{"as_of":"2026-08-18T13:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1ff4c03ea753e6c40559c503f93a37823ce07d8692e41f5891b2bab9b480fcf0","coverage":[{"denominator":58,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":58,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T04:21:30.091313Z","state":"measured"},{"denominator":59,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":59,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-13T02:52:53.741989Z","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":"2026-05-13T02:57:09.371699Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"cited_work":{"arxiv_id":"2505.01937","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.01937","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2505.01937 , year=","venue":null,"work_id":"2eeb4c4a-881d-4643-8071-c96803b033e1","year":null},"citing_paper":{"arxiv_id":"2605.12461","last_updated":"2026-05-12T17:48:09Z","snapshot_observed_at":"2026-07-06T23:24:08.763444Z","submitted_at":"2026-05-12T17:48:09Z","title":"A proximal gradient algorithm for composite log-concave sampling","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-05-13T02:52:53.741989Z"},"links":{"cited_paper":"/paper/2505.01937","citing_paper":"/paper/2605.12461"},"observation_digest":"sha256:119324b90bbd822d347b37771d48cf4f92c73dd61d76cbcf101f2e4ba4cd0140","observation_id":"3b7c4b85-1cef-426d-91a2-e9e775fd0996","resolution":{"observed_at":"2026-05-13T02:57:09.374094Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.01937/citation-record","integrity":"/paper/2505.01937/integrity","json":"/paper/2505.01937/citation-record.json","paper":"/paper/2505.01937"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.988319Z","title":"The central limit problem for convex bodies","venue":null,"work_id":"4521d9cb-3cf7-4bf9-98cc-9f59c0f40869","year":2003},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.836404Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:874f526773613734329f9bddb682712b749d0c72a180ec72ae406dcc82f3bfc9","observation_id":"87d83278-f82a-48ec-89de-4eebb57218c3","resolution":{"observed_at":"2026-08-16T04:21:30.994276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.972126Z","title":"Analysis and geometry of M arkov diffusion operators , volume 348","venue":null,"work_id":"e81fdf10-6118-47db-a86e-b0553fc420a7","year":2014},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.841981Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:5217e7f70e3e4b37ce3376dc81c11548ea8bac6a1f0342999b4d2272b433c4a2","observation_id":"ba17faa0-8e7a-440e-b346-35d977b649c7","resolution":{"observed_at":"2026-08-16T04:21:30.976766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.955148Z","title":"On measures strongly log-concave on a subspace","venue":null,"work_id":"3b31e93f-588a-4f8f-98b6-f4bc9601c6c8","year":2024},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.846559Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:3f4768e61a28ae3b4787397039877b7b465cc8f397373390bc45c7f6d9a097e9","observation_id":"0f563d24-4b6c-4310-8d9a-62d6d176ad4b","resolution":{"observed_at":"2026-08-16T04:21:30.960798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.06854","last_updated":"2025-01-12T16:07:23Z","snapshot_observed_at":"2026-08-15T13:38:10.580166Z","submitted_at":"2025-01-12T16:07:23Z","title":"The slicing conjecture via small ball estimates","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.06854","snapshot_observed_at":"2026-08-16T04:21:29.851422Z","title":"The slicing conjecture via small ball estimates","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.851422Z"},"links":{"cited_paper":"/paper/2501.06854","citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:ed2bc9b5cdafba8d029b72f90872c6e05710b165c6dea368bd1dd8432334e78b","observation_id":"0eb8d1c5-68a3-4161-8663-1148f1817945","resolution":{"observed_at":"2026-08-16T04:21:29.851422Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.939060Z","title":"Bobkov and Alexander Koldobsky","venue":null,"work_id":"f9186865-d613-4ad4-9160-de4477c51e4d","year":2003},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.856446Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:7383ea684d47f77d6b02b303d7f5a302f060fcbbdf74ce1302433d7a189c1f7d","observation_id":"ae201346-978f-4786-86da-4be104a10644","resolution":{"observed_at":"2026-08-16T04:21:30.944037Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.923071Z","title":"A note on the isoperimetric