{"as_of":"2026-08-15T00:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5f09d495e8fa852db693a4cda469dbad50b23582ec8378f3e432d42d327b3fc0","coverage":[{"denominator":26,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":26,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T01:10:41.013280Z","state":"measured"},{"denominator":27,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":27,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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":"2026-06-30T04:13:23.421102Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-01T15:15:47.755694Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"cited_work":{"arxiv_id":"2602.10714","doi":null,"metadata_source":"pith","pith_arxiv_id":"2602.10714","snapshot_observed_at":"2026-07-01T15:15:47.755694Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","venue":"stat.CO","work_id":"9a412b89-c171-4728-898f-338c51da080b","year":2026},"citing_paper":{"arxiv_id":"2606.30018","last_updated":"2026-06-29T09:23:16Z","snapshot_observed_at":"2026-08-08T17:03:06.126320Z","submitted_at":"2026-06-29T09:23:16Z","title":"Error bounds for simultaneous Wasserstein contractive adaptive increasingly rare MCMC","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-30T04:13:23.421102Z"},"links":{"cited_paper":"/paper/2602.10714","citing_paper":"/paper/2606.30018"},"observation_digest":"sha256:2a371fc5c22855eeb3ba9157579013c0363724c57194a57fa4aaf76cc3d5f21f","observation_id":"b8233641-fafe-4c49-a2d5-133987ae06a7","resolution":{"observed_at":"2026-07-01T15:15:47.760245Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2602.10714/citation-record","integrity":"/paper/2602.10714/integrity","json":"/paper/2602.10714/citation-record.json","paper":"/paper/2602.10714"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:39.026224Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:39.026224Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:0e391a6225d7ba235e4a43b007789f473044b971b41cec47b4ed951e2fb5c8b5","observation_id":"248ac549-238f-43c8-bb44-36fbffcd1680","resolution":{"observed_at":"2026-08-03T01:10:39.026224Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:39.080817Z","title":"Unadjusted Hamiltonian MCMC with stratified Monte Carlo time integration","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:39.080817Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:d487cf6db8821b695d0b64fdba1490e127f5f57581a1015e3aa46b2ea774ceea","observation_id":"6750d001-d3ec-4404-9c39-0876b90cc23f","resolution":{"observed_at":"2026-08-03T01:10:39.080817Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:39.154456Z","title":"Polar factorization and monotone rearrangement of vector-valued functions","venue":null,"work_id":null,"year":1991},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:39.154456Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:1eae634146931d07d414a0cf4dddf81e22ffc8ee070472513c3d4f9700e93280","observation_id":"47152359-b046-4fb9-9b78-a51ddeb9b6cb","resolution":{"observed_at":"2026-08-03T01:10:39.154456Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:39.260440Z","title":"Carlier, A","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:39.260440Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:0d88c811fbd82ec7984479255c0ce7b057d64b6d39f2ae9a9355c62ec067d22c","observation_id":"41c92d62-7aa3-4701-be36-9b14dc1026ee","resolution":{"observed_at":"2026-08-03T01:10:39.260440Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:39.332632Z","title":"Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:39.332632Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:5abdf534ae85d332c76529eaa4a7804c26e9a54bd66f40f530e834d80b4eb8e6","observation_id":"def4f216-fec6-498d-9ffc-3214e89ba7c0","resolution":{"observed_at":"2026-08-03T01:10:39.332632Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:39.459540Z","title":"Chatterji, Peter L","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:39.459540Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:1244a99beac4cfbb81840ca6a1863f47ce7e8fcec46371d80a846e33f8283ba8","observation_id":"fb105c5b-9209-4c00-9d19-e6a116869509","resolution":{"observed_at":"2026-08-03T01:10:39.459540Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:39.536944Z","title":"Log-concave