{"as_of":"2026-08-09T11:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e3fb2a51c9b9aa1b4769956294f345ca1ab17aa4f4ba73cf1f19d358c2542713","coverage":[{"denominator":78,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":78,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T07:36:39.751274Z","state":"measured"},{"denominator":78,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":78,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2510.25824/citation-record","integrity":"/paper/2510.25824/integrity","json":"/paper/2510.25824/citation-record.json","paper":"/paper/2510.25824"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T07:36:34.499289Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:34.499289Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:73c1979b12b39744a1119ed7060168429821ec7ae84bb07a087bb613b630d6c7","observation_id":"6cb3f61a-47df-4021-87f7-d30dd4ab0162","resolution":{"observed_at":"2026-08-04T07:36:34.499289Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.03334","last_updated":"2025-01-07T03:01:49Z","snapshot_observed_at":"2026-08-06T06:00:26.963770Z","submitted_at":"2024-10-23T19:56:57Z","title":"Neural Network Prediction of Strong Lensing Systems with Domain Adaptation and Uncertainty Quantification","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.03334","snapshot_observed_at":"2026-08-04T07:36:34.572169Z","title":"D., 2024, in 38th conference on Neural Information Processing Systems","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:34.572169Z"},"links":{"cited_paper":"/paper/2411.03334","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:c4ed9e5f527e1615fc5f549f87996e90b538f3be508481fa680789d58fb46f7d","observation_id":"3f2b3cf8-65f4-4093-8f1f-9d2b2e657c11","resolution":{"observed_at":"2026-08-04T07:36:34.572169Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.01712","last_updated":"2022-09-05T00:31:12Z","snapshot_observed_at":"2026-07-06T13:48:46.364019Z","submitted_at":"2022-09-05T00:31:12Z","title":"ChemBERTa-2: Towards Chemical Foundation Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.01712","snapshot_observed_at":"2026-08-04T07:36:34.743066Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:34.743066Z"},"links":{"cited_paper":"/paper/2209.01712","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:7d1922221e9a04f7f927abddcfe45e6ae719972978f7958623e4fd8d35a90252","observation_id":"aebeb27c-7c00-4a9e-b624-2e5f22277b6f","resolution":{"observed_at":"2026-08-04T07:36:34.743066Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.07511","last_updated":"2022-12-07T05:08:01Z","snapshot_observed_at":"2026-07-06T11:29:34.579530Z","submitted_at":"2021-07-15T17:59:50Z","title":"A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.07511","snapshot_observed_at":"2026-08-04T07:36:34.900588Z","title":"N., Bates S., 2021, @doi [arXiv e-prints] 10.48550/arXiv.2107.07511 , https://ui.adsabs.harvard.edu/abs/2021arXiv210707511A p","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:34.900588Z"},"links":{"cited_paper":"/paper/2107.07511","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:d83bb8d4f655d82ef64c479b462a6d12d2da1b4ea763424b31f809f049103591","observation_id":"4dbb86f3-68f8-4117-963c-3105c939c250","resolution":{"observed_at":"2026-08-04T07:36:34.900588Z","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":"2026-08-04T07:36:35.066411Z","title":"H., Hearin A","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:35.066411Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:4ab3e3537e2a87216b978246b1c96ecb19cf496d1adb9a57dea91af0a5782024","observation_id":"4084dcda-0f25-4d1d-a7ce-c65e9a165f09","resolution":{"observed_at":"2026-08-04T07:36:35.066411Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.13891","last_updated":"2021-09-28T17:43:25Z","snapshot_observed_at":"2026-08-06T18:29:00.367860Z","submitted_at":"2021-09-28T17:43:25Z","title":"Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect","version":1},"cited_work":{"arxiv_id":"2109.13891","doi":"10.48550/arxiv.2109.13891","metadata_source":"pith","pith_arxiv_id":"2109.13891","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect","venue":"stat.ML","work_id":"d8eae7d1-ca0e-40ee-a84e-b8e4b62e114a","year":2021},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:35.284533Z"},"links":{"cited_paper":"/paper/2109.13891","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:9e2202f6f450844f8d640a945618486b318fa3a0d7e141270f50676b4056a425","observation_id":"0869f98e-805f-47b8-a883-c95f3d754e75","resolution":{"observed_at":"2026-08-04T07:38:37.513144Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-04T07:36:35.403641Z","title":null,"venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:35.403641Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:9f1cf16b20a2226a14266450af1865d1f35a83d7f3be37a35b6c24f4d6cf9433","observation_id":"04b6740a-a981-46e7-97e6-fbad8d1af7b9","resolution":{"observed_at":"2026-08-04T07:36:35.403641Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1701.02434","last_updated":"2018-07-16T02:46:10Z","snapshot_observed_at":"2026-07-06T05:25:35.928343Z","submitted_at":"2017-01-10T04:26:06Z","title":"A Conceptual Introduction to Hamiltonian Monte