{"as_of":"2026-08-17T04:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b49d8225c2d5951168166f541dd501c48e98a9da627245abd00776c8cf14b9ff","coverage":[{"denominator":16,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":16,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T20:51:10.107804Z","state":"measured"},{"denominator":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T20:18:57.677914Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":4,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.05244","last_updated":"2025-02-07T14:29:07Z","snapshot_observed_at":"2026-08-09T14:58:49.748389Z","submitted_at":"2025-02-07T14:29:07Z","title":"Probabilistic Artificial Intelligence","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.05244","snapshot_observed_at":"2026-08-15T20:18:57.677914Z","title":"Andreas Krause and Jonas Hübotter","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.13585","last_updated":"2025-08-21T09:11:57Z","snapshot_observed_at":"2026-08-16T14:35:54.834575Z","submitted_at":"2025-05-19T17:55:32Z","title":"Scalable Bayesian Monte Carlo: fast uncertainty estimation beyond deep ensembles","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-15T20:18:57.677914Z"},"links":{"cited_paper":"/paper/2502.05244","citing_paper":"/paper/2505.13585"},"observation_digest":"sha256:535466f28586e632a12379f39a6fb0c76a1d5ed5a1d81a0efcd2dd9202e75e47","observation_id":"4fb7dec9-0397-4fe8-a38a-64739827875a","resolution":{"observed_at":"2026-08-15T20:18:57.677914Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.05244","last_updated":"2025-02-07T14:29:07Z","snapshot_observed_at":"2026-08-09T14:58:49.748389Z","submitted_at":"2025-02-07T14:29:07Z","title":"Probabilistic Artificial Intelligence","version":1},"cited_work":{"arxiv_id":"2502.05244","doi":"10.48550/arxiv.2502.05244","metadata_source":"arxiv_reference","pith_arxiv_id":"2502.05244","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Probabilistic","venue":"ArXiv.org","work_id":"07838fb6-e9a3-4ec4-9c30-fe530d2eb8d7","year":2025},"citing_paper":{"arxiv_id":"2605.16684","last_updated":"2026-05-15T22:43:11Z","snapshot_observed_at":"2026-07-06T23:27:48.303599Z","submitted_at":"2026-05-15T22:43:11Z","title":"GPU Performance of an Entropy-Stable Discontinuous Galerkin Euler Solver with Non-Conservative Terms","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-05-19T20:37:52.250369Z"},"links":{"cited_paper":"/paper/2502.05244","citing_paper":"/paper/2605.16684"},"observation_digest":"sha256:5eaa2c56f13124b22d12d85ac5a95b0e37330cbc73ca58990679fcade8ee3858","observation_id":"dc784a62-3329-40ac-83c7-7c6a4c39d286","resolution":{"observed_at":"2026-05-19T20:42:45.795408Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-05-23T22:23:35.431003+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T22:23:35.431003+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.05244","last_updated":"2025-02-07T14:29:07Z","snapshot_observed_at":"2026-08-09T14:58:49.748389Z","submitted_at":"2025-02-07T14:29:07Z","title":"Probabilistic Artificial Intelligence","version":1},"cited_work":{"arxiv_id":"2502.05244","doi":"10.48550/arxiv.2502.05244","metadata_source":"arxiv_reference","pith_arxiv_id":"2502.05244","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Probabilistic","venue":"ArXiv.org","work_id":"07838fb6-e9a3-4ec4-9c30-fe530d2eb8d7","year":2025},"citing_paper":{"arxiv_id":"2607.00284","last_updated":"2026-07-01T00:17:40Z","snapshot_observed_at":"2026-08-12T18:24:46.791717Z","submitted_at":"2026-07-01T00:17:40Z","title":"Active Learning for Calibrating Entangling Gates via Surrogate-Based