constant","venue":null,"work_id":"f0cdb2c2-08a7-40f9-a5d9-53b414d2d570","year":1982},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.861297Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:e9a36f43e7dba93883a4a2a249bb13bf9023d0501b1519986dfcbbc0267e7816","observation_id":"506c65cb-2cba-4a0f-979e-2ec06b3c3607","resolution":{"observed_at":"2026-08-16T04:21:30.927726Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:29.866502Z","title":"Caffarelli","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.866502Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:4fa5386161ecd412998be067c6fab3b800d9ae5eaa1b111be3810ec21f629e06","observation_id":"9a1d4758-8f58-498e-92c0-c96659240ccc","resolution":{"observed_at":"2026-08-16T04:21:29.866502Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.896343Z","title":"Improved analysis for a proximal algorithm for sampling","venue":null,"work_id":"10b825d3-d00a-41d3-933c-14d7a29b0fdd","year":2022},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.870978Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:aef9d57196124bda1c0a22b1f9b646d06421126c2709294b140a8649e96df17c","observation_id":"b38e8a10-1b61-419b-b4b0-a4bee3d9dccb","resolution":{"observed_at":"2026-08-16T04:21:30.901973Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.880918Z","title":"A lower bound for the smallest eigenvalue of the L aplacian","venue":null,"work_id":"18106078-0ed6-4c4b-89cb-b6259a886988","year":1970},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.875353Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:2d041ec6f80a83f8d202c5435b906a491d90b4a851de66aa581bab0324b31afe","observation_id":"857ea6e4-0892-4eb2-b103-9d57ff56fd95","resolution":{"observed_at":"2026-08-16T04:21:30.885940Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.864425Z","title":"An almost constant lower bound of the isoperimetric coefficient in the KLS conjecture","venue":null,"work_id":"700f9890-2c01-4bbf-ac0e-74f93ce5a968","year":2021},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.879716Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:170660daab5e2be0f254ff87c519f4d254396dc05f2da9dab3c886de92d02dc6","observation_id":"1a19db87-0bef-47eb-a44d-88593eed2139","resolution":{"observed_at":"2026-08-16T04:21:30.870176Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.848539Z","title":null,"venue":null,"work_id":"f6b95a90-a42a-4e8a-bc04-c7f79f62119a","year":2014},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.884013Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:bf8f1a9e49c86715f81e85efad7c86ffd99c7b83c79c4d224f81270dde384d9e","observation_id":"50cebfcd-ccec-4b0d-9702-3ae6cc007e06","resolution":{"observed_at":"2026-08-16T04:21:30.853803Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.832136Z","title":null,"venue":null,"work_id":"d0f80b51-4742-45e4-a785-df0666d1efbb","year":2015},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.888542Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:e8391c7c95bc793759c1c9e6c8df261095a48a728459751bf2325673a38c1cb5","observation_id":"dc6b4cea-04b7-480f-a498-1465ea5c7d1b","resolution":{"observed_at":"2026-08-16T04:21:30.836852Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.816295Z","title":null,"venue":null,"work_id":"e9babe5e-8893-485f-a366-d44f1ad01f6e","year":2016},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.893150Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:63d8a71e58e4d5fd0679f7395cc832812a69ab3ad85c316b4a1f1b5c8d76d2dd","observation_id":"3af5b599-f4f5-45dc-8689-802440b645a2","resolution":{"observed_at":"2026-08-16T04:21:30.821407Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.800289Z","title":null,"venue":null,"work_id":"c49cb064-0261-42fb-a21e-cc59368870a5","year":2018},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.897445Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:a44445896ed890013f4ee33c7f891d8d59cb98e69e21a4d9b064f84b3b20d39b","observation_id":"92fb0ab4-dc1c-4b22-80d9-82d995b333cc","resolution":{"observed_at":"2026-08-16T04:21:30.805198Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.785134Z","title":"A random polynomial-time algorithm for approximating the volume of convex bodies","venue":null,"work_id":"6e019260-d301-4a26-a92a-419e8ab1487b","year":1991},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.901878Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:36e7071312ab86fcb78930a39309c619bb37ceefe2f1ce7a6457690b664ae8c2","observation_id":"c0ba017c-82fb-4868-ac35-75bc33cc9afa","resolution":{"observed_at":"2026-08-16T04:21:30.789883Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.770248Z","title":"Small