{"as_of":"2026-08-17T05:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bcd358ee8616ad3293629230b7d1412986251fcf8c17842d746b833a3b5cace0","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":13,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":13,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":13,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":13,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T00:42:27.553871Z","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":"2026-07-02T00:56:25.134661Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.14171","last_updated":"2024-10-29T09:59:22Z","snapshot_observed_at":"2026-08-16T13:08:10.467183Z","submitted_at":"2024-10-18T04:29:46Z","title":"Heavy-Tailed Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.14171","snapshot_observed_at":"2026-08-11T13:36:04.883253Z","title":"Heavy-tailed diffusion models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.12971","last_updated":"2024-12-17T14:54:30Z","snapshot_observed_at":"2026-08-14T03:33:36.546544Z","submitted_at":"2024-12-17T14:54:30Z","title":"ArchesWeather & ArchesWeatherGen: a deterministic and generative model for efficient ML weather forecasting","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T13:36:04.883253Z"},"links":{"cited_paper":"/paper/2410.14171","citing_paper":"/paper/2412.12971"},"observation_digest":"sha256:b2e75385bcd9fc5da0a94584e6e542e889de6ee99d914cef258739d095813dd8","observation_id":"5265fa1e-5f6d-494f-8af0-65d0026aed22","resolution":{"observed_at":"2026-08-11T13:36:04.883253Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.14171","last_updated":"2024-10-29T09:59:22Z","snapshot_observed_at":"2026-08-16T13:08:10.467183Z","submitted_at":"2024-10-18T04:29:46Z","title":"Heavy-Tailed Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.14171","snapshot_observed_at":"2026-08-06T20:38:34.311608Z","title":"Heavy-tailed diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02356","last_updated":"2025-07-03T06:41:03Z","snapshot_observed_at":"2026-08-14T13:08:53.477930Z","submitted_at":"2025-07-03T06:41:03Z","title":"Offline Reinforcement Learning with Penalized Action Noise Injection","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-06T20:38:34.311608Z"},"links":{"cited_paper":"/paper/2410.14171","citing_paper":"/paper/2507.02356"},"observation_digest":"sha256:110a154f468c9ac2ea7ddc4f0a3a0236567731cc2ff9634c6a38c5fdc0ff9616","observation_id":"b65795e4-72c4-4282-bfeb-70a1979aaa5b","resolution":{"observed_at":"2026-08-06T20:38:34.311608Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.14171","last_updated":"2024-10-29T09:59:22Z","snapshot_observed_at":"2026-08-16T13:08:10.467183Z","submitted_at":"2024-10-18T04:29:46Z","title":"Heavy-Tailed Diffusion Models","version":2},"cited_work":{"arxiv_id":"2410.14171","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.14171","snapshot_observed_at":"2026-07-02T00:56:25.134661Z","title":"arXiv preprint arXiv:2410.14171 , year=","venue":null,"work_id":"a8e289ed-c00d-41be-9309-69b1338db81e","year":2024},"citing_paper":{"arxiv_id":"2605.18931","last_updated":"2026-06-03T15:30:42Z","snapshot_observed_at":"2026-08-16T12:48:18.753168Z","submitted_at":"2026-05-18T14:21:46Z","title":"Markov Chain Decoders Overcome the Heavy-Tail Limitations of Lipschitz Generative Models","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-05-20T08:33:38.253156Z"},"links":{"cited_paper":"/paper/2410.14171","citing_paper":"/paper/2605.18931"},"observation_digest":"sha256:88f2606e16dd6c6c277aaf08540ca5e11edef55cdb3a6a2cd6244b68bfd60e0e","observation_id":"f29e638f-8a1b-43bb-8ea2-61ee5f7e353b","resolution":{"observed_at":"2026-05-20T08:38:10.490299Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.14171","last_updated":"2024-10-29T09:59:22Z","snapshot_observed_at":"2026-08-16T13:08:10.467183Z","submitted_at":"2024-10-18T04:29:46Z","title":"Heavy-Tailed