Optimization","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-02T12:58:27.343501Z"},"links":{"cited_paper":"/paper/2502.05244","citing_paper":"/paper/2607.00284"},"observation_digest":"sha256:80a49d8ead6820ccc0e50bee22a53a9ab73ab0e10c86b8aab35a7650010b0923","observation_id":"531c95fc-ff85-4a56-83d5-365affd244b3","resolution":{"observed_at":"2026-07-02T13:06:58.781304Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-05-23T22:23:35.431003+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T22:23:35.431003+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2502.05244/citation-record","integrity":"/paper/2502.05244/integrity","json":"/paper/2502.05244/citation-record.json","paper":"/paper/2502.05244"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1805.00909","last_updated":"2018-05-20T20:03:59Z","snapshot_observed_at":"2026-08-08T20:31:18.897748Z","submitted_at":"2018-05-02T17:11:20Z","title":"Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.00909","snapshot_observed_at":"2026-08-08T20:51:10.076990Z","title":"Reinforcement learning and control as probabilistic inference: Tutorial and review","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.05244","last_updated":"2025-02-07T14:29:07Z","snapshot_observed_at":"2026-08-09T14:58:49.748389Z","submitted_at":"2025-02-07T14:29:07Z","title":"Probabilistic Artificial Intelligence","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T20:51:10.076990Z"},"links":{"cited_paper":"/paper/1805.00909","citing_paper":"/paper/2502.05244"},"observation_digest":"sha256:8b83a1f31b2c40f7a9207a29debd5fff531cd9604108b072c609dc3a783605b4","observation_id":"88ea348b-39a9-4d1f-970b-acb033221157","resolution":{"observed_at":"2026-08-08T20:51:10.076990Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.03122","last_updated":"2018-01-09T19:58:41Z","snapshot_observed_at":"2026-08-14T19:56:33.489486Z","submitted_at":"2018-01-09T19:58:41Z","title":"A detailed treatment of Doob's theorem","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.03122","snapshot_observed_at":"2026-08-08T20:51:10.084752Z","title":"A detailed treatment of doob’s theorem","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.05244","last_updated":"2025-02-07T14:29:07Z","snapshot_observed_at":"2026-08-09T14:58:49.748389Z","submitted_at":"2025-02-07T14:29:07Z","title":"Probabilistic Artificial Intelligence","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T20:51:10.084752Z"},"links":{"cited_paper":"/paper/1801.03122","citing_paper":"/paper/2502.05244"},"observation_digest":"sha256:eb01cf471102cf9179bb72d61e2fa939880aa0e4d9cedf2d4bdddd8d049ee701","observation_id":"ffca75f1-664b-4557-80c5-38bb985f4dd3","resolution":{"observed_at":"2026-08-08T20:51:10.084752Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1609.04747","last_updated":"2017-06-15T13:21:04Z","snapshot_observed_at":"2026-08-15T03:49:17.013617Z","submitted_at":"2016-09-15T17:32:34Z","title":"An overview of gradient descent optimization algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.04747","snapshot_observed_at":"2026-08-08T20:51:10.096594Z","title":"An overview of gradient descent optimization algorithms","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.05244","last_updated":"2025-02-07T14:29:07Z","snapshot_observed_at":"2026-08-09T14:58:49.748389Z","submitted_at":"2025-02-07T14:29:07Z","title":"Probabilistic Artificial Intelligence","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T20:51:10.096594Z"},"links":{"cited_paper":"/paper/1609.04747","citing_paper":"/paper/2502.05244"},"observation_digest":"sha256:9957f2d0e546c58acc3f8d55cea28f6bac37481f61fe6802eade4e2d6e292028","observation_id":"d73a48d9-9e5a-49bb-b27c-57f90561eb63","resolution":{"observed_at":"2026-08-08T20:51:10.096594Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-08-15T20:26:32.102285Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-08T20:51:10.100253Z","title":"Proximal policy optimization algo- rithms","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.05244","last_updated":"2025-02-07T14:29:07Z","snapshot_observed_at":"2026-08-09T14:58:49.748389Z","submitted_at":"2025-02-07T14:29:07Z","title":"Probabilistic Artificial Intelligence","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T20:51:10.100253Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2502.05244"},"observation_digest":"sha256:64cbb8213f9ff407e834147aefb2cc76a888931e21575236841c163f38029a84","observation_id":"b50abdd6-bbfb-49c3-8aca-811a9445444a","resolution":{"observed_at":"2026-08-08T20:51:10.100253Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03300","last_updated":"2024-04-27T15:25:53Z","snapshot_observed_at":"2026-08-06T14:58:42.911363Z","submitted_at":"2024-02-05T18:55:32Z","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03300","snapshot_observed_at":"2026-08-08T20:51:10.104062Z","title":"Deepseekmath: Pushing the limits of mathematical reasoning in open language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.05244","last_updated":"2025-02-07T14:29:07Z","snapshot_observed_at":"2026-08-09T14:58:49.748389Z","submitted_at":"2025-02-07T14:29:07Z","title":"Probabilistic Artificial Intelligence","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T20:51:10.104062Z"},"links":{"cited_paper":"/paper/2402.03300","citing_paper":"/paper/2502.05244"},"observation_digest":"sha256:9ce1fc4bbc5dba2d077c419c820e6f11576e7d13bc83a594f4a451bf1f7a3507","observation_id":"1c11e398-21fb-4bf5-96b5-15080ba93b8c","resolution":{"observed_at":"2026-08-08T20:51:10.104062Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.01815","last_updated":"2017-12-05T18:45:38Z","snapshot_observed_at":"2026-08-14T20:06:37.819179Z","submitted_at":"2017-12-05T18:45:38Z","title":"Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.01815","snapshot_observed_at":"2026-08-08T20:51:10.107804Z","title":"Mastering chess and shogi by self-play with a general reinforce- ment learning algorithm","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.05244","last_updated":"2025-02-07T14:29:07Z","snapshot_observed_at":"2026-08-09T14:58:49.748389Z","submitted_at":"2025-02-07T14:29:07Z","title":"Probabilistic Artificial Intelligence","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-08T20:51:10.107804Z"},"links":{"cited_paper":"/paper/1712.01815","citing_paper":"/paper/2502.05244"},"observation_digest":"sha256:1d2afb1112aaa9bf27b8ca239a25376d846bb85c58aa69a2531593299a4b4d2e","observation_id":"a36ca27a-c381-46e4-a85b-a0960fc6b2fa","resolution":{"observed_at":"2026-08-08T20:51:10.107804Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.09464","last_updated":"2018-03-10T18:11:25Z","snapshot_observed_at":"2026-08-14T19:42:01.790871Z","submitted_at":"2018-02-26T17:20:14Z","title":"Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.09464","snapshot_observed_at":"2026-08-08T20:51:10.092584Z","title":"Multi-goal reinforcement learning: Challenging robotics environments and request for research","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.05244","last_updated":"2025-02-07T14:29:07Z","snapshot_observed_at":"2026-08-09T14:58:49.748389Z","submitted_at":"2025-02-07T14:29:07Z","title":"Probabilistic Artificial Intelligence","version":1},"reference_index":2008,"source":"pdf_text","source_observed_at":"2026-08-08T20:51:10.092584Z"},"links":{"cited_paper":"/paper/1802.09464","citing_paper":"/paper/2502.05244"},"observation_digest":"sha256:564da8a3f95243da99c8aef5966a5cc30cc61566ccc4e8a85a6e2e1d39805e26","observation_id":"fccb7db7-c78b-47d5-8a1a-f7d8ff09cc75","resolution":{"observed_at":"2026-08-08T20:51:10.092584Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.20092","last_updated":"2025-02-13T18:38:13Z","snapshot_observed_at":"2026-08-16T13:05:48.031658Z","submitted_at":"2024-10-26T06:06:08Z","title":"OGBench: Benchmarking Offline Goal-Conditioned RL","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.20092","snapshot_observed_at":"2026-08-08T20:51:10.088509Z","title":"Ogbench: Benchmarking offline goal-conditioned rl","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.05244","last_updated":"2025-02-07T14:29:07Z","snapshot_observed_at":"2026-08-09T14:58:49.748389Z","submitted_at":"2025-02-07T14:29:07Z","title":"Probabilistic Artificial Intelligence","version":1},"reference_index":2009,"source":"pdf_text","source_observed_at":"2026-08-08T20:51:10.088509Z"},"links":{"cited_paper":"/paper/2410.20092","citing_paper":"/paper/2502.05244"},"observation_digest":"sha256:2a5a3684a98584f64d386e95208d4f43a849fdad7509551cbca591d095059dc8","observation_id":"80094c8b-5b20-472e-8415-45ff012c3829","resolution":{"observed_at":"2026-08-08T20:51:10.088509Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.15217","last_updated":"2023-09-11T17:25:24Z","snapshot_observed_at":"2026-08-03T19:11:09.671782Z","submitted_at":"2023-07-27T22:29:25Z","title":"Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.15217","snapshot_observed_at":"2026-08-08T20:51:10.056960Z","title":"Open