ball probability estimates, _2 -behavior and the hyperplane conjecture","venue":null,"work_id":"de0b1656-71a4-4f9e-9e11-3ce58bbebf9f","year":1933},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.906384Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:ffbb4b71b0af6e286ebb83308e906fbf38720f6dcae02883cb261c54c12adadf","observation_id":"2df81458-0f2b-4546-8323-c5fc276b891c","resolution":{"observed_at":"2026-08-16T04:21:30.775388Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:29.910841Z","title":"Thin shell implies spectral gap up to polylog via a stochastic localization scheme","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.910841Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:c16748a793efd0162a5c245870bfb732d3722c16c23c8e89d93a16ab20abb523","observation_id":"8bbdb052-e10d-4b7d-a503-341fb90f28d5","resolution":{"observed_at":"2026-08-16T04:21:29.910841Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.743068Z","title":"Log- S obolev inequalities and sampling from log-concave distributions","venue":null,"work_id":"2fb9f673-19c6-4279-b8ba-50a4d190da47","year":1999},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.915164Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:7e1b785c0d671b27a8d7a31073e1e8a33ca65e6b723ad8bb3903d9a53dc0b163","observation_id":"62f57a33-59ab-444d-9bb6-c8253d2f4e48","resolution":{"observed_at":"2026-08-16T04:21:30.748033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.727804Z","title":"Improved dimension dependence of a proximal algorithm for sampling","venue":null,"work_id":"0149984b-b507-415f-a9db-bb89f178101d","year":2023},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.919369Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:36a8194ecbe66478a5dc3d5364615c2561b16807d82f2287dcd7712b94e3bc1b","observation_id":"6f7e5c63-606c-43e8-8b7a-b99ee3389288","resolution":{"observed_at":"2026-08-16T04:21:30.732650Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.712155Z","title":"Geometric algorithms and combinatorial optimization , volume 2 of Algorithms and Combinatorics","venue":null,"work_id":"e6154638-0b96-42e0-8132-375f842e0eeb","year":1993},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.923423Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:67ed5aa92d516eea74f21338e9b9b069f59b02696862a1ec162d6be308a71d17","observation_id":"6d62b098-ea38-495d-b8b7-0fbbefbdf0e2","resolution":{"observed_at":"2026-08-16T04:21:30.717366Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.09075","last_updated":"2024-12-12T08:59:33Z","snapshot_observed_at":"2026-08-16T15:59:09.690605Z","submitted_at":"2024-12-12T08:59:33Z","title":"A note on Bourgain's slicing problem","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.09075","snapshot_observed_at":"2026-08-16T04:21:29.927638Z","title":"A note on B ourgain's slicing problem","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.927638Z"},"links":{"cited_paper":"/paper/2412.09075","citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:a5d6c25960976d97c84a1bc2607857b67438dd905ead97c6c7f5b020ff4cd1c3","observation_id":"00076572-f2dd-47e1-bbc3-606704ca288e","resolution":{"observed_at":"2026-08-16T04:21:29.927638Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.695961Z","title":null,"venue":null,"work_id":"49d496f1-f660-4410-b8e7-971413e9a0e0","year":2017},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.932087Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:a84ca0acd22cd63be540d9dfd56bcfb9f81ee9b036cf04c1483098d6956bd1a1","observation_id":"58f7750c-437a-4833-9adc-2545583fa89a","resolution":{"observed_at":"2026-08-16T04:21:30.700718Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.680575Z","title":"Logarithmic S obolev inequalities and stochastic I sing models","venue":null,"work_id":"6499075a-a2f6-464c-a34c-e9c5ee6360f7","year":1987},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.936114Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:91a28a78332ebaad06f70f50a518279e88982aa084b78d11ecfb9918a806dcc5","observation_id":"802c1044-dfd6-43cd-be0d-c8928733cf5c","resolution":{"observed_at":"2026-08-16T04:21:30.685476Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.665759Z","title":"On the geometry of differential