"observation_digest":"sha256:67411ef0ba5c9fb410a76adc88f61ca41017605307d4e3d27af6555a7376e603","observation_id":"665a47ba-5259-4c1b-ac05-e93b44a2fe83","resolution":{"observed_at":"2026-08-16T12:27:30.485255Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09695","last_updated":"2024-12-04T15:35:48Z","snapshot_observed_at":"2026-08-16T13:09:58.672761Z","submitted_at":"2024-10-13T02:10:26Z","title":"Can In-context Learning Really Generalize to Out-of-distribution Tasks?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.09695","snapshot_observed_at":"2026-08-07T11:17:41.632679Z","title":"Can in-context learning really generalize to out-of-distribution tasks? arXiv preprint arXiv:2410.09695, 2024 b","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.03100","last_updated":"2025-06-09T10:35:22Z","snapshot_observed_at":"2026-08-09T22:05:53.415139Z","submitted_at":"2025-06-03T17:31:53Z","title":"Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds","version":3},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-07T11:17:41.632679Z"},"links":{"cited_paper":"/paper/2410.09695","citing_paper":"/paper/2506.03100"},"observation_digest":"sha256:85bf64ef35dfa923701980aae52c9d8795315be93f56d31ee376a11325270275","observation_id":"f5346b35-224c-428d-bca0-fbc0dd17e265","resolution":{"observed_at":"2026-08-07T11:17:41.632679Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09695","last_updated":"2024-12-04T15:35:48Z","snapshot_observed_at":"2026-08-16T13:09:58.672761Z","submitted_at":"2024-10-13T02:10:26Z","title":"Can In-context Learning Really Generalize to Out-of-distribution Tasks?","version":3},"cited_work":{"arxiv_id":"2410.09695","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.09695","snapshot_observed_at":"2026-07-04T20:00:08.094306Z","title":"Canin-contextlearningreallygeneralizetoout-of-distribution tasks? arXiv preprint arXiv:2410.09695","venue":null,"work_id":"6edab345-f102-4467-b277-21f0f1c8a269","year":2024},"citing_paper":{"arxiv_id":"2508.01191","last_updated":"2026-05-08T20:20:58Z","snapshot_observed_at":"2026-07-06T22:06:38.785781Z","submitted_at":"2025-08-02T04:37:28Z","title":"Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens","version":6},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-19T01:18:31.661827Z"},"links":{"cited_paper":"/paper/2410.09695","citing_paper":"/paper/2508.01191"},"observation_digest":"sha256:0bfbf04c7897396b6b442b318acbdc162e94a234a51b1ed7043d6ececef975d1","observation_id":"8a9e324d-693e-4a16-b338-011ea706baea","resolution":{"observed_at":"2026-05-19T01:21:58.039341Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09695","last_updated":"2024-12-04T15:35:48Z","snapshot_observed_at":"2026-08-16T13:09:58.672761Z","submitted_at":"2024-10-13T02:10:26Z","title":"Can In-context Learning Really Generalize to Out-of-distribution Tasks?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.09695","snapshot_observed_at":"2026-08-04T17:46:56.310784Z","title":"Can In-context Learning Really Generalize to Out-of-distribution Tasks?arXiv preprint arXiv:2410.09695, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.12263","last_updated":"2026-05-31T19:40:08Z","snapshot_observed_at":"2026-08-14T14:12:12.376830Z","submitted_at":"2025-09-12T20:07:12Z","title":"InPhyRe Discovers: Large Multimodal Models Struggle in Inductive Physical Reasoning","version":3},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-04T17:46:56.310784Z"},"links":{"cited_paper":"/paper/2410.09695","citing_paper":"/paper/2509.12263"},"observation_digest":"sha256:b001b29017a9b725a312abe2313415f9649a16c32ced66476ced3ff5aa0fd8c1","observation_id":"e740b62d-97b7-46b4-af5a-0c785c2c3be9","resolution":{"observed_at":"2026-08-04T17:46:56.310784Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09695","last_updated":"2024-12-04T15:35:48Z","snapshot_observed_at":"2026-08-16T13:09:58.672761Z","submitted_at":"2024-10-13T02:10:26Z","title":"Can In-context Learning Really Generalize to Out-of-distribution Tasks?","version":3},"cited_work":{"arxiv_id":"2410.09695","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.09695","snapshot_observed_at":"2026-07-04T20:00:08.094306Z","title":"Canin-contextlearningreallygeneralizetoout-of-distribution tasks? arXiv preprint arXiv:2410.09695","venue":null,"work_id":"6edab345-f102-4467-b277-21f0f1c8a269","year":2024},"citing_paper":{"arxiv_id":"2606.25450","last_updated":"2026-06-25T16:37:45Z","snapshot_observed_at":"2026-08-15T00:00:21.528560Z","submitted_at":"2026-06-24T06:26:02Z","title":"The Generalization Spectrum: A Chromatographic Approach to Evaluating Learning Algorithms","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-25T20:55:15.784610Z"},"links":{"cited_paper":"/paper/2410.09695","citing_paper":"/paper/2606.25450"},"observation_digest":"sha256:0187d6274409a78c17d8a971d8555b775fec40299da4318e57e58d674377c559","observation_id":"0c1f1f29-163e-4083-b42f-2989f12a068d","resolution":{"observed_at":"2026-07-04T20:00:08.096417Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09695","last_updated":"2024-12-04T15:35:48Z","snapshot_observed_at":"2026-08-16T13:09:58.672761Z","submitted_at":"2024-10-13T02:10:26Z","title":"Can In-context Learning Really Generalize to Out-of-distribution Tasks?","version":3},"cited_work":{"arxiv_id":"2410.09695","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.09695","snapshot_observed_at":"2026-07-04T20:00:08.094306Z","title":"Canin-contextlearningreallygeneralizetoout-of-distribution tasks? arXiv preprint arXiv:2410.09695","venue":null,"work_id":"6edab345-f102-4467-b277-21f0f1c8a269","year":2024},"citing_paper":{"arxiv_id":"2606.25450","last_updated":"2026-06-25T16:37:45Z","snapshot_observed_at":"2026-08-15T00:00:21.528560Z","submitted_at":"2026-06-24T06:26:02Z","title":"The Generalization Spectrum: A Chromatographic Approach to Evaluating Learning Algorithms","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-26T05:29:21.598397Z"},"links":{"cited_paper":"/paper/2410.09695","citing_paper":"/paper/2606.25450"},"observation_digest":"sha256:63267680adc0f01941ace37c9e41187f02e20475eb32871e88ff3d227eee1494","observation_id":"f463e849-8f7c-487a-b9c6-be49e606206e","resolution":{"observed_at":"2026-07-04T13:09:50.581497Z","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/2410.09695/citation-record","integrity":"/paper/2410.09695/integrity","json":"/paper/2410.09695/citation-record.json","paper":"/paper/2410.09695"},"outbound":[],"paper":{"arxiv_id":"2410.09695","last_updated":"2024-12-04T15:35:48Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T13:09:58.672761Z","submitted_at":"2024-10-13T02:10:26Z","title":"Can In-context Learning Really Generalize to Out-of-distribution Tasks?"},"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 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2410.09695."}