Diffusion Models","version":2},"cited_work":{"arxiv_id":"2410.14171","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.14171","snapshot_observed_at":"2026-07-02T00:56:25.134661Z","title":"arXiv preprint arXiv:2410.14171 , year=","venue":null,"work_id":"a8e289ed-c00d-41be-9309-69b1338db81e","year":2024},"citing_paper":{"arxiv_id":"2605.20068","last_updated":"2026-06-21T10:35:41Z","snapshot_observed_at":"2026-08-02T08:53:33.208300Z","submitted_at":"2026-05-19T16:22:03Z","title":"Tail Annealing for Heavy-Tailed Flow Matching","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-20T03:45:58.258721Z"},"links":{"cited_paper":"/paper/2410.14171","citing_paper":"/paper/2605.20068"},"observation_digest":"sha256:0d5b405a20234b16da56f8afcc02b6cc7897b6e3e48f8f051b842d2397d470f8","observation_id":"2d068562-f968-4214-8aa8-fb0017c45c27","resolution":{"observed_at":"2026-05-20T03:48:02.616373Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.14171","last_updated":"2024-10-29T09:59:22Z","snapshot_observed_at":"2026-08-16T13:08:10.467183Z","submitted_at":"2024-10-18T04:29:46Z","title":"Heavy-Tailed Diffusion Models","version":2},"cited_work":{"arxiv_id":"2410.14171","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.14171","snapshot_observed_at":"2026-07-02T00:56:25.134661Z","title":"arXiv preprint arXiv:2410.14171 , year=","venue":null,"work_id":"a8e289ed-c00d-41be-9309-69b1338db81e","year":2024},"citing_paper":{"arxiv_id":"2605.20068","last_updated":"2026-06-21T10:35:41Z","snapshot_observed_at":"2026-08-02T08:53:33.208300Z","submitted_at":"2026-05-19T16:22:03Z","title":"Tail Annealing for Heavy-Tailed Flow Matching","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-30T18:06:31.251203Z"},"links":{"cited_paper":"/paper/2410.14171","citing_paper":"/paper/2605.20068"},"observation_digest":"sha256:9c48687ad8e1f5d22e50dd99bf830ea9c5afdd65ce27861e442a744f783210a7","observation_id":"0d978c7e-9835-4309-824d-903e45c4cf9e","resolution":{"observed_at":"2026-07-01T15:05:47.939221Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.14171","last_updated":"2024-10-29T09:59:22Z","snapshot_observed_at":"2026-08-16T13:08:10.467183Z","submitted_at":"2024-10-18T04:29:46Z","title":"Heavy-Tailed Diffusion Models","version":2},"cited_work":{"arxiv_id":"2410.14171","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.14171","snapshot_observed_at":"2026-07-02T00:56:25.134661Z","title":"arXiv preprint arXiv:2410.14171 , year=","venue":null,"work_id":"a8e289ed-c00d-41be-9309-69b1338db81e","year":2024},"citing_paper":{"arxiv_id":"2606.01645","last_updated":"2026-06-01T03:52:08Z","snapshot_observed_at":"2026-08-15T11:16:31.387273Z","submitted_at":"2026-06-01T03:52:08Z","title":"Self-Regulating Annealing in Heavy-Tailed Diffusion Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-28T13:05:16.965788Z"},"links":{"cited_paper":"/paper/2410.14171","citing_paper":"/paper/2606.01645"},"observation_digest":"sha256:5a17580e721afc98bb9f82a4385a4d4ede737c544ba3e6b6d3a8bc3ae383c592","observation_id":"06e9d5ba-cf21-47dd-9805-2f59dfd13576","resolution":{"observed_at":"2026-07-02T00:56:25.137024Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.14171","last_updated":"2024-10-29T09:59:22Z","snapshot_observed_at":"2026-08-16T13:08:10.467183Z","submitted_at":"2024-10-18T04:29:46Z","title":"Heavy-Tailed Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.14171","snapshot_observed_at":"2026-07-14T09:55:27.630410Z","title":"arXiv