privacy","venue":null,"work_id":"e776785c-3759-489a-b6cc-a22d6e7eb380","year":2010},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.940219Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:3a6329a1d1e46b20a65dd9f9d306c279ce2b52ebb87ae8fc843fa48f68b3e253","observation_id":"8bec6cb8-2d03-4b43-8c66-1c7c3167102c","resolution":{"observed_at":"2026-08-16T04:21:30.670546Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2008.02146","last_updated":"2024-08-29T15:34:04Z","snapshot_observed_at":"2026-08-04T14:52:38.606558Z","submitted_at":"2020-08-05T14:08:16Z","title":"Reducing Isotropy and Volume to KLS: Faster Rounding and Volume Algorithms","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.02146","snapshot_observed_at":"2026-08-16T04:21:29.944052Z","title":null,"venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.944052Z"},"links":{"cited_paper":"/paper/2008.02146","citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:de2f67c3af40b74cc29732b3f0fc135de1f99d2f6977840c10cf609aa060dac8","observation_id":"a6c5449e-250f-42ee-9a3c-ad1b06a7b569","resolution":{"observed_at":"2026-08-16T04:21:29.944052Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.651206Z","title":"Bourgain's slicing problem and KLS isoperimetry up to polylog","venue":null,"work_id":"663d5270-426c-4dbf-a6d3-587985de21fc","year":2022},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.948112Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:38eb8f0ccd275378f86a6350dab357e634ad1167cb25a59e60801cfbf4fc330c","observation_id":"10c769b5-f366-48a4-a2ca-a517cd8c4180","resolution":{"observed_at":"2026-08-16T04:21:30.655790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.636762Z","title":"Isoperimetric inequalities in high-dimensional convex sets","venue":null,"work_id":"d56633f8-311a-4c92-b3ab-2ee3a0a5913a","year":2024},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.952400Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:34a9c1133aae87144339cc50286961dc4f88ee3ae82d26c99e22a7f82302374f","observation_id":"75d8dea8-0e33-4df3-ad52-8ad70c69809e","resolution":{"observed_at":"2026-08-16T04:21:30.641585Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.622006Z","title":"Logarithmic bounds for isoperimetry and slices of convex sets","venue":null,"work_id":"fc4305d2-b915-4b90-b403-341a0cc3730d","year":2023},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.957196Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:99658b7282763722682fa24c7c97faef76ab426570baa8ecfbf3c78688aeaa2d","observation_id":"de95d1f0-6ddc-48f5-b710-c4bcba40cc16","resolution":{"observed_at":"2026-08-16T04:21:30.626428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.607334Z","title":"Blocking conductance and mixing in random walks","venue":null,"work_id":"50834541-e104-41b0-9d72-b36d9e0bccc4","year":2006},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.961493Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:17e26e9f28010e355a43e49c02839bef5dcef635f0e17fe020169ab98956f6d7","observation_id":"983f72f5-26e0-4719-ab53-e1ef03127368","resolution":{"observed_at":"2026-08-16T04:21:30.611933Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.591409Z","title":"Isoperimetric problems for convex bodies and a localization lemma","venue":null,"work_id":"c069320f-dc83-41fe-936f-520cab023830","year":1995},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.965973Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:2c5eb1ed16558578ff8a45806f649efd2a74f08743f7f84ae2bd1b521f39c7a4","observation_id":"70e44e53-2e24-4b4f-80ec-f951316457b1","resolution":{"observed_at":"2026-08-16T04:21:30.596400Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.575756Z","title":"Random walks and an O^*(n^5) volume algorithm for convex bodies","venue":null,"work_id":"48db3537-9130-4e6e-b042-d6bfa9d20ee2","year":1997},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.970441Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:f0e314c7bfd681880c808e9fdb420aede3aace46f60fe6a0f999e0642f5d5467","observation_id":"3bcad6b3-a720-4bae-b63d-8f6a0f66ea0e","resolution":{"observed_at":"2026-08-16T04:21:30.580528Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.560623Z","title":null,"venue":null,"work_id":"28ea70d9-e018-42fc-8ade-52c8161d0c46","year":2022},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.974814Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:1b5009daa937a202c83ab719215ab2a77352a862d947853984a4dc776d4617bf","observation_id":"d148ec8d-f63c-41cf-a6a2-d5c925f8fcaf","resolution":{"observed_at":"2026-08-16T04:21:30.565498Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.545988Z","title":"The