sampling","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:39.536944Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:57072d6be69f0a559eed557260e4c3f304d44195cdf0d56738cb931fe246f202","observation_id":"8a8b41bf-e5bc-4e15-a67a-870d147e4f12","resolution":{"observed_at":"2026-08-03T01:10:39.536944Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:39.643149Z","title":"Air Markov Chain Monte Carlo , January 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:39.643149Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:0417fe6a5e7dccd1096b9ec1150c4046d743ced433e228d6b7f506ce57e9d96c","observation_id":"a08805cc-d30c-4324-8c6d-28af5ee25a0a","resolution":{"observed_at":"2026-08-03T01:10:39.643149Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:39.753731Z","title":"Dalalyan and Avetik G","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:39.753731Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:91435084e28b141d30e83870bd816a57cef410374f4851fe16e7ab4b7961a339","observation_id":"ccf8813c-1ed6-48a0-8708-6080eea2c735","resolution":{"observed_at":"2026-08-03T01:10:39.753731Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:39.812502Z","title":"Analysis of langevin monte carlo via convex optimization","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:39.812502Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:304e477db25c306ce5c28f3ad72279fbcb9deef6f132d641c99c7a240b3a960a","observation_id":"78f39e8f-74e2-4d8b-a12f-7b7b55d41d07","resolution":{"observed_at":"2026-08-03T01:10:39.812502Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:39.889188Z","title":"An Adaptive Metropolis Algorithm","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:39.889188Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:266d3b4e0b71889006a5ef50214a148414d1fa6b428e1aaa1f0b71ce58916bef","observation_id":"65119263-9e24-4fd5-a634-f30bf2a98000","resolution":{"observed_at":"2026-08-03T01:10:39.889188Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:39.955078Z","title":"Beyond Canonical MCMC : Preconditioning , Adaptivity , and Variational Approximations","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:39.955078Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:eb4597ffae48660ea1462d33f2025c4412b363d784bc59f1259e0fe28c2c72a3","observation_id":"443f059e-11bf-4b4a-8f16-98c41995fb1d","resolution":{"observed_at":"2026-08-03T01:10:39.955078Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:40.048019Z","title":"Roberts, and Daniel Rudolf","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:40.048019Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:fc880c29c03e0b322a135c1302bf30516d1c30a9e4104a0e49f15734cf2fbe5a","observation_id":"be314d33-8a23-4d7a-b31e-440b1b29998f","resolution":{"observed_at":"2026-08-03T01:10:40.048019Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:40.113481Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:40.113481Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:0867f9d4886c83b8d60245362e097551d972446ec0b2b4d4027b24755c7f3d84","observation_id":"bf6464a1-d20d-43f8-ba04-668c8a39e7b8","resolution":{"observed_at":"2026-08-03T01:10:40.113481Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:40.216669Z","title":"Concentration of measure and logarithmic Sobolev inequalities","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:40.216669Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:1520df82859f4c6af8dfd7a38bd355be6f94867f433d43d7fc991e205fa0c5a5","observation_id":"8d18b330-363e-4232-8f37-542fc3f526e5","resolution":{"observed_at":"2026-08-03T01:10:40.216669Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:40.300469Z","title":"Structured Logconcave Sampling with a Restricted Gaussian Oracle","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:40.300469Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:b5be4d2acc73118e88ed78869b17511a9bbdac1bc23080d09c586264bba9f001","observation_id":"62d44654-7867-411c-a40a-78daaab5bf41","resolution":{"observed_at":"2026-08-03T01:10:40.300469Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:40.365444Z","title":"Characterizing Dependence of Samples along the Langevin Dynamics and Algorithms via Contraction of \\ \\ - Mutual Information , June 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:40.365444Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:e847644fb25014e498ba01c1aa7428bc7f96a661435a99d6b49140c6a973c94e","observation_id":"792bd64d-e81f-4571-a60b-df855b73a440","resolution":{"observed_at":"2026-08-03T01:10:40.365444Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:40.446340Z","title":"Fast convergence of \\ \\ -divergence along the unadjusted