{"as_of":"2026-08-06T15:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3633882b19a5f46f9f8904af5412121f176ee7db54f49fbd2ac30589aeb39557","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":11,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":11,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":11,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-11T08:46:29.099447Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":12,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2207.08799","last_updated":"2023-01-15T21:53:00Z","snapshot_observed_at":"2026-08-06T09:14:09.933368Z","submitted_at":"2022-07-18T17:55:05Z","title":"Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit","version":3},"cited_work":{"arxiv_id":"2207.08799","doi":"10.48550/arxiv.2207.08799","metadata_source":"arxiv_reference","pith_arxiv_id":"2207.08799","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Neural Information Processing Systems , year =","venue":"arXiv (Cornell University)","work_id":"92192172-5c98-475d-ab81-1f83e1a2d120","year":2022},"citing_paper":{"arxiv_id":"2211.00593","last_updated":"2022-11-01T17:08:44Z","snapshot_observed_at":"2026-08-05T06:04:21.031867Z","submitted_at":"2022-11-01T17:08:44Z","title":"Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-05-13T17:13:51.408311Z"},"links":{"cited_paper":"/paper/2207.08799","citing_paper":"/paper/2211.00593"},"observation_digest":"sha256:9af6124d1f068cfd58b5dfdf006fb3b2a9473ef40f8f2ed43ccd9f8712baeb0e","observation_id":"fbc2d07d-597a-42d0-9b66-0e56c0f3a6b1","resolution":{"observed_at":"2026-05-13T17:13:51.494465Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.08799","last_updated":"2023-01-15T21:53:00Z","snapshot_observed_at":"2026-08-06T09:14:09.933368Z","submitted_at":"2022-07-18T17:55:05Z","title":"Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit","version":3},"cited_work":{"arxiv_id":"2207.08799","doi":"10.48550/arxiv.2207.08799","metadata_source":"arxiv_reference","pith_arxiv_id":"2207.08799","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Neural Information Processing Systems , year =","venue":"arXiv (Cornell University)","work_id":"92192172-5c98-475d-ab81-1f83e1a2d120","year":2022},"citing_paper":{"arxiv_id":"2301.05217","last_updated":"2023-10-19T21:25:32Z","snapshot_observed_at":"2026-08-02T10:20:00.635719Z","submitted_at":"2023-01-12T18:56:49Z","title":"Progress measures for grokking via mechanistic interpretability","version":3},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-05-14T21:52:56.040569Z"},"links":{"cited_paper":"/paper/2207.08799","citing_paper":"/paper/2301.05217"},"observation_digest":"sha256:6d480a579e79ab24edaa499b4c4ed68ad0942a5e85c7a89990b8cf9478581925","observation_id":"695b23b0-ae68-418e-9e26-ceb4e62fcf24","resolution":{"observed_at":"2026-05-14T21:52:56.141440Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.08799","last_updated":"2023-01-15T21:53:00Z","snapshot_observed_at":"2026-08-06T09:14:09.933368Z","submitted_at":"2022-07-18T17:55:05Z","title":"Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit","version":3},"cited_work":{"arxiv_id":"2207.08799","doi":"10.48550/arxiv.2207.08799","metadata_source":"arxiv_reference","pith_arxiv_id":"2207.08799","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Neural Information Processing Systems , year =","venue":"arXiv (Cornell University)","work_id":"92192172-5c98-475d-ab81-1f83e1a2d120","year":2022},"citing_paper":{"arxiv_id":"2402.17762","last_updated":"2024-08-14T16:00:49Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-02-27T18:55:17Z","title":"Massive Activations in Large Language Models","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-05-16T07:02:53.740597Z"},"links":{"cited_paper":"/paper/2207.08799","citing_paper":"/paper/2402.17762"},"observation_digest":"sha256:c470df9a93ed5282f748ef3466ec51bf2a2181e84d7580c273ab782a605921bc","observation_id":"2aaa1d0a-f51b-4518-b377-c470626ae7eb","resolution":{"observed_at":"2026-05-16T07:02:53.826534Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.08799","last_updated":"2023-01-15T21:53:00Z","snapshot_observed_at":"2026-08-06T09:14:09.933368Z","submitted_at":"2022-07-18T17:55:05Z","title":"Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit","version":3},"cited_work":{"arxiv_id":"2207.08799","doi":"10.48550/arxiv.2207.08799","metadata_source":"arxiv_reference","pith_arxiv_id":"2207.08799","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Neural Information Processing Systems , year =","venue":"arXiv (Cornell University)","work_id":"92192172-5c98-475d-ab81-1f83e1a2d120","year":2022},"citing_paper":{"arxiv_id":"2604.13082","last_updated":"2026-06-17T04:09:14Z","snapshot_observed_at":"2026-07-13T15:57:54.517771Z","submitted_at":"2026-03-30T22:20:38Z","title":"The