{"as_of":"2026-08-08T09:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:02571926b4dbdb851c02579c50f0eee21d68bb985f4009f5e947bf76f63e1600","coverage":[{"denominator":21,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":21,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T22:30:08.787185Z","state":"measured"},{"denominator":21,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":21,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2506.21367/citation-record","integrity":"/paper/2506.21367/integrity","json":"/paper/2506.21367/citation-record.json","paper":"/paper/2506.21367"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1807.07049","last_updated":"2018-07-18T17:25:28Z","snapshot_observed_at":"2026-07-06T06:51:00.833741Z","submitted_at":"2018-07-18T17:25:28Z","title":"Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias","version":1},"cited_work":{"arxiv_id":"1807.07049","doi":null,"metadata_source":"pith","pith_arxiv_id":"1807.07049","snapshot_observed_at":"2026-08-06T22:30:08.957778Z","title":"Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias","venue":"cs.RO","work_id":"75c561b9-57a8-49f6-9012-612b4ae702e8","year":2018},"citing_paper":{"arxiv_id":"2506.21367","last_updated":"2025-06-26T15:16:35Z","snapshot_observed_at":"2026-08-06T22:23:44.435650Z","submitted_at":"2025-06-26T15:16:35Z","title":"rQdia: Regularizing Q-Value Distributions With Image Augmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T22:30:07.840957Z"},"links":{"cited_paper":"/paper/1807.07049","citing_paper":"/paper/2506.21367"},"observation_digest":"sha256:681edb4ce37665cc4bddd76090d25fe8d00268942ce9644a92d4e63c6bf6dc8a","observation_id":"d5c2ff23-1057-4d5e-b4be-02f746836453","resolution":{"observed_at":"2026-08-06T22:30:08.978756Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T22:30:07.881905Z","title":"Soft actor-critic algorithms and appli- cations","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.21367","last_updated":"2025-06-26T15:16:35Z","snapshot_observed_at":"2026-08-06T22:23:44.435650Z","submitted_at":"2025-06-26T15:16:35Z","title":"rQdia: Regularizing Q-Value Distributions With Image Augmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T22:30:07.881905Z"},"links":{"cited_paper":"/paper/1812.05905","citing_paper":"/paper/2506.21367"},"observation_digest":"sha256:247a92dfd860c822a8596ec1d4cc3557486cfc055c590b6c53867b224f00fc49","observation_id":"c8b0ee3c-445b-4b59-8e9a-615513f3f7b7","resolution":{"observed_at":"2026-08-06T22:30:07.881905Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.01603","last_updated":"2020-03-17T17:10:58Z","snapshot_observed_at":"2026-08-03T15:20:23.515607Z","submitted_at":"2019-12-03T18:57:16Z","title":"Dream to Control: Learning Behaviors by Latent Imagination","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.01603","snapshot_observed_at":"2026-08-06T22:30:07.924253Z","title":"Dream to control: Learning behaviors by latent imagination","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2506.21367","last_updated":"2025-06-26T15:16:35Z","snapshot_observed_at":"2026-08-06T22:23:44.435650Z","submitted_at":"2025-06-26T15:16:35Z","title":"rQdia: Regularizing Q-Value Distributions With Image Augmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T22:30:07.924253Z"},"links":{"cited_paper":"/paper/1912.01603","citing_paper":"/paper/2506.21367"},"observation_digest":"sha256:e71b15f7585ab560a3ac04eef5ff5429f9396ee361a7959a7b192a29714f3ce7","observation_id":"f6c4e426-7a41-4bb5-9224-a03417459b93","resolution":{"observed_at":"2026-08-06T22:30:07.924253Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.00953","last_updated":"2020-10-26T12:21:51Z","snapshot_observed_at":"2026-07-06T08:04:11.161145Z","submitted_at":"2019-07-01T17:45:09Z","title":"Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.00953","snapshot_observed_at":"2026-08-06T22:30:07.977037Z","title":"Imagenet