langevin algorithm and proximal sampler","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:40.446340Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:0714e37b1dbf0169c3a6b23229aae4ddd4fc2bfe753bc8879a6932987ae89905","observation_id":"708e70e0-fbb9-429c-a0ad-c5bc5e813ca7","resolution":{"observed_at":"2026-08-03T01:10:40.446340Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:40.518870Z","title":"On sample complexity for covariance estimation via the unadjusted Langevin algorithm, January 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:40.518870Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:76f8057c1594865b4242e345d8ed18393dd36f2b1998e64e3bb6bfde28f4e4db","observation_id":"b2bf7c91-8704-4e7d-b865-f9017565ba7a","resolution":{"observed_at":"2026-08-03T01:10:40.518870Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:40.592360Z","title":"Optimal Scaling and Shaping of Random Walk Metropolis via Diffusion Limits of Block-I","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:40.592360Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:d3061d7bbfc5e79f04de4c1cfec8b893d64c47fac2cb5c52eb2a2299b1c91e6c","observation_id":"4de515f8-f930-4814-b7d1-62f7bad3a299","resolution":{"observed_at":"2026-08-03T01:10:40.592360Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:40.660173Z","title":"Approximations and Scaling Limits of Markov Chains with Applications to MCMC and Approximate Inference","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:40.660173Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:ace83182a2adca8f1fa351054d99d23703cc76ac76a0be83da95453a337de105","observation_id":"60d8bc97-cce3-47f1-8ea6-7662129352ce","resolution":{"observed_at":"2026-08-03T01:10:40.660173Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:40.738491Z","title":null,"venue":null,"work_id":null,"year":1959},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:40.738491Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:ae46fecb604b2ca8866824949f17c7ac4b7bebd8d15e7e98869cbff5a572a693","observation_id":"93b5ec44-5a71-4bd4-ad41-3d42ba8f773c","resolution":{"observed_at":"2026-08-03T01:10:40.738491Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:40.808794Z","title":null,"venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:40.808794Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:1cd40090678e45d524d46e58e30c385c1a2308c994963b93888fd140196ecab0","observation_id":"51a45411-5b08-445c-83fa-4129b12bf423","resolution":{"observed_at":"2026-08-03T01:10:40.808794Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:40.864663Z","title":"Optimal Preconditioning and Fisher Adaptive Langevin Sampling","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:40.864663Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:694fd674db762784c499f224f2780139be308d4457a7b18f5329be2b3ec61848","observation_id":"35c63e91-c890-4933-80ad-3281799e79ff","resolution":{"observed_at":"2026-08-03T01:10:40.864663Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:40.954186Z","title":"High-Dimensional Probability: An Introduction with Applications in Data Science , volume 47","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:40.954186Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:2f38d230c3fe36f9ad5791035a8a0e9b7de0bfba6f7a0fd8fdc67cb0dd94fbea","observation_id":"d40d28c4-bf74-43ac-8faa-fb620d33f954","resolution":{"observed_at":"2026-08-03T01:10:40.954186Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T01:10:41.013280Z","title":"Minimax Mixing Time of the Metropolis-Adjusted Langevin Algorithm for Log-Concave Sampling","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-03T01:10:41.013280Z"},"links":{"citing_paper":"/paper/2602.10714"},"observation_digest":"sha256:bf805c12d611127a4ef96207c684bc54deb2bbf9f37f8e13b0dcf58c59783255","observation_id":"0faa2a16-4f24-4d7f-b876-0bd3cce7ca3e","resolution":{"observed_at":"2026-08-03T01:10:41.013280Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2602.10714","last_updated":"2026-06-26T01:09:30Z","latest_version":2,"primary_category":"stat.CO","snapshot_observed_at":"2026-08-08T17:03:39.094184Z","submitted_at":"2026-02-11T10:19:56Z","title":"A Non-asymptotic Analysis for Learning and Applying a Preconditioner in MCMC"},"reference_resolution":{"displayed":26,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":26,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":26},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2602.10714."}