Carlo","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1701.02434","snapshot_observed_at":"2026-08-04T07:36:35.548912Z","title":"arXiv:1701.02434","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:35.548912Z"},"links":{"cited_paper":"/paper/1701.02434","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:ee460f1c508c35238b3e04dd6c26fd564facd7453ab64e9dfc0fd63ab995f88e","observation_id":"e4deaa03-8737-4256-a13b-750f37546fde","resolution":{"observed_at":"2026-08-04T07:36:35.548912Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1607.03188","last_updated":"2018-04-23T13:30:35Z","snapshot_observed_at":"2026-08-04T04:34:36.728470Z","submitted_at":"2016-07-11T22:30:50Z","title":"The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1607.03188","snapshot_observed_at":"2026-08-04T07:36:35.718440Z","title":"arXiv:1607.03188","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:35.718440Z"},"links":{"cited_paper":"/paper/1607.03188","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:6c304effb0e3c273fad3b2a32c91d9ac65fdac5a612ce2f1ff645929c20a1d43","observation_id":"f22571de-83f3-4439-995d-ef98095cada7","resolution":{"observed_at":"2026-08-04T07:36:35.718440Z","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":"2026-08-04T07:36:35.859222Z","title":"Pergamon Press","venue":null,"work_id":null,"year":1980},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:35.859222Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:9a265d0e934d7358fc71df10877698c46c8bf5a40ca5d121a90c4054044822f0","observation_id":"65fbbe5b-27ce-44c9-9674-6f56166953a5","resolution":{"observed_at":"2026-08-04T07:36:35.859222Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1510.02451","last_updated":"2017-02-17T20:49:02Z","snapshot_observed_at":"2026-08-04T19:22:23.796772Z","submitted_at":"2015-10-08T19:17:41Z","title":"The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1510.02451","snapshot_observed_at":"2026-08-04T07:36:36.013980Z","title":"J., Doucet A., 2015, @doi [arXiv e-prints] 10.48550/arXiv.1510.02451 , https://ui.adsabs.harvard.edu/abs/2015arXiv151002451B p","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:36.013980Z"},"links":{"cited_paper":"/paper/1510.02451","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:bd68b3ca1007a844aafd9727052592c1e51b5c5b56402069ca1cabe113fc0782","observation_id":"ad6f6890-c705-41c9-9603-1fa8aa69e567","resolution":{"observed_at":"2026-08-04T07:36:36.013980Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1402.4102","last_updated":"2014-05-12T06:38:21Z","snapshot_observed_at":"2026-08-03T19:28:23.939703Z","submitted_at":"2014-02-17T19:57:59Z","title":"Stochastic Gradient Hamiltonian Monte Carlo","version":2},"cited_work":{"arxiv_id":"1402.4102","doi":"10.48550/arxiv.1402.4102","metadata_source":"pith","pith_arxiv_id":"1402.4102","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"Stochastic Gradient Hamiltonian Monte Carlo","venue":"stat.ME","work_id":"6a694b17-4b6e-44c9-8d07-e8c2e569f28c","year":2014},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:36.074300Z"},"links":{"cited_paper":"/paper/1402.4102","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:c0330d420fe0e9ef4ce5bfbd4f113817d2ef7413def6cd396960955c383b4167","observation_id":"ba490d28-abb5-4a83-acbc-8aea560e783b","resolution":{"observed_at":"2026-08-04T07:38:36.850705Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-04T07:36:36.156656Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:36.156656Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:c8f48ca8ec9064ca77eb5225eb9e485b11ac073fcf12754391643959650d1184","observation_id":"1b1dbdcd-9dbf-416a-8b03-3bfe8349bf1e","resolution":{"observed_at":"2026-08-04T07:36:36.156656Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.02311","last_updated":"2022-10-05T06:02:24Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-04-05T16:11:45Z","title":"PaLM: Scaling Language Modeling with Pathways","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.02311","snapshot_observed_at":"2026-08-04T07:36:36.239000Z","title":"arXiv:2204.02311","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:36.239000Z"},"links":{"cited_paper":"/paper/2204.02311","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:e0b3323729b7993c847c2d16d97f9321dc7803aeb06a2ae325f13757b0be1e3c","observation_id":"3066c5f5-2d51-4e7f-9a89-bd668b80644d","resolution":{"observed_at":"2026-08-04T07:36:36.239000Z","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":"2026-08-04T07:36:36.349109Z","title":"N., Wild S","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:36.349109Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:7ec6cadf806d6dca744ce20c9f392ef22e46e94b441bf75760b790a3517ee417","observation_id":"e72f0137-dc8d-4096-837d-f34320c58b59","resolution":{"observed_at":"2026-08-04T07:36:36.349109Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.11410","last_updated":"2022-09-02T10:42:50Z","snapshot_observed_at":"2026-07-06T13:23:47.847351Z","submitted_at":"2022-06-22T22:34:36Z","title":"Automatic Zig-Zag sampling in practice","version":2},"cited_work":{"arxiv_id":"2206.11410","doi":"10.48550/arxiv.2206.11410","metadata_source":"pith","pith_arxiv_id":"2206.11410","snapshot_observed_at":"2026-08-04T12:16:13.904932Z","title":"Automatic Zig-Zag sampling in practice","venue":"stat.CO","work_id":"ffc4272b-12bd-4fa8-9bbb-678438f30219","year":2022},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:36.408196Z"},"links":{"cited_paper":"/paper/2206.11410","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:102706877d968a609e4b718fa4a453d53012a5b3137c5cb77cb4f765b77acf2f","observation_id":"94d0b11c-e1c2-4e2d-8240-9aa51af0187d","resolution":{"observed_at":"2026-08-04T07:38:36.452785Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1410.8516","last_updated":"2015-04-10T12:27:56Z","snapshot_observed_at":"2026-07-06T03:59:05.142685Z","submitted_at":"2014-10-30T19:44:20Z","title":"NICE: Non-linear Independent Components