Long Delay to Arithmetic Generalization: When Learned Representations Outrun Behavior","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-14T21:16:40.430384Z"},"links":{"cited_paper":"/paper/2207.08799","citing_paper":"/paper/2604.13082"},"observation_digest":"sha256:a9eef5ea461da1317a65700be78038cf9001ad490cf91516e03a8bdc65fb54c8","observation_id":"19323556-7624-4dab-a6db-53763a74bb8b","resolution":{"observed_at":"2026-05-14T21:17:58.875355Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.08799","last_updated":"2023-01-15T21:53:00Z","snapshot_observed_at":"2026-08-06T09:14:09.933368Z","submitted_at":"2022-07-18T17:55:05Z","title":"Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit","version":3},"cited_work":{"arxiv_id":"2207.08799","doi":"10.48550/arxiv.2207.08799","metadata_source":"arxiv_reference","pith_arxiv_id":"2207.08799","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Neural Information Processing Systems , year =","venue":"arXiv (Cornell University)","work_id":"92192172-5c98-475d-ab81-1f83e1a2d120","year":2022},"citing_paper":{"arxiv_id":"2605.10019","last_updated":"2026-05-11T05:44:18Z","snapshot_observed_at":"2026-07-06T23:22:03.830881Z","submitted_at":"2026-05-11T05:44:18Z","title":"The two clocks and the innovation window: When and how generative models learn rules","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-05-12T03:15:45.257213Z"},"links":{"cited_paper":"/paper/2207.08799","citing_paper":"/paper/2605.10019"},"observation_digest":"sha256:db568a269226085cf1011a43faf3649a106765716e3dc2f5d1f81c0f6627192b","observation_id":"298e738a-b6be-435f-86d9-f592bcc31193","resolution":{"observed_at":"2026-05-12T03:16:18.120838Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.08799","last_updated":"2023-01-15T21:53:00Z","snapshot_observed_at":"2026-08-06T09:14:09.933368Z","submitted_at":"2022-07-18T17:55:05Z","title":"Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit","version":3},"cited_work":{"arxiv_id":"2207.08799","doi":"10.48550/arxiv.2207.08799","metadata_source":"arxiv_reference","pith_arxiv_id":"2207.08799","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Neural Information Processing Systems , year =","venue":"arXiv (Cornell University)","work_id":"92192172-5c98-475d-ab81-1f83e1a2d120","year":2022},"citing_paper":{"arxiv_id":"2605.10237","last_updated":"2026-05-11T09:11:20Z","snapshot_observed_at":"2026-07-06T23:22:13.774652Z","submitted_at":"2026-05-11T09:11:20Z","title":"The Benefits of Temporal Correlations: SGD Learns k-Juntas from Random Walks Efficiently","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-05-12T05:27:11.761971Z"},"links":{"cited_paper":"/paper/2207.08799","citing_paper":"/paper/2605.10237"},"observation_digest":"sha256:98eb583d7768954d4022bb6dd54bd4decf31eb781852122bbd3938527b305500","observation_id":"746933b5-0e7a-4b20-ac76-0a3269ed01ad","resolution":{"observed_at":"2026-05-12T05:31:24.123871Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.08799","last_updated":"2023-01-15T21:53:00Z","snapshot_observed_at":"2026-08-06T09:14:09.933368Z","submitted_at":"2022-07-18T17:55:05Z","title":"Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit","version":3},"cited_work":{"arxiv_id":"2207.08799","doi":"10.48550/arxiv.2207.08799","metadata_source":"arxiv_reference","pith_arxiv_id":"2207.08799","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Neural Information Processing Systems , year =","venue":"arXiv (Cornell University)","work_id":"92192172-5c98-475d-ab81-1f83e1a2d120","year":2022},"citing_paper":{"arxiv_id":"2605.20314","last_updated":"2026-05-19T17:28:20Z","snapshot_observed_at":"2026-07-06T23:30:53.900592Z","submitted_at":"2026-05-19T17:28:20Z","title":"Less Data, Faster Training: repeating smaller datasets speeds up learning via sampling biases","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-05-21T07:16:15.549463Z"},"links":{"cited_paper":"/paper/2207.08799","citing_paper":"/paper/2605.20314"},"observation_digest":"sha256:c18600435777ccd53d83245fce613df2352ba20a418f3ea7bd2d78dc6d58f4c8","observation_id":"b17d132e-133a-4163-a9cf-a73a301c60a2","resolution":{"observed_at":"2026-05-21T07:19:46.460873Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.08799","last_updated":"2023-01-15T21:53:00Z","snapshot_observed_at":"2026-08-06T09:14:09.933368Z","submitted_at":"2022-07-18T17:55:05Z","title":"Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit","version":3},"cited_work":{"arxiv_id":"2207.08799","doi":"10.48550/arxiv.2207.08799","metadata_source":"arxiv_reference","pith_arxiv_id":"2207.08799","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Neural