problems and fundamental limitations of reinforcement learning from human feedback","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.05244","last_updated":"2025-02-07T14:29:07Z","snapshot_observed_at":"2026-08-09T14:58:49.748389Z","submitted_at":"2025-02-07T14:29:07Z","title":"Probabilistic Artificial Intelligence","version":1},"reference_index":2013,"source":"pdf_text","source_observed_at":"2026-08-08T20:51:10.056960Z"},"links":{"cited_paper":"/paper/2307.15217","citing_paper":"/paper/2502.05244"},"observation_digest":"sha256:06fb83d21fcab09fe0200711365d4f61d565bbfd9b33ee7fecf947105aefdb4c","observation_id":"ceb3ae33-a214-48ea-a9b7-9747da5c0bc8","resolution":{"observed_at":"2026-08-08T20:51:10.056960Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1207.0580","last_updated":"2012-07-03T06:35:15Z","snapshot_observed_at":"2026-08-15T00:54:35.320150Z","submitted_at":"2012-07-03T06:35:15Z","title":"Improving neural networks by preventing co-adaptation of feature detectors","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1207.0580","snapshot_observed_at":"2026-08-08T20:51:10.073063Z","title":"Improving neural networks by preventing co-adaptation of feature detectors","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.05244","last_updated":"2025-02-07T14:29:07Z","snapshot_observed_at":"2026-08-09T14:58:49.748389Z","submitted_at":"2025-02-07T14:29:07Z","title":"Probabilistic Artificial Intelligence","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-08T20:51:10.073063Z"},"links":{"cited_paper":"/paper/1207.0580","citing_paper":"/paper/2502.05244"},"observation_digest":"sha256:dcebab7de9932ec57d0e6ca025b7bc41258ea7180e3870d2b0489a6b3cf55c7b","observation_id":"a330d4fb-6c83-4110-968d-1628a122c00f","resolution":{"observed_at":"2026-08-08T20:51:10.073063Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.13316","last_updated":"2022-06-27T13:58:51Z","snapshot_observed_at":"2026-08-16T16:50:07.375168Z","submitted_at":"2022-06-27T13:58:51Z","title":"Humans are not Boltzmann Distributions: Challenges and Opportunities for Modelling Human Feedback and Interaction in Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2206.13316","doi":null,"metadata_source":"pith","pith_arxiv_id":"2206.13316","snapshot_observed_at":"2026-08-08T20:51:10.210606Z","title":"Humans are not Boltzmann Distributions: Challenges and Opportunities for Modelling Human Feedback and Interaction in Reinforcement Learning","venue":"cs.LG","work_id":"07bcb78d-9ac4-414f-8920-0f3ea705771b","year":2022},"citing_paper":{"arxiv_id":"2502.05244","last_updated":"2025-02-07T14:29:07Z","snapshot_observed_at":"2026-08-09T14:58:49.748389Z","submitted_at":"2025-02-07T14:29:07Z","title":"Probabilistic Artificial Intelligence","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-08T20:51:10.081159Z"},"links":{"cited_paper":"/paper/2206.13316","citing_paper":"/paper/2502.05244"},"observation_digest":"sha256:8d4b9b3f33d5674afe37865ee748ba50ebb9942a3317f9954c6329ba79871a2e","observation_id":"106bcbf8-c45c-4402-97f3-b9088c4607a6","resolution":{"observed_at":"2026-08-08T20:51:10.216610Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-08-15T12:33:55.451951Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-08T20:51:10.065071Z","title":"Deepseek-r 1: Incentivizing reasoning capability in llms via reinforcement learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.05244","last_updated":"2025-02-07T14:29:07Z","snapshot_observed_at":"2026-08-09T14:58:49.748389Z","submitted_at":"2025-02-07T14:29:07Z","title":"Probabilistic Artificial Intelligence","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-08T20:51:10.065071Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2502.05244"},"observation_digest":"sha256:3a3096e9ad75d2ebd9cfec5543685de3803e437763817958058bac1de805e4d0","observation_id":"7e55e571-0262-4fea-a79b-8fd53951eaf5","resolution":{"observed_at":"2026-08-08T20:51:10.065071Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T20:51:10.290863Z","title":"Application