{"as_of":"2026-08-08T17:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:303b7844374858440fb92928bfcccce2eabe132c614106dce289ace40f6fa172","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":"2026-08-08T06:32:00.761636+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":"2026-08-07T11:22:39.512099Z","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":104,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2004.14546","last_updated":"2020-04-30T02:20:14Z","snapshot_observed_at":"2026-07-06T09:16:26.306280Z","submitted_at":"2020-04-30T02:20:14Z","title":"WT5?! Training Text-to-Text Models to Explain their Predictions","version":1},"cited_work":{"arxiv_id":"2004.14546","doi":"10.48550/arxiv.2004.14546","metadata_source":"arxiv_reference","pith_arxiv_id":"2004.14546","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"org/abs/2004.14546","venue":"arXiv (Cornell University)","work_id":"f2b311a2-2f95-43c1-af18-b27d63fa0e64","year":2004},"citing_paper":{"arxiv_id":"2201.11903","last_updated":"2023-01-10T23:07:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-01-28T02:33:07Z","title":"Chain-of-Thought Prompting Elicits Reasoning in Large Language Models","version":6},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-05-10T12:54:44.636760Z"},"links":{"cited_paper":"/paper/2004.14546","citing_paper":"/paper/2201.11903"},"observation_digest":"sha256:32e4a20789e2f32904d2630f13c1d6a2a4ee4e81195446c10a8e409ecdfc466f","observation_id":"8d94bcd2-87da-4f0a-8b06-c3290ce2ea8f","resolution":{"observed_at":"2026-05-10T12:54:44.763665Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.14546","last_updated":"2020-04-30T02:20:14Z","snapshot_observed_at":"2026-07-06T09:16:26.306280Z","submitted_at":"2020-04-30T02:20:14Z","title":"WT5?! Training Text-to-Text Models to Explain their Predictions","version":1},"cited_work":{"arxiv_id":"2004.14546","doi":"10.48550/arxiv.2004.14546","metadata_source":"arxiv_reference","pith_arxiv_id":"2004.14546","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"org/abs/2004.14546","venue":"arXiv (Cornell University)","work_id":"f2b311a2-2f95-43c1-af18-b27d63fa0e64","year":2004},"citing_paper":{"arxiv_id":"2210.11610","last_updated":"2022-10-25T17:45:17Z","snapshot_observed_at":"2026-07-06T14:08:26.493996Z","submitted_at":"2022-10-20T21:53:54Z","title":"Large Language Models Can Self-Improve","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-18T17:00:48.167441Z"},"links":{"cited_paper":"/paper/2004.14546","citing_paper":"/paper/2210.11610"},"observation_digest":"sha256:a2c07bd807389e5a23108d1028ea4de44edc2cdd278ed0e398028e88cf2c4be4","observation_id":"1d83044e-e847-428e-82ac-d6f08c7d4c0f","resolution":{"observed_at":"2026-05-18T17:00:48.257052Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.14546","last_updated":"2020-04-30T02:20:14Z","snapshot_observed_at":"2026-07-06T09:16:26.306280Z","submitted_at":"2020-04-30T02:20:14Z","title":"WT5?! Training Text-to-Text Models to Explain their Predictions","version":1},"cited_work":{"arxiv_id":"2004.14546","doi":"10.48550/arxiv.2004.14546","metadata_source":"arxiv_reference","pith_arxiv_id":"2004.14546","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"org/abs/2004.14546","venue":"arXiv (Cornell University)","work_id":"f2b311a2-2f95-43c1-af18-b27d63fa0e64","year":2004},"citing_paper":{"arxiv_id":"2305.02301","last_updated":"2023-07-05T16:59:31Z","snapshot_observed_at":"2026-07-06T15:22:55.122322Z","submitted_at":"2023-05-03T17:50:56Z","title":"Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes","version":2},"reference_index":89,"source":"arxiv_source","source_observed_at":"2026-05-21T20:50:09.265838Z"},"links":{"cited_paper":"/paper/2004.14546","citing_paper":"/paper/2305.02301"},"observation_digest":"sha256:b9c4e018fb030fb774b8f06f5094e013d890526000055fda1914d2e228a8c3f0","observation_id":"d39f8154-42de-4346-9e6e-5d8eeee4c966","resolution":{"observed_at":"2026-05-21T20:50:09.454367Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.14546","last_updated":"2020-04-30T02:20:14Z","snapshot_observed_at":"2026-07-06T09:16:26.306280Z","submitted_at":"2020-04-30T02:20:14Z","title":"WT5?! Training