{"as_of":"2026-08-09T16:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8e7ad01673c708c22f230e66e223d096a5271526e489872b02306c08538dfccf","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":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:16:53.975628Z","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":"2026-05-12T06:01:24.790388Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2401.14953","last_updated":"2024-01-26T15:37:16Z","snapshot_observed_at":"2026-07-06T17:20:57.937385Z","submitted_at":"2024-01-26T15:37:16Z","title":"Learning Universal Predictors","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14953","snapshot_observed_at":"2026-08-07T15:16:53.975628Z","title":"Learning universal predictors","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15784","last_updated":"2025-05-21T17:35:08Z","snapshot_observed_at":"2026-08-08T17:38:59.010547Z","submitted_at":"2025-05-21T17:35:08Z","title":"Large Language Models as Computable Approximations to Solomonoff Induction","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T15:16:53.975628Z"},"links":{"cited_paper":"/paper/2401.14953","citing_paper":"/paper/2505.15784"},"observation_digest":"sha256:072356ca5bbc1bf10560d1c59b6834981fa873fec742cdb0078b927abdcfc1e4","observation_id":"81c8af4e-bd3d-4810-85cf-e5d9d5c1edc5","resolution":{"observed_at":"2026-08-07T15:16:53.975628Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14953","last_updated":"2024-01-26T15:37:16Z","snapshot_observed_at":"2026-07-06T17:20:57.937385Z","submitted_at":"2024-01-26T15:37:16Z","title":"Learning Universal Predictors","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14953","snapshot_observed_at":"2026-08-07T13:14:25.350545Z","title":"K., Mattern, C., Aitchison, M., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.22308","last_updated":"2025-05-28T12:50:09Z","snapshot_observed_at":"2026-08-07T23:56:23.918643Z","submitted_at":"2025-05-28T12:50:09Z","title":"Transformers Pretrained on Procedural Data Contain Modular Structures for Algorithmic Reasoning","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-07T13:14:25.350545Z"},"links":{"cited_paper":"/paper/2401.14953","citing_paper":"/paper/2505.22308"},"observation_digest":"sha256:e4e9bbb5025381adbd4ab7dc1ce972b3356ee2c47e50ec1c90b49ed4f07f2337","observation_id":"86706279-418f-4f33-829f-e5aa1726c826","resolution":{"observed_at":"2026-08-07T13:14:25.350545Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14953","last_updated":"2024-01-26T15:37:16Z","snapshot_observed_at":"2026-07-06T17:20:57.937385Z","submitted_at":"2024-01-26T15:37:16Z","title":"Learning Universal Predictors","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14953","snapshot_observed_at":"2026-08-06T23:48:39.492242Z","title":"K., Mat- tern, C., Aitchison, M., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.16288","last_updated":"2025-06-19T13:05:12Z","snapshot_observed_at":"2026-08-09T14:07:03.467744Z","submitted_at":"2025-06-19T13:05:12Z","title":"Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T23:48:39.492242Z"},"links":{"cited_paper":"/paper/2401.14953","citing_paper":"/paper/2506.16288"},"observation_digest":"sha256:6ec7ec6d5bde88d98c4d2c7e4b986c7d14e3f963efa6d95489d3515c2962ee10","observation_id":"b98ce003-2956-457d-a2d4-3bb0cfe13fbf","resolution":{"observed_at":"2026-08-06T23:48:39.492242Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14953","last_updated":"2024-01-26T15:37:16Z","snapshot_observed_at":"2026-07-06T17:20:57.937385Z","submitted_at":"2024-01-26T15:37:16Z","title":"Learning Universal Predictors","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14953","snapshot_observed_at":"2026-08-03T06:55:10.064992Z","title":"Learning uni- versal predictors.arXiv preprint arXiv:2401.14953,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.21725","last_updated":"2026-05-27T19:52:53Z","snapshot_observed_at":"2026-08-04T21:23:57.701786Z","submitted_at":"2026-01-29T13:48:43Z","title":"Procedural Pretraining: Warming Up Language Models with Abstract