Estimation","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1410.8516","snapshot_observed_at":"2026-08-04T07:36:36.487623Z","title":"arXiv:1410.8516","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:36.487623Z"},"links":{"cited_paper":"/paper/1410.8516","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:4536790f3cb0f70a52204752e6a7d7ec7858bbef8b8400db332a357720f13e52","observation_id":"4a6f7bf0-df90-4bfa-b694-03eb51e65242","resolution":{"observed_at":"2026-08-04T07:36:36.487623Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.06905","last_updated":"2022-08-01T21:07:58Z","snapshot_observed_at":"2026-07-06T12:18:14.988251Z","submitted_at":"2021-12-13T18:58:19Z","title":"GLaM: Efficient Scaling of Language Models with Mixture-of-Experts","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.06905","snapshot_observed_at":"2026-08-04T07:36:36.566867Z","title":"arXiv:2112.06905","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:36.566867Z"},"links":{"cited_paper":"/paper/2112.06905","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:6b17d302629f73e05a1251a4166c634a8f42997683bae94f1b7ee8787b5a3ca7","observation_id":"9a54603f-628a-4d12-934c-30c4f2595c77","resolution":{"observed_at":"2026-08-04T07:36:36.566867Z","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":"2026-08-04T07:36:36.569348Z","title":"D., Pendleton B","venue":null,"work_id":null,"year":1987},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:36.569348Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:b97b62e524b00f33171d8d05b5420f1159a5f840a512dabdb25788512bae1b9b","observation_id":"81c76ceb-cc62-4279-9000-0c63aa7a160d","resolution":{"observed_at":"2026-08-04T07:36:36.569348Z","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":"2026-08-04T07:36:36.598321Z","title":"J., Deem M","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:36.598321Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:45ed5de9505a1acae99c61c4da5e47c3d30abf3cf8f04a3130bfc69d3c353c7d","observation_id":"8cbba994-20e8-40b8-844e-2c92ed319ba5","resolution":{"observed_at":"2026-08-04T07:36:36.598321Z","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":"2026-08-04T07:36:36.698064Z","title":"P., 2008, @doi [ ] 10.1111/j.1365-2966.2007.12353.x , https://ui.adsabs.harvard.edu/abs/2008MNRAS.384..449F 384, 449","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:36.698064Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:3105443e2f009ebe64a533be094daf28a6c4deb70c037703795a58f594b9db1b","observation_id":"442981e9-3ad7-4a3c-95fb-50cfef83adda","resolution":{"observed_at":"2026-08-04T07:36:36.698064Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1306.2144","last_updated":"2019-11-26T18:54:07Z","snapshot_observed_at":"2026-08-06T09:48:22.577598Z","submitted_at":"2013-06-10T09:22:32Z","title":"Importance Nested Sampling and the MultiNest Algorithm","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1306.2144","snapshot_observed_at":"2026-08-04T07:36:36.812226Z","title":"P., Cameron E., Pettitt A","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:36.812226Z"},"links":{"cited_paper":"/paper/1306.2144","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:a6c6616f16a3d8ca79d3410104072b27191a5273f217cfb807ad0a68465cec00","observation_id":"8af04316-5fd7-4202-92c7-041a642a4e90","resolution":{"observed_at":"2026-08-04T07:36:36.812226Z","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":"2026-08-04T07:36:36.959105Z","title":"W., Lang D., Goodman J., 2013, @doi [ ] 10.1086/670067 , http://adsabs.harvard.edu/abs/2013PASP..125..306F 125, 306","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:36.959105Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:d5e462af31af71330cb041309ad48c1d696118e37fd246ae74456fc4ab795068","observation_id":"97d6d711-caac-451a-b90c-b8daa244f5b6","resolution":{"observed_at":"2026-08-04T07:36:36.959105Z","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":"2026-08-04T07:36:37.111084Z","title":"D., 1990, @doi [Physica D: Nonlinear Phenomena] https://doi.org/10.1016/0167-2789(90)90019-L , 43, 105","venue":null,"work_id":null,"year":1990},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.111084Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:d91676313252ad4fb7dc0ab0423e453b7f885387bfbd6503362c568bc1f72af5","observation_id":"a7eb4862-6881-4785-a4cf-298a5f7dd576","resolution":{"observed_at":"2026-08-04T07:36:37.111084Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.20252","last_updated":"2024-10-01T16:34:13Z","snapshot_observed_at":"2026-07-06T19:24:30.632401Z","submitted_at":"2024-09-30T12:42:25Z","title":"What is the Role of Large Language Models in the Evolution of Astronomy Research?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.20252","snapshot_observed_at":"2026-08-04T07:36:37.114577Z","title":"arXiv:2409.20252","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.114577Z"},"links":{"cited_paper":"/paper/2409.20252","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:dbc594854635bc879f97c82bdaef7d8a404c6df0aa52e8e9a0c55703d4b05896","observation_id":"91ebfd14-8fdd-4136-9295-5d2ba739886f","resolution":{"observed_at":"2026-08-04T07:36:37.114577Z","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":"2026-08-04T07:36:37.158205Z","title":null,"venue":null,"work_id":null,"year":1984},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.158205Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:ef64429d0222ae4652267970c6b423ba512a65374579c449100eafe12bb502f3","observation_id":"2d6899dc-73ad-4e2a-8370-ef8b0786ae12","resolution":{"observed_at":"2026-08-04T07:36:37.158205Z","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":"2026-08-04T07:36:37.176168Z","title":null,"venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.176168Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:731e014016e6