Text-to-Text Models to Explain their Predictions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.14546","snapshot_observed_at":"2026-08-07T11:22:39.512099Z","title":"Wt5?! training text-to-text models to explain their predictions,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2506.02708","last_updated":"2025-06-03T10:04:19Z","snapshot_observed_at":"2026-08-07T11:15:25.307392Z","submitted_at":"2025-06-03T10:04:19Z","title":"Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T11:22:39.512099Z"},"links":{"cited_paper":"/paper/2004.14546","citing_paper":"/paper/2506.02708"},"observation_digest":"sha256:5fe7b847fdf2e41b165afe3f07eb3826ba3d6e2083fdcdb3c1daf6308b7776ad","observation_id":"5673ca2f-f78d-4879-9147-6e596ce96ab1","resolution":{"observed_at":"2026-08-07T11:22:39.512099Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.14546","last_updated":"2020-04-30T02:20:14Z","snapshot_observed_at":"2026-07-06T09:16:26.306280Z","submitted_at":"2020-04-30T02:20:14Z","title":"WT5?! Training Text-to-Text Models to Explain their Predictions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.14546","snapshot_observed_at":"2026-08-06T22:22:41.903497Z","title":"and et al","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.21812","last_updated":"2025-06-26T23:25:22Z","snapshot_observed_at":"2026-08-06T22:16:14.734980Z","submitted_at":"2025-06-26T23:25:22Z","title":"Towards Transparent AI: A Survey on Explainable Large Language Models","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T22:22:41.903497Z"},"links":{"cited_paper":"/paper/2004.14546","citing_paper":"/paper/2506.21812"},"observation_digest":"sha256:0b4108e23e31d29bb459e6b8746700e8caa795204826df6fb2e42feba266b8f8","observation_id":"a358237e-5287-42bd-80b9-f841707aab15","resolution":{"observed_at":"2026-08-06T22:22:41.903497Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.14546","last_updated":"2020-04-30T02:20:14Z","snapshot_observed_at":"2026-07-06T09:16:26.306280Z","submitted_at":"2020-04-30T02:20:14Z","title":"WT5?! Training Text-to-Text Models to Explain their Predictions","version":1},"cited_work":{"arxiv_id":"2004.14546","doi":"10.48550/arxiv.2004.14546","metadata_source":"arxiv_reference","pith_arxiv_id":"2004.14546","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"org/abs/2004.14546","venue":"arXiv (Cornell University)","work_id":"f2b311a2-2f95-43c1-af18-b27d63fa0e64","year":2004},"citing_paper":{"arxiv_id":"2606.30775","last_updated":"2026-06-29T18:06:43Z","snapshot_observed_at":"2026-07-07T00:04:34.491408Z","submitted_at":"2026-06-29T18:06:43Z","title":"A Single Rewrite Suffices: Empirical Lessons from Production Skill Description Optimization","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-07-01T02:32:19.425550Z"},"links":{"cited_paper":"/paper/2004.14546","citing_paper":"/paper/2606.30775"},"observation_digest":"sha256:eccb045906307790f07940c6e0a26127f4110c0984148bf4dd47b0fb93e00c94","observation_id":"1f573713-a6d7-477e-a380-08b2f083f789","resolution":{"observed_at":"2026-07-01T02:35:15.842723Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2004.14546/citation-record","integrity":"/paper/2004.14546/integrity","json":"/paper/2004.14546/citation-record.json","paper":"/paper/2004.14546"},"outbound":[],"paper":{"arxiv_id":"2004.14546","last_updated":"2020-04-30T02:20:14Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T09:16:26.306280Z","submitted_at":"2020-04-30T02:20:14Z","title":"WT5?! Training Text-to-Text Models to Explain their Predictions"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2004.14546."}