Information Processing Systems , year =","venue":"arXiv (Cornell University)","work_id":"92192172-5c98-475d-ab81-1f83e1a2d120","year":2022},"citing_paper":{"arxiv_id":"2606.05957","last_updated":"2026-06-04T09:54:08Z","snapshot_observed_at":"2026-07-06T23:45:51.910263Z","submitted_at":"2026-06-04T09:54:08Z","title":"Dead Directions: Geometric Singular Learning","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-06-28T03:20:09.365073Z"},"links":{"cited_paper":"/paper/2207.08799","citing_paper":"/paper/2606.05957"},"observation_digest":"sha256:1efeaaa02c328516e49a2e1264204f72eb9b58b71034f30461b1f62018f79b60","observation_id":"84460bec-87cd-4060-998c-fb2d1b0d334d","resolution":{"observed_at":"2026-07-02T11:36:55.206532Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.08799","last_updated":"2023-01-15T21:53:00Z","snapshot_observed_at":"2026-08-06T09:14:09.933368Z","submitted_at":"2022-07-18T17:55:05Z","title":"Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit","version":3},"cited_work":{"arxiv_id":"2207.08799","doi":"10.48550/arxiv.2207.08799","metadata_source":"arxiv_reference","pith_arxiv_id":"2207.08799","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Neural Information Processing Systems , year =","venue":"arXiv (Cornell University)","work_id":"92192172-5c98-475d-ab81-1f83e1a2d120","year":2022},"citing_paper":{"arxiv_id":"2606.19542","last_updated":"2026-06-17T19:35:10Z","snapshot_observed_at":"2026-07-06T23:54:51.451358Z","submitted_at":"2026-06-17T19:35:10Z","title":"Tracking Representation Dynamics in Large Language Models with Persistent Homology","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-26T21:03:07.253265Z"},"links":{"cited_paper":"/paper/2207.08799","citing_paper":"/paper/2606.19542"},"observation_digest":"sha256:c5e87786c39d346101c26ac73c2822c4e4489d920e502e70004d1e986d86ae1d","observation_id":"5da745fc-0311-4d6e-bd9e-a4005e0819f7","resolution":{"observed_at":"2026-07-04T00:39:17.222472Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.08799","last_updated":"2023-01-15T21:53:00Z","snapshot_observed_at":"2026-08-06T09:14:09.933368Z","submitted_at":"2022-07-18T17:55:05Z","title":"Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit","version":3},"cited_work":{"arxiv_id":"2207.08799","doi":"10.48550/arxiv.2207.08799","metadata_source":"arxiv_reference","pith_arxiv_id":"2207.08799","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Neural Information Processing Systems , year =","venue":"arXiv (Cornell University)","work_id":"92192172-5c98-475d-ab81-1f83e1a2d120","year":2022},"citing_paper":{"arxiv_id":"2606.21158","last_updated":"2026-06-19T06:49:09Z","snapshot_observed_at":"2026-08-02T18:52:22.861042Z","submitted_at":"2026-06-19T06:49:09Z","title":"Dead-Direction Signatures: A Cheap Spectral Reading of Singular Complexity","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-06-26T14:36:17.793710Z"},"links":{"cited_paper":"/paper/2207.08799","citing_paper":"/paper/2606.21158"},"observation_digest":"sha256:e1bb9e78be9d8614ba8dcb17fbc1e2d6e90bf7db49e03ad0d0e7e021bb9ada83","observation_id":"af373fd4-4fc2-4a8a-88ac-58ab1142c327","resolution":{"observed_at":"2026-07-04T06:19:38.277556Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.08799","last_updated":"2023-01-15T21:53:00Z","snapshot_observed_at":"2026-08-06T09:14:09.933368Z","submitted_at":"2022-07-18T17:55:05Z","title":"Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.08799","snapshot_observed_at":"2026-07-11T08:46:29.099447Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.05104","last_updated":"2026-07-06T13:58:31Z","snapshot_observed_at":"2026-07-30T23:11:09.581606Z","submitted_at":"2026-07-06T13:58:31Z","title":"Grokking Is Conditional and Fragile: A Fully-Tractable, Multi-Seed Study at 12K Parameters","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-11T08:46:29.099447Z"},"links":{"cited_paper":"/paper/2207.08799","citing_paper":"/paper/2607.05104"},"observation_digest":"sha256:f7bbde690faf24dab074ff05156ae3c3a24c72dc33d345f7c9979ec030a1834c","observation_id":"d977a711-ba09-4398-9940-0d3116057758","resolution":{"observed_at":"2026-07-11T08:46:29.099447Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2207.08799/citation-record","integrity":"/paper/2207.08799/integrity","json":"/paper/2207.08799/citation-record.json","paper":"/paper/2207.08799"},"outbound":[],"paper":{"arxiv_id":"2207.08799","last_updated":"2023-01-15T21:53:00Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T09:14:09.933368Z","submitted_at":"2022-07-18T17:55:05Z","title":"Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit"},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2207.08799."}