cad7a6bb2ef02bb7b6c74c9d1f178dfa469e097419f56ff7124b","observation_id":"fe01e283-f147-4927-b3a8-07107443bb5c","resolution":{"observed_at":"2026-08-04T07:36:37.176168Z","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":"2026-08-04T07:36:37.178102Z","title":null,"venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.178102Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:2f892bb9f165aed046a348ba6622399d7e1c84251468a000eb4c82b40a74618b","observation_id":"416441c1-ada7-4057-b33d-86763302a572","resolution":{"observed_at":"2026-08-04T07:36:37.178102Z","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":"2026-08-04T07:36:37.180337Z","title":null,"venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.180337Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:11b629bb1d6f601c8e5906fc6845a01c2d670339c4a43d3fa9bc3cb6b7a42e81","observation_id":"02c2d49f-c416-497c-81cf-d335eee3293f","resolution":{"observed_at":"2026-08-04T07:36:37.180337Z","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":"2026-08-04T07:36:37.187850Z","title":"K., 1970, @doi [Biometrika] 10.1093/biomet/57.1.97 , https://ui.adsabs.harvard.edu/abs/1970Bimka..57...97H 57, 97","venue":null,"work_id":null,"year":1970},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.187850Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:b79459db44d5aa85097ed14984ebff2a2eca431b07661792996d868bec2276ab","observation_id":"0e17cec6-8109-440e-8e47-7519355a3ee3","resolution":{"observed_at":"2026-08-04T07:36:37.187850Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1502.01852","last_updated":"2015-02-06T10:44:00Z","snapshot_observed_at":"2026-08-08T02:09:42.844283Z","submitted_at":"2015-02-06T10:44:00Z","title":"Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1502.01852","snapshot_observed_at":"2026-08-04T07:36:37.240673Z","title":"arXiv:1502.01852","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.240673Z"},"links":{"cited_paper":"/paper/1502.01852","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:58d7f71f9f127ede9130ebb3fc0d8084cd6d32f27631150bdaf288e84a0a5008","observation_id":"e8584e59-c1a7-45db-8754-3132cddf743d","resolution":{"observed_at":"2026-08-04T07:36:37.240673Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1512.03385","last_updated":"2015-12-10T19:51:55Z","snapshot_observed_at":"2026-07-06T04:39:28.429064Z","submitted_at":"2015-12-10T19:51:55Z","title":"Deep Residual Learning for Image Recognition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1512.03385","snapshot_observed_at":"2026-08-04T07:36:37.298303Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.298303Z"},"links":{"cited_paper":"/paper/1512.03385","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:f1893f34b10ac73cd24c26c518c7e7169982661bdbb3d7ebd6389c96c3967f97","observation_id":"7000cd3a-bf9a-49ce-9815-008e67f85ef2","resolution":{"observed_at":"2026-08-04T07:36:37.298303Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.14421","last_updated":"2021-04-29T15:38:46Z","snapshot_observed_at":"2026-08-05T03:51:45.877582Z","submitted_at":"2021-04-29T15:38:46Z","title":"What Are Bayesian Neural Network Posteriors Really Like?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.14421","snapshot_observed_at":"2026-08-04T07:36:37.339686Z","title":"D., Wilson A","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.339686Z"},"links":{"cited_paper":"/paper/2104.14421","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:4d80fb1451215ad122a0c89caf1d19bf2e8744bac89f49349ad3e7f74651f5b0","observation_id":"178dfc34-32a6-414b-8a82-7586aa2d6482","resolution":{"observed_at":"2026-08-04T07:36:37.339686Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1505.05770","last_updated":"2016-06-14T09:01:36Z","snapshot_observed_at":"2026-08-08T21:24:12.859869Z","submitted_at":"2015-05-21T15:36:37Z","title":"Variational Inference with Normalizing Flows","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1505.05770","snapshot_observed_at":"2026-08-04T07:36:37.397400Z","title":"arXiv:1505.05770","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.397400Z"},"links":{"cited_paper":"/paper/1505.05770","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:a1e7af80b79bdfcf934f4c8a1b2bb67b8906a6de566bf6bfcb26e7a0baf3a0bb","observation_id":"c4202c39-9366-40f6-b2e0-7fff99ecc6c3","resolution":{"observed_at":"2026-08-04T07:36:37.397400Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-04T07:36:37.471456Z","title":"P., Ba J., 2014, arXiv e-prints, https://arxiv.org/abs/1412.6980 p","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.471456Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:e59a2062d5120c6e2a15dc5adb6c6a0009eccfcce9db98fff2ec2b56318534a6","observation_id":"a8579d31-b2df-477f-8f04-f3a239d7ec4e","resolution":{"observed_at":"2026-08-04T07:36:37.471456Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.02515","last_updated":"2017-09-07T10:39:00Z","snapshot_observed_at":"2026-07-06T05:46:03.161282Z","submitted_at":"2017-06-08T11:14:24Z","title":"Self-Normalizing Neural