strong data processing inequality under the heat flow","venue":null,"work_id":"27255aca-84bf-4fde-9a5a-2834250f8b30","year":2025},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.979193Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:b6a2a96dbaffe20ee43c63538f1b003c57bdea17e80b355b65397ea0b4b444c3","observation_id":"178487b7-4fdc-42f5-9e70-b2b5d3946dff","resolution":{"observed_at":"2026-08-16T04:21:30.550843Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.530981Z","title":null,"venue":null,"work_id":"46799b36-eb73-44cc-bd16-914b0e1d63f5","year":2006},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.983601Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:81d954f4a617a63e53de408410f912fab987f5ec1fda9c687bb2c047848c1e3b","observation_id":"5c4ff1f5-b982-420a-8e28-244820faea0e","resolution":{"observed_at":"2026-08-16T04:21:30.535749Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.515655Z","title":null,"venue":null,"work_id":"34d54fe5-c3c0-4627-9aee-6cc624df2b11","year":2025},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.987791Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:498f3a6c002fc58e4f260c2d3e758b7da6fe1020ad771383acff4a3f7c2fed06","observation_id":"7f7220c0-9626-4275-8035-e617f256014b","resolution":{"observed_at":"2026-08-16T04:21:30.520462Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.502385Z","title":"Vempala, and Matthew S","venue":null,"work_id":"348dd11e-c24d-4317-bb2b-03e6daa6d31a","year":2024},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.992639Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:d8d9c509b2b76b6c57ac096d981a90e97d28fbc50ec2dc24994666aeb5ed6b5c","observation_id":"fa306a28-f9d3-4d57-95c0-86a24ac4d349","resolution":{"observed_at":"2026-08-16T04:21:30.506370Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.488368Z","title":null,"venue":null,"work_id":"1da2183c-628c-484f-a4ed-dce4f7dd2d8d","year":2025},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:29.996898Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:a02f34a6f8d16ccbf3b5d9d986e7394acf53fb9557fc4d94c85aa15cdd3ff442","observation_id":"8e73cc5b-f1d4-450e-bcac-1117cf673415","resolution":{"observed_at":"2026-08-16T04:21:30.492860Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.473462Z","title":"A simple analytic proof of an inequality by P","venue":null,"work_id":"65f6ede7-ef3a-4990-b356-aaf8f753e9f0","year":1994},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:30.001230Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:74805a46c28a8369a74fb5ac0516eafc1eada3b977ff1eedeb44939622cb8c71","observation_id":"53a7f1f4-470a-4326-ac36-a0d6b8489bf0","resolution":{"observed_at":"2026-08-16T04:21:30.478103Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.458607Z","title":"Spectral gap, logarithmic S obolev constant, and geometric bounds","venue":null,"work_id":"6d0afd6b-f34f-4011-b3cf-46b6ceac0705","year":2004},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:30.005807Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:aa2440897319f6ad7bc1b46c1089b517434c966f20c6eaceed8d06426cb50117","observation_id":"a7410145-cc33-45f5-9b13-c70a9c37668c","resolution":{"observed_at":"2026-08-16T04:21:30.463340Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.443262Z","title":"Faster mixing via average conductance","venue":null,"work_id":"acceafff-7d16-494e-b6f0-c52516f22bec","year":1999},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:30.010306Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:1550384036c3a7b00d1c1014fa76031ad12df61a8467240e2eae97a1cf3a71a0","observation_id":"ba8b0467-4cac-4572-92e9-615d1c31c19c","resolution":{"observed_at":"2026-08-16T04:21:30.447852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.428779Z","title":"How to compute the volume? Jber","venue":null,"work_id":"afa74608-8869-44d2-bdad-58bf94580dfc","year":1990},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:30.014739Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:1c6ebb942531b9ac23f0662617d4dbd1afbfcab6c8dd9991bfb86d2175c8e688","observation_id":"2e3a9781-a8b6-4755-9067-0803c6bdf689","resolution":{"observed_at":"2026-08-16T04:21:30.433479Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.415047Z","title":"Hit-and-run mixes