preprint arXiv:2410.14171 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.10700","last_updated":"2026-07-12T10:46:41Z","snapshot_observed_at":"2026-08-15T05:26:31.091748Z","submitted_at":"2026-07-12T10:46:41Z","title":"An Extreme Value Perspective on Learning Stress Laws","version":1},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-07-14T09:55:27.630410Z"},"links":{"cited_paper":"/paper/2410.14171","citing_paper":"/paper/2607.10700"},"observation_digest":"sha256:dfcf08ba2bce04fc7dcb985bdbf05ee8aa0e37c2a09bc08b43e790cf2b51ba26","observation_id":"fb2469bb-98c0-4377-8567-0991891d04d6","resolution":{"observed_at":"2026-07-14T09:55:27.630410Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.14171","last_updated":"2024-10-29T09:59:22Z","snapshot_observed_at":"2026-08-16T13:08:10.467183Z","submitted_at":"2024-10-18T04:29:46Z","title":"Heavy-Tailed Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.14171","snapshot_observed_at":"2026-07-14T08:47:34.060973Z","title":"Heavy-tailed diffusion models.arXiv preprint arXiv:2410.14171, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10853","last_updated":"2026-07-12T17:32:29Z","snapshot_observed_at":"2026-08-15T07:26:26.544528Z","submitted_at":"2026-07-12T17:32:29Z","title":"Diversify Diffusion with Temperature Sampling and Variance-Corrective Time Shifting","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-14T08:47:34.060973Z"},"links":{"cited_paper":"/paper/2410.14171","citing_paper":"/paper/2607.10853"},"observation_digest":"sha256:97125695564b92095db7feb768ae69d4cd7061a47f8dcee2a653dc726ed27b0a","observation_id":"88c84f2b-9e67-412b-92ce-55186d97fd15","resolution":{"observed_at":"2026-07-14T08:47:34.060973Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.14171","last_updated":"2024-10-29T09:59:22Z","snapshot_observed_at":"2026-08-16T13:08:10.467183Z","submitted_at":"2024-10-18T04:29:46Z","title":"Heavy-Tailed Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.14171","snapshot_observed_at":"2026-08-02T03:38:45.114592Z","title":"Heavy-tailed diffusion models.arXiv preprint arXiv:2410.14171, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.13841","last_updated":"2026-07-15T13:48:05Z","snapshot_observed_at":"2026-08-14T16:16:43.705960Z","submitted_at":"2026-07-15T13:48:05Z","title":"Heavy-Tailed Flow Matching via Random Clocks","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-02T03:38:45.114592Z"},"links":{"cited_paper":"/paper/2410.14171","citing_paper":"/paper/2607.13841"},"observation_digest":"sha256:771043d2e168afcd4fd3e911fcc9b55590d0a8711000ad1a71a6d1d45161fabf","observation_id":"729329e4-abca-4209-84a0-461f4766b4c1","resolution":{"observed_at":"2026-08-02T03:38:45.114592Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.14171","last_updated":"2024-10-29T09:59:22Z","snapshot_observed_at":"2026-08-16T13:08:10.467183Z","submitted_at":"2024-10-18T04:29:46Z","title":"Heavy-Tailed Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.14171","snapshot_observed_at":"2026-08-01T13:15:19.794117Z","title":"Pandey et al","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.19218","last_updated":"2026-07-21T15:48:50Z","snapshot_observed_at":"2026-08-12T04:41:53.253443Z","submitted_at":"2026-07-21T15:48:50Z","title":"Denoising Subordinated Probabilistic Models: Diffusion with a Tempered-Stable Volatility Clock, and What the Noise Mechanism Actually