Networks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.02515","snapshot_observed_at":"2026-08-04T07:36:37.548075Z","title":"arXiv:1706.02515","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.548075Z"},"links":{"cited_paper":"/paper/1706.02515","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:ef296549466238c81f49e25250326a2ca32d5ed5916cd9253c604e375a43e451","observation_id":"f65b94a3-3012-4d05-a8b6-7ca4ffc64377","resolution":{"observed_at":"2026-08-04T07:36:37.548075Z","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":"2026-08-04T07:36:37.604304Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.604304Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:4e388d8d76b21d6350fc60128f2cca88f8e1b19bcaa4ebc7a48752381d43a105","observation_id":"241393b3-29a7-4cfc-ae3a-de544561edb9","resolution":{"observed_at":"2026-08-04T07:36:37.604304Z","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":"2026-08-04T07:36:37.678717Z","title":null,"venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.678717Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:e6e3a051df67f77cef0344700ebb26930a1c428a239a045856b1be58d9abbf32","observation_id":"5e7283b8-052d-4d4b-9dda-c89b6d98bf0f","resolution":{"observed_at":"2026-08-04T07:36:37.678717Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1612.01474","last_updated":"2017-11-04T01:33:43Z","snapshot_observed_at":"2026-07-06T05:21:24.357721Z","submitted_at":"2016-12-05T18:54:43Z","title":"Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1612.01474","snapshot_observed_at":"2026-08-04T07:36:37.684993Z","title":"arXiv:1612.01474","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.684993Z"},"links":{"cited_paper":"/paper/1612.01474","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:a6f35ad0d62c1f686cee8d7557d0e6a8f8eed04b949f0ce749dd1fa5b6884c14","observation_id":"e672955d-27b1-4bc4-8f27-6ca59b3986d3","resolution":{"observed_at":"2026-08-04T07:36:37.684993Z","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":"2026-08-04T07:36:37.776370Z","title":"pp 4188--4188, @doi 10.1109/PIERS.2016.7735574","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.776370Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:3d02fdc0e53d8c79d3de155b97c3ec55c7af220708c6293c903f077aac102bd7","observation_id":"4355d818-03f9-4813-a5c2-4a10e540535a","resolution":{"observed_at":"2026-08-04T07:36:37.776370Z","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":"2026-08-04T07:36:37.781275Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.781275Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:3e7f66376bac1693094065e52110faf8fb6d8fa44020b79988e17b4072a7764a","observation_id":"1cd7ebc2-7a80-430c-acba-1bb97541f839","resolution":{"observed_at":"2026-08-04T07:36:37.781275Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.17917","last_updated":"2025-01-29T19:00:01Z","snapshot_observed_at":"2026-07-06T20:28:04.863224Z","submitted_at":"2025-01-29T19:00:01Z","title":"Deep Ensembles Secretly Perform Empirical Bayes","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.17917","snapshot_observed_at":"2026-08-04T07:36:37.783437Z","title":"arXiv:2501.17917","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.783437Z"},"links":{"cited_paper":"/paper/2501.17917","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:e07adf973de9d1ae538d0c9339b4bdeed68d42806f125fd6e572219ffd589b46","observation_id":"1a89973e-ce3f-49dd-8668-bcc7d40680c4","resolution":{"observed_at":"2026-08-04T07:36:37.783437Z","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":"10.1364/ao.501102","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"V., 2023, @doi [Appl","venue":"Applied Optics","work_id":"62ef0446-e1ed-44f8-826b-286abe0f94e6","year":2023},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.786068Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:e9a17522352ecc142de8c8a79fd16ff67c097e2a02b59df8b42527586d9aa998","observation_id":"1ebafd62-6b0f-407b-8cb5-77d083074564","resolution":{"observed_at":"2026-08-04T07:38:35.265357Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-04T07:36:37.788241Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.788241Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:74588bc43cc033a0663b37edd6bf51b6694da1001fd30443286086fe07e1763a","observation_id":"96bb0449-1ed7-4ba3-9a23-2084ffd1bf03","resolution":{"observed_at":"2026-08-04T07:36:37.788241Z","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":"2026-08-04T07:36:37.814165Z","title":"W., Rosenbluth M","venue":null,"work_id":null,"year":1953},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.814165Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:b32d95e858168024a4e63c7c82f67cd275c3ec28ca4ded62535ffafaa58cd51b","observation_id":"47863826-8508-46d8-9a01-4472eb1269d8","resolution":{"observed_at":"2026-08-04T07:36:37.814165Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1206.1901","last_updated":"2012-06-09T02:34:11Z","snapshot_observed_at":"2026-07-31T23:40:20.587584Z","submitted_at":"2012-06-09T02:34:11Z","title":"MCMC using Hamiltonian dynamics","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1206.1901","snapshot_observed_at":"2026-08-04T07:36:37.920440Z","title":"M., 2012, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2012arXiv1206.1901N p","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.920440Z"},"links":{"cited_paper":"/paper/1206.1901","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:8353bccd26b84192be2d0f30e0b6a2a7258a889e304061ea243485d166968eab","observation_id":"4e6f22f9-489f-43fa-adf3-733db11f0d0f","resolution":{"observed_at":"2026-08-04T07:36:37.920440Z","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":"10.1364/josaa.378829","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Journal of the Optical Society of America A","work_id":"ec168968-ff72-4336-905a-8a8a00278525","year":2020},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:37.982743Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:73b143e004c327537d566540e3b7c0b00419614e9adebdc729541ba3af2caed3","observation_id":"d49b27ed-dc07-45de-ab21-6197497678fb","resolution":{"observed_at":"2026-08-04T07:38:34.921925Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/s0010-4655(02)00754-3","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Computer Physics