fast","venue":null,"work_id":"638cfb12-5b55-41ef-8e8b-94e00beff502","year":1999},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:30.018934Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:42efd4b7274a911194bc6b2be9a45a5143ed63de25a104f60e7980e568f3c49a","observation_id":"5ff2faf3-7203-46b3-aba0-0724d5504f85","resolution":{"observed_at":"2026-08-16T04:21:30.419054Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.401565Z","title":"The mixing rate of M arkov chains, an isoperimetric inequality, and computing the volume","venue":null,"work_id":"162b8bee-339f-4ea2-b314-ce10bfece718","year":1990},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:30.023261Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:75048cf0e4e55607a67d47cae56db91c7afa074cd9e350b1aa61576728216288","observation_id":"c4f40841-e8cd-4c25-bc81-a2b5aca96bc9","resolution":{"observed_at":"2026-08-16T04:21:30.405756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.386295Z","title":"Random walks in a convex body and an improved volume algorithm","venue":null,"work_id":"c46064c9-c930-44d7-a46a-14598ea815f1","year":1993},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:30.028017Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:265b7255e01cdc87993fdd6e37f2f120b5eee7ae4af9a536659f872b667264b4","observation_id":"b4a761ee-9b72-418f-8532-cb75524099f6","resolution":{"observed_at":"2026-08-16T04:21:30.391067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.371911Z","title":"Structured logconcave sampling with a restricted G aussian oracle","venue":null,"work_id":"bba5561b-5b40-41eb-84fe-53ecb9afdebb","year":2021},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:30.032685Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:72044fc506417743f9d4c1baaa48ef8e20fb474aa940ee34e90b76b4e93a62a3","observation_id":"98930c9f-2e4e-441e-bc61-2a6fa5f51be3","resolution":{"observed_at":"2026-08-16T04:21:30.376588Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.357444Z","title":null,"venue":null,"work_id":"273e91e9-f05f-495f-bcf0-c82623f6c6a4","year":2006},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:30.037126Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:0953e07d0f4dc6ce7d3c589fb8e77d0a2b4ec110c9176f21ee03ff179d76c233","observation_id":"27773d83-9de9-4a97-bd3b-261b55f2b63b","resolution":{"observed_at":"2026-08-16T04:21:30.362157Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.342391Z","title":null,"venue":null,"work_id":"a502905b-fd79-48ff-a4c7-e2465a48bed7","year":2006},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:30.041651Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:f8775a72936cc8bedcf81f2a476a3f62e73e257e4730037ae81a7d743dc9ff2c","observation_id":"c4bd0cf5-8a51-461d-9704-efd630212ac9","resolution":{"observed_at":"2026-08-16T04:21:30.347114Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.327242Z","title":null,"venue":null,"work_id":"087b9528-5614-4686-8f2b-15894ff1b422","year":2006},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:30.046184Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:6e1726eab430706c30dc6d5c3d9909975d1af6bf987a313a0a33d9d87cfc0bbe","observation_id":"83aa7d66-9267-4688-a01d-e10740b2473a","resolution":{"observed_at":"2026-08-16T04:21:30.331907Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.311580Z","title":null,"venue":null,"work_id":"a52fe86a-2c54-4eb9-b3af-f42498c741c0","year":2007},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:30.050950Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:6f81f30a1590c1410b87717f8535552e4c97cb14636eaf91a154d3bc601fc3b4","observation_id":"e67bba47-25a7-46c4-bb34-c8d2ecfb7a9e","resolution":{"observed_at":"2026-08-16T04:21:30.316320Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.296665Z","title":null,"venue":null,"work_id":"b58edb9b-68ea-49cf-8849-ca53dae57607","year":2024},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:30.055309Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:2bf41e24c757bfd98bec3fb021f141b5c918fc9c70c0591a14e6e38bddf832d7","observation_id":"8c8c61ba-35ad-4f54-9929-b35842988244","resolution":{"observed_at":"2026-08-16T04:21:30.301329Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.281332Z","title":"Isoperimetric and concentration inequalities: Equivalence under curvature lower bound","venue":null,"work_id":"ab7d04e7-b53f-49f3-a407-be0d72bd95ff","year":2010},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:30.059554Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:1471ba213fd40b2b35fc889ed805ce895a5b7730c1915a7f065baec7a1124633","observation_id":"0d7d3dba-45dd-4c7d-9d6e-961a50b3cd4a","resolution":{"observed_at":"2026-08-16T04:21:30.286327Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.265338Z","title":"R\\' e