Controls","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-01T13:15:19.794117Z"},"links":{"cited_paper":"/paper/2410.14171","citing_paper":"/paper/2607.19218"},"observation_digest":"sha256:24d1f797c58c6de1f67a69942517cdfd70b52198eab03e7616d779091e8cfbcd","observation_id":"a2933e68-8246-4490-b218-88dbe200c255","resolution":{"observed_at":"2026-08-01T13:15:19.794117Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.14171","last_updated":"2024-10-29T09:59:22Z","snapshot_observed_at":"2026-08-16T13:08:10.467183Z","submitted_at":"2024-10-18T04:29:46Z","title":"Heavy-Tailed Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.14171","snapshot_observed_at":"2026-08-06T00:33:18.144751Z","title":"Pandey, J","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.01223","last_updated":"2026-08-02T13:12:16Z","snapshot_observed_at":"2026-08-10T04:55:14.792489Z","submitted_at":"2026-08-02T13:12:16Z","title":"Perspectives on Tsallis Statistics for Artificial Intelligence","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T00:33:18.144751Z"},"links":{"cited_paper":"/paper/2410.14171","citing_paper":"/paper/2608.01223"},"observation_digest":"sha256:ffbbaa0e51cc19f8b6c1b7a0e619cb21c1d8d7cd8927ad51d9f0712e3e530b57","observation_id":"e78e911a-1f8d-40eb-b81c-359ba12563a7","resolution":{"observed_at":"2026-08-06T00:33:18.144751Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.14171","last_updated":"2024-10-29T09:59:22Z","snapshot_observed_at":"2026-08-16T13:08:10.467183Z","submitted_at":"2024-10-18T04:29:46Z","title":"Heavy-Tailed Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.14171","snapshot_observed_at":"2026-08-16T00:42:27.553871Z","title":"Pandey, J","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.11544","last_updated":"2026-08-12T01:26:14Z","snapshot_observed_at":"2026-08-16T00:33:37.254686Z","submitted_at":"2026-08-12T01:26:14Z","title":"Fine-Tuning Generative Models for Extreme Events via CVaR-Penalized Wasserstein Gradient Flows","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-16T00:42:27.553871Z"},"links":{"cited_paper":"/paper/2410.14171","citing_paper":"/paper/2608.11544"},"observation_digest":"sha256:ffe684f950fbf4f223731d9d554f0ce20f46703d095daae227c8e3005e6e877a","observation_id":"d9cb9cd3-58fb-47ae-bc8e-ebc74bfdd477","resolution":{"observed_at":"2026-08-16T00:42:27.553871Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.14171","last_updated":"2024-10-29T09:59:22Z","snapshot_observed_at":"2026-08-16T13:08:10.467183Z","submitted_at":"2024-10-18T04:29:46Z","title":"Heavy-Tailed Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.14171","snapshot_observed_at":"2026-08-15T17:52:23.317575Z","title":"Heavy-tailed diffusion models.arXiv preprint arXiv:2410.14171,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.13056","last_updated":"2026-08-13T10:21:07Z","snapshot_observed_at":"2026-08-16T23:11:32.909872Z","submitted_at":"2026-08-13T10:21:07Z","title":"Simulating Stress Laws under Extremal Dependence: Characterizing What Generative Models Must Preserve","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T17:52:23.317575Z"},"links":{"cited_paper":"/paper/2410.14171","citing_paper":"/paper/2608.13056"},"observation_digest":"sha256:1b0045ae8083cc2e84097cc24983e4c54a997c030239b1d29d3f1995d6e62d71","observation_id":"a16ace2b-8e4e-40f1-b2f8-f67485f631ad","resolution":{"observed_at":"2026-08-15T17:52:23.317575Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2410.14171/citation-record","integrity":"/paper/2410.14171/integrity","json":"/paper/2410.14171/citation-record.json","paper":"/paper/2410.14171"},"outbound":[],"paper":{"arxiv_id":"2410.14171","last_updated":"2024-10-29T09:59:22Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T13:08:10.467183Z","submitted_at":"2024-10-18T04:29:46Z","title":"Heavy-Tailed Diffusion Models"},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2410.14171."}