Communications","work_id":"5f161e80-34fa-4e84-9fe7-1b5311646492","year":2003},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:38.056759Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:4fa9b83256dc8c176170d61daf2cf5a95173ae0c666b15f5d00167988d5b66e6","observation_id":"92da8140-6921-467d-981c-6070bea205d5","resolution":{"observed_at":"2026-08-04T07:38:34.686440Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-04T07:36:38.115047Z","title":"arXiv:2303.08774","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:38.115047Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:83154dd5cbefc27f3669861791d120c7d68241a486ae1e083aa77c91d966bc11","observation_id":"7073e358-9845-4f6f-a44c-e9fa890dbd3c","resolution":{"observed_at":"2026-08-04T07:36:38.115047Z","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":"2026-08-04T07:36:38.173207Z","title":null,"venue":null,"work_id":null,"year":1962},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:38.173207Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:8c6ea1bbb6a5e1fd37d7fe7bbbf87bdcb957cc0ea9988c25558018168a6fd550","observation_id":"9e3ac645-ba8b-470f-a90b-5596d51fce7e","resolution":{"observed_at":"2026-08-04T07:36:38.173207Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.17557","last_updated":"2024-10-31T11:37:49Z","snapshot_observed_at":"2026-08-02T15:25:02.551919Z","submitted_at":"2024-06-25T13:50:56Z","title":"The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.17557","snapshot_observed_at":"2026-08-04T07:36:38.233055Z","title":"arXiv:2406.17557","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:38.233055Z"},"links":{"cited_paper":"/paper/2406.17557","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:896bffc1169d9d969aeec215bfe0bbc8eb580ae9874df4073da7728ce8007146","observation_id":"f5b64a98-f409-415c-9732-fd85119e6566","resolution":{"observed_at":"2026-08-04T07:36:38.233055Z","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":"10.1103/physreve.85.026703","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Physical Review E","work_id":"e21bc067-46d7-4ebe-807c-2674a9013f52","year":2012},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:38.289830Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:b328cbaa377dfafd557174378add5098c1d8797268000a7991545d7823020adc","observation_id":"4321dae8-eff2-4131-8eb1-d4bcbdf279c2","resolution":{"observed_at":"2026-08-04T07:38:34.310046Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1186/s40562-022-00241-y","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Geoscience Letters","work_id":"d496ac57-2aba-438b-851b-664824ca7694","year":2022},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:38.295256Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:d0e453e8c5493502be20acb52e3c40969eb784b07ceedc3df00ff6fc083da168","observation_id":"3c4ccda0-3366-4cad-8c37-cdffdb9042d3","resolution":{"observed_at":"2026-08-04T07:38:34.187076Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-04T07:36:38.422533Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:38.422533Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:983824a923fa6df33a21c0152652c55bdb9819763527770b58e085a88c38e229","observation_id":"31c8eb1c-5b3c-43ea-8a9e-dbb8dd5667cc","resolution":{"observed_at":"2026-08-04T07:36:38.422533Z","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":"2026-08-04T07:36:38.454272Z","title":"O., Tweedie R","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:38.454272Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:8dc6b6a25d23d55997e3edd0652430001d4ee95c0457a9a8df300e0302219237","observation_id":"ca2cecee-4f61-484f-84ed-041205791deb","resolution":{"observed_at":"2026-08-04T07:36:38.454272Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.08549","last_updated":"2026-05-27T20:36:48Z","snapshot_observed_at":"2026-08-07T18:55:53.925005Z","submitted_at":"2022-12-16T15:58:36Z","title":"Microcanonical Hamiltonian Monte Carlo","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.08549","snapshot_observed_at":"2026-08-04T07:36:38.468054Z","title":"B., Silverstein E., Seljak U., 2022, @doi [arXiv e-prints] 10.48550/arXiv.2212.08549 , https://ui.adsabs.harvard.edu/abs/2022arXiv221208549R p","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:38.468054Z"},"links":{"cited_paper":"/paper/2212.08549","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:66f23275583b22a323ae6ccfa136245947a69eeb7b2a49a0fc1369ba97efac5e","observation_id":"2db2a1e5-eee8-4a12-9820-fc612cbd1961","resolution":{"observed_at":"2026-08-04T07:36:38.468054Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.01707","last_updated":"2025-05-17T08:34:01Z","snapshot_observed_at":"2026-08-07T17:33:09.413955Z","submitted_at":"2025-03-03T16:20:19Z","title":"Metropolis Adjusted Microcanonical Hamiltonian Monte Carlo","version":2},"cited_work":{"arxiv_id":"2503.01707","doi":"10.48550/arxiv.2503.01707","metadata_source":"pith","pith_arxiv_id":"2503.01707","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"Metropolis Adjusted Microcanonical Hamiltonian Monte Carlo","venue":"stat.CO","work_id":"360655ba-be43-41d5-a8af-d536469fb6c0","year":2025},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:38.485824Z"},"links":{"cited_paper":"/paper/2503.01707","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:e0886ec3ff4eec5597b9831edffaae2c1970801e3fdd107543272675da311e40","observation_id":"b7f59f5d-a388-4bb5-8714-b87571232fd9","resolution":{"observed_at":"2026-08-04T07:38:33.639517Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-04T07:36:38.494274Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:38.494274Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:1041fc6232c93c249db4571ca103d06185a0a29d6fc2fe64d80feb065e36571f","observation_id":"10446fc9-916d-4597-8d73-a0e701678b05","resolution":{"observed_at":"2026-08-04T07:36:38.494274Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.01588","last_updated":"2024-04-15T13:56:24Z","snapshot_observed_at":"2026-07-06T16:42:28.104553Z","submitted_at":"2023-11-02T20:40:21Z","title":"Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.01588","snapshot_observed_at":"2026-08-04T07:36:38.496610Z","title":"( @eprint