{"as_of":"2026-08-09T11:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:73705ab05ff83de1217305124e50acfb82b23b6d2c683b605b1806b71e880870","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":"2026-08-09T06:31:02.800959+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":"2026-08-08T16:39:14.118607Z","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":"2026-05-20T23:23:51.654192Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.11268","last_updated":"2025-02-28T22:12:33Z","snapshot_observed_at":"2026-07-06T19:33:34.819837Z","submitted_at":"2024-10-15T04:44:23Z","title":"Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.11268","snapshot_observed_at":"2026-08-08T16:39:14.118607Z","title":"Bypassing the exponential dependency: Looped transformers efficiently learn in-context by multi- step gradient descent","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.06167","last_updated":"2025-02-10T05:36:30Z","snapshot_observed_at":"2026-08-09T01:42:08.911908Z","submitted_at":"2025-02-10T05:36:30Z","title":"Universal Approximation of Visual Autoregressive Transformers","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T16:39:14.118607Z"},"links":{"cited_paper":"/paper/2410.11268","citing_paper":"/paper/2502.06167"},"observation_digest":"sha256:19ba20a1391c46538173f73ff169d2009437b87c5a8e8ad1f718c352eb8da481","observation_id":"476ef222-2196-4878-8eec-db59a372e755","resolution":{"observed_at":"2026-08-08T16:39:14.118607Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.11268","last_updated":"2025-02-28T22:12:33Z","snapshot_observed_at":"2026-07-06T19:33:34.819837Z","submitted_at":"2024-10-15T04:44:23Z","title":"Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.11268","snapshot_observed_at":"2026-08-06T19:14:22.857140Z","title":"Bypassing the exponential dependency: Looped transformers efficiently learn in-context by multi-step gradient descent","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06203","last_updated":"2025-07-10T16:43:36Z","snapshot_observed_at":"2026-08-07T04:57:37.201438Z","submitted_at":"2025-07-08T17:29:07Z","title":"A Survey on Latent Reasoning","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-06T19:14:22.857140Z"},"links":{"cited_paper":"/paper/2410.11268","citing_paper":"/paper/2507.06203"},"observation_digest":"sha256:f3de416c721d3c0967d4ea6e51b52fe86cd5677523f598e4c3d38d90fc42aa61","observation_id":"362eea60-591f-4fab-8478-510d067a27f9","resolution":{"observed_at":"2026-08-06T19:14:22.857140Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.11268","last_updated":"2025-02-28T22:12:33Z","snapshot_observed_at":"2026-07-06T19:33:34.819837Z","submitted_at":"2024-10-15T04:44:23Z","title":"Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent","version":2},"cited_work":{"arxiv_id":"2410.11268","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.11268","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2410.11268 , year=","venue":null,"work_id":"171c864a-c091-4258-bff6-583a186bdbec","year":null},"citing_paper":{"arxiv_id":"2605.18797","last_updated":"2026-05-25T04:21:17Z","snapshot_observed_at":"2026-08-02T05:53:57.809613Z","submitted_at":"2026-05-11T07:21:53Z","title":"Simply Stabilizing the Loop via Fully Looped Transformer","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-05-20T23:19:28.027625Z"},"links":{"cited_paper":"/paper/2410.11268","citing_paper":"/paper/2605.18797"},"observation_digest":"sha256:aa56bff2ad95a207ff67a2db56226faf7ab6d6cdcaad39cc7ac7da4864df9dcb","observation_id":"c58ff7cf-4744-4c63-9628-d0183ad75873","resolution":{"observed_at":"2026-05-20T23:23:51.678015Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2410.11268/citation-record","integrity":"/paper/2410.11268/integrity","json":"/paper/2410.11268/citation-record.json","paper":"/paper/2410.11268"},"outbound":[],"paper":{"arxiv_id":"2410.11268","last_updated":"2025-02-28T22:12:33Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T19:33:34.819837Z","submitted_at":"2024-10-15T04:44:23Z","title":"Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2410.11268."}