nyi differential privacy","venue":null,"work_id":"35e983c6-ab7a-4735-8447-eed4d6b66bcc","year":2017},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:30.064357Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:f35f781b7b729906c2ef9f639f72f2974f8d236aa1b9a703bd122e6d942c3770","observation_id":"9fb4e1dd-a8d1-4963-89ed-974eb82de724","resolution":{"observed_at":"2026-08-16T04:21:30.270592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.249930Z","title":"Mechanism design via differential privacy","venue":null,"work_id":"70972d0c-56d4-452e-ae5d-3cf60f74f6f5","year":2007},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:30.068741Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:8c41def6e8e8444728ac9c8257e90b3d84c98f5b25536c55310c539e5400869c","observation_id":"46fa5890-9095-4615-8d37-b97b3b430a36","resolution":{"observed_at":"2026-08-16T04:21:30.254476Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.235471Z","title":"Payne and Hans F","venue":null,"work_id":"76fd852a-b00f-4aa1-97fe-e59472ba59fb","year":1960},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:30.073264Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:56c72ad9258772c6a52eb0a7d406e695445be8f6452bb431c08d1153e284605e","observation_id":"472adc5c-1d4c-4069-a09a-40e3bc81ea9c","resolution":{"observed_at":"2026-08-16T04:21:30.239643Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.221415Z","title":null,"venue":null,"work_id":"943a4f90-3713-4b61-ab66-eb98e5381b6e","year":1984},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:30.077609Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:d0a6978c34787d637b95dd5edf6e30427960253da6fc419182da08d622c992a2","observation_id":"6ddd1cd0-a360-40f1-9f7c-83ae2ee0cbc4","resolution":{"observed_at":"2026-08-16T04:21:30.225368Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.207223Z","title":"A community-driven global reconstruction of human metabolism","venue":null,"work_id":"84a10ca8-18cd-4576-8d66-c1f53629a402","year":2013},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:30.082261Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:21f67f3cd99303e013da2e49adf1910eeb14ef60388e426c205801dee6f6d98c","observation_id":"4ecc200c-c79f-4fef-ab2f-9e820118fc1d","resolution":{"observed_at":"2026-08-16T04:21:30.211599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:21:30.189965Z","title":"R\\' e nyi divergence and K ullback- L eibler divergence","venue":null,"work_id":"970204ae-bf6a-4d03-a00a-7262ace5459b","year":2014},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:30.086657Z"},"links":{"citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:653112d96f17ceae0c7f47cdee99db0f2810eeb7f97c761f8733e37b6ed1ffde","observation_id":"3bdb4302-88a3-454c-9696-578fdd1afbd3","resolution":{"observed_at":"2026-08-16T04:21:30.196539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.05623","last_updated":"2025-06-28T00:24:10Z","snapshot_observed_at":"2026-08-13T17:30:52.594744Z","submitted_at":"2025-02-08T16:07:12Z","title":"Mixing Time of the Proximal Sampler in Relative Fisher Information via Strong Data Processing Inequality","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.05623","snapshot_observed_at":"2026-08-16T04:21:30.091313Z","title":"Mixing time of the proximal sampler in relative F isher information via strong data processing inequality","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-16T04:21:30.091313Z"},"links":{"cited_paper":"/paper/2502.05623","citing_paper":"/paper/2505.01937"},"observation_digest":"sha256:78151e3e62d5e61a3665cfc63c8544e9c3c66df5cc98fbbc3ee69a8917b1378b","observation_id":"2378b416-ebda-4b08-be1b-06d7d1b34a0d","resolution":{"observed_at":"2026-08-16T04:21:30.091313Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.01937","last_updated":"2025-05-03T22:14:04Z","latest_version":1,"primary_category":"cs.DS","snapshot_observed_at":"2026-08-18T09:07:28.648630Z","submitted_at":"2025-05-03T22:14:04Z","title":"Faster logconcave sampling from a cold start in high dimension"},"reference_resolution":{"displayed":58,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":21,"verified_exact":0,"verified_fuzzy":37},"total_outbound_references":58},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 1 inbound Pith citation observation for arXiv:2505.01937."}