arXiv 2311.01588 )","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:38.496610Z"},"links":{"cited_paper":"/paper/2311.01588","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:c5ae022f3620a893ab4420bc493c490cacc74022ec10898e1d12f99612fae249","observation_id":"62477067-9f63-47c0-97ba-f3ae2fb9a636","resolution":{"observed_at":"2026-08-04T07:36:38.496610Z","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":"2026-08-04T07:36:38.498696Z","title":null,"venue":null,"work_id":null,"year":1956},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:38.498696Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:d78e40614d6bf9394fefd8c1e8bd7bef61ea49783feea5bdd9b2dea981460f3d","observation_id":"d8d06ae7-6a26-41dc-9b5f-f9ab159861d6","resolution":{"observed_at":"2026-08-04T07:36:38.498696Z","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":"10.1117/12.473932","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"R., 2003, in Erbacher R","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","work_id":"5b4f63df-949f-44cb-bd66-69629d8bf2f3","year":2003},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:38.511226Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:c79913e36525d5d7fcb9f47d5134666fcf6a03c8dc849945f886d2c5e52b0ed0","observation_id":"3ae803bd-ac19-46d3-95bd-43351977e7c4","resolution":{"observed_at":"2026-08-04T07:38:33.356715Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-04T07:36:38.553177Z","title":"V., eds, American Institute of Physics Conference Series Vol","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:38.553177Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:3dbec12e385851c663504d2b0dfa7d8854afc46cc9964d4833fbeff7169dddf9","observation_id":"e8550e88-2d96-4863-a40a-1b60cd4c6c67","resolution":{"observed_at":"2026-08-04T07:36:38.553177Z","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":"2026-08-04T07:36:38.633311Z","title":"D., 1996","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:38.633311Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:67bf027af5bb266b424dac64facb7bed979ecba190151027d8468fbc6dbbb6e6","observation_id":"155e7a25-5cd6-4e16-928b-85f703fbec0c","resolution":{"observed_at":"2026-08-04T07:36:38.633311Z","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":"2026-08-04T07:36:38.686052Z","title":"S., 2020, @doi [ ] 10.1093/mnras/staa278 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.493.3132S 493, 3132","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:38.686052Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:69eaec1cc479b5fd30b7f4baf931520c1332b1beda40494d8e75cccc68bb8784","observation_id":"51c8c702-7837-447e-95aa-a9db6d07b299","resolution":{"observed_at":"2026-08-04T07:36:38.686052Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.09697","last_updated":"2022-03-18T01:54:34Z","snapshot_observed_at":"2026-08-04T15:40:47.232520Z","submitted_at":"2022-03-18T01:54:34Z","title":"Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations","version":1},"cited_work":{"arxiv_id":"2203.09697","doi":"10.48550/arxiv.2203.09697","metadata_source":"pith","pith_arxiv_id":"2203.09697","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations","venue":"cs.LG","work_id":"e752e8ad-487c-4c62-9c47-ec69ad0a387d","year":2022},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:38.764664Z"},"links":{"cited_paper":"/paper/2203.09697","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:052278aa1434a3b19853790c33b8e1557aed64bc1f553e35c8fafb7e00c42c2c","observation_id":"2c8898ef-8e27-40cd-97a9-69e054c496fd","resolution":{"observed_at":"2026-08-04T07:38:33.010286Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-04T07:36:38.844035Z","title":"G., Vanden-Eijnden E., 2010, Communications in Mathematical Sciences, 8, 217","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:38.844035Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:9fd002b131510e68402c55d217d1e51623387aff61625310e1559cc82a6da02a","observation_id":"7fdbbb2f-d3ff-4518-8263-d11eaa4072a1","resolution":{"observed_at":"2026-08-04T07:36:38.844035Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.06921","last_updated":"2026-04-07T04:29:13Z","snapshot_observed_at":"2026-08-07T20:58:28.315035Z","submitted_at":"2025-06-07T21:00:01Z","title":"Teaching Astronomy with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.06921","snapshot_observed_at":"2026-08-04T07:36:38.924028Z","title":"arXiv:2506.06921","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:38.924028Z"},"links":{"cited_paper":"/paper/2506.06921","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:ee3748f7f413fde2c1c754222a058a75e835ff8bb7ac1a74406c98ff5548753a","observation_id":"b2119e87-4e59-4901-aeef-7f6e65d56743","resolution":{"observed_at":"2026-08-04T07:36:38.924028Z","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":"2026-08-04T07:36:39.002183Z","title":"K., 2001, Scandinavian Journal of Statistics, 28, 205","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:39.002183Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:07a43b6fdaa9c9d1fdd1695b1d9c10332351c1fabeebe65374d0f7284fa85bd6","observation_id":"78367120-7ae2-497f-b456-4049e3a64372","resolution":{"observed_at":"2026-08-04T07:36:39.002183Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.03762","last_updated":"2023-08-02T00:41:18Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-06-12T17:57:34Z","title":"Attention