of the theory of martingales","venue":null,"work_id":"44c1e982-6a77-4d02-ac87-a63997c2c9a9","year":1948},"citing_paper":{"arxiv_id":"2502.05244","last_updated":"2025-02-07T14:29:07Z","snapshot_observed_at":"2026-08-09T14:58:49.748389Z","submitted_at":"2025-02-07T14:29:07Z","title":"Probabilistic Artificial Intelligence","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-08T20:51:10.061145Z"},"links":{"citing_paper":"/paper/2502.05244"},"observation_digest":"sha256:934661e77c5adceb08920eb98122e689d9d53704363ffd311962a429530f7e57","observation_id":"420bb6f7-8e4c-499f-86de-f9b618cfa9dc","resolution":{"observed_at":"2026-08-08T20:51:10.294731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.05026","last_updated":"2025-06-22T16:19:25Z","snapshot_observed_at":"2026-08-16T13:11:55.450904Z","submitted_at":"2024-10-07T13:26:36Z","title":"Active Fine-Tuning of Multi-Task Policies","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.05026","snapshot_observed_at":"2026-08-08T20:51:10.048325Z","title":"Active fine-tuning of generalist policies","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.05244","last_updated":"2025-02-07T14:29:07Z","snapshot_observed_at":"2026-08-09T14:58:49.748389Z","submitted_at":"2025-02-07T14:29:07Z","title":"Probabilistic Artificial Intelligence","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-08T20:51:10.048325Z"},"links":{"cited_paper":"/paper/2410.05026","citing_paper":"/paper/2502.05244"},"observation_digest":"sha256:b21d61f338a2fdbe1a442859386e923d5733b1cd61a6eadf72c6d3a2d8d540b3","observation_id":"ff63a438-8d3d-4cc2-9371-7cbba5275e38","resolution":{"observed_at":"2026-08-08T20:51:10.048325Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T20:51:10.301725Z","title":"Diffusions hypercontractives","venue":null,"work_id":"371d6591-2698-4eed-80f9-beec42793fed","year":1983},"citing_paper":{"arxiv_id":"2502.05244","last_updated":"2025-02-07T14:29:07Z","snapshot_observed_at":"2026-08-09T14:58:49.748389Z","submitted_at":"2025-02-07T14:29:07Z","title":"Probabilistic Artificial Intelligence","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-08T20:51:10.053045Z"},"links":{"citing_paper":"/paper/2502.05244"},"observation_digest":"sha256:8d512d7114be4b7e56bdc899027efe45b29513e1d6e84d5927e2d4f11cbb80dc","observation_id":"07804eea-4b68-4e2f-b02a-ef821aa02a34","resolution":{"observed_at":"2026-08-08T20:51:10.305037Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.05905","last_updated":"2019-01-29T12:10:47Z","snapshot_observed_at":"2026-08-01T15:24:35.515954Z","submitted_at":"2018-12-13T04:44:29Z","title":"Soft Actor-Critic Algorithms and Applications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.05905","snapshot_observed_at":"2026-08-08T20:51:10.069138Z","title":"Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.05244","last_updated":"2025-02-07T14:29:07Z","snapshot_observed_at":"2026-08-09T14:58:49.748389Z","submitted_at":"2025-02-07T14:29:07Z","title":"Probabilistic Artificial Intelligence","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-08T20:51:10.069138Z"},"links":{"cited_paper":"/paper/1812.05905","citing_paper":"/paper/2502.05244"},"observation_digest":"sha256:f1821869104eeda903deab94890f6c9ee5feb9baa0e5bc14e4e81b990f54a020","observation_id":"b1079f1f-26dd-44e4-8b5d-cc184806ec9e","resolution":{"observed_at":"2026-08-08T20:51:10.069138Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.05244","last_updated":"2025-02-07T14:29:07Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-09T14:58:49.748389Z","submitted_at":"2025-02-07T14:29:07Z","title":"Probabilistic Artificial Intelligence"},"reference_resolution":{"displayed":16,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":1,"verified_fuzzy":2},"total_outbound_references":16},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 3 inbound Pith citation observations for arXiv:2502.05244."}