Data","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T06:55:10.064992Z"},"links":{"cited_paper":"/paper/2401.14953","citing_paper":"/paper/2601.21725"},"observation_digest":"sha256:1fed79793bce37f3e4f1595e29ff99260d80ab97e4fd967d9b93d896711251f5","observation_id":"11141277-a8a9-48be-b338-b1b224767ca5","resolution":{"observed_at":"2026-08-03T06:55:10.064992Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14953","last_updated":"2024-01-26T15:37:16Z","snapshot_observed_at":"2026-07-06T17:20:57.937385Z","submitted_at":"2024-01-26T15:37:16Z","title":"Learning Universal Predictors","version":1},"cited_work":{"arxiv_id":"2401.14953","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.14953","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning universal predictors","venue":null,"work_id":"6273e21b-f5d1-4052-871f-ccdb7a5c6b7d","year":2024},"citing_paper":{"arxiv_id":"2605.05115","last_updated":"2026-05-06T16:46:03Z","snapshot_observed_at":"2026-08-07T10:12:24.411121Z","submitted_at":"2026-05-06T16:46:03Z","title":"Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior","version":1},"reference_index":247,"source":"arxiv_source","source_observed_at":"2026-05-08T17:47:09.591001Z"},"links":{"cited_paper":"/paper/2401.14953","citing_paper":"/paper/2605.05115"},"observation_digest":"sha256:9d1f9e88fcd41cf275ada202f6b5117456bbb7d3e5f641815e4a469ba0a02f96","observation_id":"875c997c-67a6-492e-8775-c158ff5fd22f","resolution":{"observed_at":"2026-05-11T17:16:06.861969Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14953","last_updated":"2024-01-26T15:37:16Z","snapshot_observed_at":"2026-07-06T17:20:57.937385Z","submitted_at":"2024-01-26T15:37:16Z","title":"Learning Universal Predictors","version":1},"cited_work":{"arxiv_id":"2401.14953","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.14953","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning universal predictors","venue":null,"work_id":"6273e21b-f5d1-4052-871f-ccdb7a5c6b7d","year":2024},"citing_paper":{"arxiv_id":"2605.10878","last_updated":"2026-05-11T17:27:31Z","snapshot_observed_at":"2026-07-06T23:22:47.781940Z","submitted_at":"2026-05-11T17:27:31Z","title":"Neural Weight Norm = Kolmogorov Complexity","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-12T04:41:27.219489Z"},"links":{"cited_paper":"/paper/2401.14953","citing_paper":"/paper/2605.10878"},"observation_digest":"sha256:eb3ba3c3f18349c34e49bdb98a9f0c6765d711d8e5a7c2fa65d0d1ea80e55aa6","observation_id":"a0376b95-1805-41f7-a11a-b7bd164f5e57","resolution":{"observed_at":"2026-05-12T06:01:24.794585Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14953","last_updated":"2024-01-26T15:37:16Z","snapshot_observed_at":"2026-07-06T17:20:57.937385Z","submitted_at":"2024-01-26T15:37:16Z","title":"Learning Universal Predictors","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14953","snapshot_observed_at":"2026-08-06T00:53:43.006141Z","title":"Proceedings of Machine Learning Research , keywords =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.01005","last_updated":"2026-08-02T05:22:30Z","snapshot_observed_at":"2026-08-08T01:27:54.317708Z","submitted_at":"2026-08-02T05:22:30Z","title":"Hierarchical Solomonoff Induction: An Unbounded Machine Learning Model","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T00:53:43.006141Z"},"links":{"cited_paper":"/paper/2401.14953","citing_paper":"/paper/2608.01005"},"observation_digest":"sha256:4051cebdcd88ed3fe04d517cb4ca4da42fd0a490e787157a20e9ff2b701fc884","observation_id":"c63a9051-95d0-4240-a218-ad26f42ee7b0","resolution":{"observed_at":"2026-08-06T00:53:43.006141Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2401.14953/citation-record","integrity":"/paper/2401.14953/integrity","json":"/paper/2401.14953/citation-record.json","paper":"/paper/2401.14953"},"outbound":[],"paper":{"arxiv_id":"2401.14953","last_updated":"2024-01-26T15:37:16Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T17:20:57.937385Z","submitted_at":"2024-01-26T15:37:16Z","title":"Learning Universal Predictors"},"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 7 inbound Pith citation observations for arXiv:2401.14953."}