Is All You Need","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.03762","snapshot_observed_at":"2026-08-04T07:36:39.021971Z","title":"N., Kaiser L., Polosukhin I., 2017, @doi [arXiv e-prints] 10.48550/arXiv.1706.03762 , https://ui.adsabs.harvard.edu/abs/2017arXiv170603762V p","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:39.021971Z"},"links":{"cited_paper":"/paper/1706.03762","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:a782d43179151c3f1b5cd096e5a89910a2bea440740e33a1dd0cb5bbb7030fca","observation_id":"15eece01-3a37-4612-8b48-c7d795605a57","resolution":{"observed_at":"2026-08-04T07:36:39.021971Z","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":"2026-08-04T07:36:39.123617Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:39.123617Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:be425424d1650c8c4636f3c2439b8e7cf8577d95e072b0fa5d7527fcdf0fa75d","observation_id":"5819ccf5-21a7-4341-84c0-b468ba36ee20","resolution":{"observed_at":"2026-08-04T07:36:39.123617Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.02405","last_updated":"2020-07-02T22:18:12Z","snapshot_observed_at":"2026-07-06T08:55:23.638036Z","submitted_at":"2020-02-06T17:38:48Z","title":"How Good is the Bayes Posterior in Deep Neural Networks Really?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.02405","snapshot_observed_at":"2026-08-04T07:36:39.215715Z","title":"arXiv:2002.02405","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:39.215715Z"},"links":{"cited_paper":"/paper/2002.02405","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:a0c137169fd2458c65e2c772db67d1c7a853389c7adf21aefb8c8cb631786d5a","observation_id":"4cc2b3aa-d292-49ac-bffb-091d2e1f37cf","resolution":{"observed_at":"2026-08-04T07:36:39.215715Z","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":"2026-08-04T07:36:39.323919Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:39.323919Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:1e079bc85f9a151a8b2902c150523bf75b0a6097d1a89e88c783b2df20519c87","observation_id":"124106d8-1f3e-49e6-a50a-f7e521b85860","resolution":{"observed_at":"2026-08-04T07:36:39.323919Z","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":"10.1093/mnras/stz333","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"F., Boada S., 2019, @doi [ ] 10.1093/mnras/stz333 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.484.4683W 484, 4683","venue":"Monthly Notices of the Royal Astronomical Society","work_id":"bed3ac16-1d30-41b3-be27-3c83216214c1","year":2019},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:39.379834Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:584c96fd43befe0b4dc439b0e165505e1d83b1ea2bdde82392150f450574c768","observation_id":"ba195b92-2f13-4714-bfab-971263a3340d","resolution":{"observed_at":"2026-08-04T07:38:32.383767Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-04T07:36:39.400737Z","title":null,"venue":null,"work_id":null,"year":1990},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:39.400737Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:66e77efc7a2286f7f3f53314a50ca15cb982b49bcc328954880c3aef95093d2b","observation_id":"01f4bea0-d607-422a-b256-8d89916970c2","resolution":{"observed_at":"2026-08-04T07:36:39.400737Z","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":"2026-08-04T07:36:39.534832Z","title":"I., 2019, in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:39.534832Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:ac21041f739c020b45ca417e98f0220ec8c62936c183035da023222a04473a22","observation_id":"eaad0e4a-c3c1-4ef5-bef1-a0c9ac1ed51b","resolution":{"observed_at":"2026-08-04T07:36:39.534832Z","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":"2026-08-04T07:36:39.618059Z","title":"T., Wang W., Bai J., Wang Z., Song Y., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2505.13259 , https://ui.adsabs.harvard.edu/abs/2025arXiv250513259Z p","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:39.618059Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:8b435c5fba7480d1f7bf025cb336d96b76bebe9b801728d42e7450695c7eb4e6","observation_id":"098ce06e-6dda-474d-b7c6-9b0697f343b5","resolution":{"observed_at":"2026-08-04T07:36:39.618059Z","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":"2026-08-04T07:36:39.676462Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:39.676462Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:6683bcc79374d282c3e7575976693bd245c27008c5107d5d1e533cb828c46095","observation_id":"256e36f3-42a4-4cc6-b44c-b0ea6496baa0","resolution":{"observed_at":"2026-08-04T07:36:39.676462Z","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":"2026-08-04T07:36:39.751274Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:39.751274Z"},"links":{"citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:91212d3ec676c10f579eae9498ad4e8e7e6c1c5bca8792c644c08695b1191356","observation_id":"63bfb5a9-caa4-459e-bd8a-791427e81d2d","resolution":{"observed_at":"2026-08-04T07:36:39.751274Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","latest_version":2,"primary_category":"astro-ph.IM","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone"},"reference_resolution":{"displayed":78,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":66,"verified_exact":10,"verified_fuzzy":0},"total_outbound_references":78},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 78 of 78 outbound references and 0 inbound Pith citation observations for arXiv:2510.25824."}