classification with deep con- volutional neural networks","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2506.21367","last_updated":"2025-06-26T15:16:35Z","snapshot_observed_at":"2026-08-06T22:23:44.435650Z","submitted_at":"2025-06-26T15:16:35Z","title":"rQdia: Regularizing Q-Value Distributions With Image Augmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T22:30:07.977037Z"},"links":{"cited_paper":"/paper/1907.00953","citing_paper":"/paper/2506.21367"},"observation_digest":"sha256:aca2eda6fc1293672dcdfc36eee1d3be3f1545af2d9efc77e17f3ff6a95f2496","observation_id":"c13a3897-0a53-46f0-ac0b-9b058b732865","resolution":{"observed_at":"2026-08-06T22:30:07.977037Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.15332","last_updated":"2021-03-29T05:00:14Z","snapshot_observed_at":"2026-07-06T10:54:14.838348Z","submitted_at":"2021-03-29T05:00:14Z","title":"Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.15332","snapshot_observed_at":"2026-08-06T22:30:08.033451Z","title":"Mea- suring sample efficiency and generalization in reinforcement learning benchmarks: Neurips 2020 procgen benchmark","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.21367","last_updated":"2025-06-26T15:16:35Z","snapshot_observed_at":"2026-08-06T22:23:44.435650Z","submitted_at":"2025-06-26T15:16:35Z","title":"rQdia: Regularizing Q-Value Distributions With Image Augmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T22:30:08.033451Z"},"links":{"cited_paper":"/paper/2103.15332","citing_paper":"/paper/2506.21367"},"observation_digest":"sha256:fe431d4c080a79525c8efa70da7a68a02fd99ed7fdff77a25177b43564845633","observation_id":"17c36e3a-124a-48aa-b3fb-e7f9e207e1ae","resolution":{"observed_at":"2026-08-06T22:30:08.033451Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.12862","last_updated":"2021-02-20T12:32:59Z","snapshot_observed_at":"2026-07-06T09:31:50.012114Z","submitted_at":"2020-06-23T09:50:22Z","title":"Automatic Data Augmentation for Generalization in Deep Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.12862","snapshot_observed_at":"2026-08-06T22:30:08.286297Z","title":"Auto- matic data augmentation for generalization in deep reinforcement learning","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2506.21367","last_updated":"2025-06-26T15:16:35Z","snapshot_observed_at":"2026-08-06T22:23:44.435650Z","submitted_at":"2025-06-26T15:16:35Z","title":"rQdia: Regularizing Q-Value Distributions With Image Augmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T22:30:08.286297Z"},"links":{"cited_paper":"/paper/2006.12862","citing_paper":"/paper/2506.21367"},"observation_digest":"sha256:c711b82ec498ab87d55cd87edab9b178ffd3f9343193e1e60e9e00217fc7729e","observation_id":"4e94b1d1-de6c-483e-96ce-2734cdd769c1","resolution":{"observed_at":"2026-08-06T22:30:08.286297Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1511.05952","last_updated":"2016-02-25T17:55:31Z","snapshot_observed_at":"2026-07-06T04:37:00.543211Z","submitted_at":"2015-11-18T20:54:44Z","title":"Prioritized Experience Replay","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.05952","snapshot_observed_at":"2026-08-06T22:30:08.364969Z","title":"Prioritized experience replay.arXiv preprint arXiv:1511.05952,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.21367","last_updated":"2025-06-26T15:16:35Z","snapshot_observed_at":"2026-08-06T22:23:44.435650Z","submitted_at":"2025-06-26T15:16:35Z","title":"rQdia: Regularizing Q-Value Distributions With Image Augmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T22:30:08.364969Z"},"links":{"cited_paper":"/paper/1511.05952","citing_paper":"/paper/2506.21367"},"observation_digest":"sha256:8281ff1d15c6e9640e4fe78b323e4769b8bc86b98c556a629b88199c9e21ec34","observation_id":"b32ed7c1-0d06-49cd-96fe-493d6445fbc0","resolution":{"observed_at":"2026-08-06T22:30:08.364969Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.05929","last_updated":"2021-05-20T09:15:57Z","snapshot_observed_at":"2026-08-08T02:14:18.940330Z","submitted_at":"2020-07-12T07:38:15Z","title":"Data-Efficient Reinforcement Learning with Self-Predictive Representations","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.05929","snapshot_observed_at":"2026-08-06T22:30:08.476025Z","title":"Pierre Sermanet, Corey Lynch, Yevgen Chebotar, Jasmine Hsu, Eric Jang, Stefan Schaal, Sergey Levine, and Google Brain","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2506.21367","last_updated":"2025-06-26T15:16:35Z","snapshot_observed_at":"2026-08-06T22:23:44.435650Z","submitted_at":"2025-06-26T15:16:35Z","title":"rQdia: Regularizing Q-Value Distributions With Image Augmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T22:30:08.476025Z"},"links":{"cited_paper":"/paper/2007.05929","citing_paper":"/paper/2506.21367"},"observation_digest":"sha256:7ac1d839fafa378053798fefc2d7851933133285f040d8b39c51cdd8518f52e9","observation_id":"93c144cf-2dc6-48fa-8e0a-d91d7ec9ec76","resolution":{"observed_at":"2026-08-06T22:30:08.476025Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.04136","last_updated":"2020-09-21T15:34:30Z","snapshot_observed_at":"2026-08-08T04:10:15.931449Z","submitted_at":"2020-04-08T17:40:43Z","title":"CURL: Contrastive Unsupervised Representations for Reinforcement Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.04136","snapshot_observed_at":"2026-08-06T22:30:08.621610Z","title":"Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2506.21367","last_updated":"2025-06-26T15:16:35Z","snapshot_observed_at":"2026-08-06T22:23:44.435650Z","submitted_at":"2025-06-26T15:16:35Z","title":"rQdia: Regularizing Q-Value Distributions With Image Augmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T22:30:08.621610Z"},"links":{"cited_paper":"/paper/2004.04136","citing_paper":"/paper/2506.21367"},"observation_digest":"sha256:953e1072c752917d73da393ae19e41f4d6cda1da948cf317cc4f7aaf04a32599","observation_id":"8ac6adbc-64cf-4d0d-b7a9-208927350633","resolution":{"observed_at":"2026-08-06T22:30:08.621610Z","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":"2026-08-06T22:30:09.227413Z","title":"Do- main randomization for transferring deep neural networks from simulation to the real world","venue":null,"work_id":"8c734fc1-2f96-411a-a292-e72cd4fd0831","year":2017},"citing_paper":{"arxiv_id":"2506.21367","last_updated":"2025-06-26T15:16:35Z","snapshot_observed_at":"2026-08-06T22:23:44.435650Z","submitted_at":"2025-06-26T15:16:35Z","title":"rQdia: Regularizing Q-Value Distributions With Image Augmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T22:30:08.669931Z"},"links":{"citing_paper":"/paper/2506.21367"},"observation_digest":"sha256:fe48da1bbbc45b865ac5e6eea4a001a3c5c67d7aa29a9cfa91eed7e63848de4c","observation_id":"55df81b4-a162-4a8f-a32b-ab80bebeb547","resolution":{"observed_at":"2026-08-06T22:30:09.278565Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:30:09.133395Z","title":"Dueling network architectures for deep reinforcement learning","venue":null,"work_id":"f8357fff-bcd2-4458-9070-52e6b102f3ff","year":1995},"citing_paper":{"arxiv_id":"2506.21367","last_updated":"2025-06-26T15:16:35Z","snapshot_observed_at":"2026-08-06T22:23:44.435650Z","submitted_at":"2025-06-26T15:16:35Z","title":"rQdia: Regularizing Q-Value Distributions With Image Augmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T22:30:08.724246Z"},"links":{"citing_paper":"/paper/2506.21367"},"observation_digest":"sha256:47bc98593e7c1c07684608cc68166e52c32c6a5dde08e87d2967355f8e66dea8","observation_id":"ba7e379e-5e66-4f58-9cad-836655bccdf6","resolution":{"observed_at":"2026-08-06T22:30:09.179433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:30:09.042066Z","title":"Code for continuous control and discrete Atari will be released on GitHub upon notification of decision and is provided separately together with the supplementary material","venue":null,"work_id":"8a2815f6-3780-4f64-90a0-1c5728769d20","year":2000},"citing_paper":{"arxiv_id":"2506.21367","last_updated":"2025-06-26T15:16:35Z","snapshot_observed_at":"2026-08-06T22:23:44.435650Z","submitted_at":"2025-06-26T15:16:35Z","title":"rQdia: Regularizing Q-Value Distributions With Image Augmentation","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-06T22:30:08.787185Z"},"links":{"citing_paper":"/paper/2506.21367"},"observation_digest":"sha256:a831e66fc5c41772308140815b914e049fffa1fdc636c2521a195bc6502c93c1","observation_id":"40c11495-7a56-418d-aec1-75ba470bb48d","resolution":{"observed_at":"2026-08-06T22:30:09.073739Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.01741","last_updated":"2020-07-09T15:42:09Z","snapshot_observed_at":"2026-08-05T06:32:56.208161Z","submitted_at":"2019-10-02T15:50:03Z","title":"Improving Sample Efficiency in Model-Free Reinforcement Learning from Images","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.01741","snapshot_observed_at":"2026-08-06T22:30:08.754034Z","title":"Im- proving sample efficiency in model-free reinforcement learning from images","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2506.21367","last_updated":"2025-06-26T15:16:35Z","snapshot_observed_at":"2026-08-06T22:23:44.435650Z","submitted_at":"2025-06-26T15:16:35Z","title":"rQdia: Regularizing Q-Value Distributions With Image Augmentation","version":1},"reference_index":1991,"source":"pdf_text","source_observed_at":"2026-08-06T22:30:08.754034Z"},"links":{"cited_paper":"/paper/1910.01741","citing_paper":"/paper/2506.21367"},"observation_digest":"sha256:ba3456f2b67e809e312756f2245a3e9570bf46b15592b61ecea0de0af658dc2f","observation_id":"8f7dcaf2-9d3d-4753-b731-ab51291877fa","resolution":{"observed_at":"2026-08-06T22:30:08.754034Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.10537","last_updated":"2020-03-06T09:11:04Z","snapshot_observed_at":"2026-07-06T08:31:39.300566Z","submitted_at":"2019-10-23T12:58:08Z","title":"Robust Visual Domain Randomization for Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.10537","snapshot_observed_at":"2026-08-06T22:30:08.526952Z","title":"Robust visual domain randomization for reinforcement learning","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2506.21367","last_updated":"2025-06-26T15:16:35Z","snapshot_observed_at":"2026-08-06T22:23:44.435650Z","submitted_at":"2025-06-26T15:16:35Z","title":"rQdia: Regularizing Q-Value Distributions With Image Augmentation","version":1},"reference_index":2003,"source":"pdf_text","source_observed_at":"2026-08-06T22:30:08.526952Z"},"links":{"cited_paper":"/paper/1910.10537","citing_paper":"/paper/2506.21367"},"observation_digest":"sha256:4d573ae7d8655308d7bec11ad7cf58f0431653dc4f26e7851047f3314df60304","observation_id":"fe8444bd-b67f-4325-85f5-477cedc76b01","resolution":{"observed_at":"2026-08-06T22:30:08.526952Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1102.0183","last_updated":"2011-02-01T15:34:43Z","snapshot_observed_at":"2026-07-06T02:22:51.555249Z","submitted_at":"2011-02-01T15:34:43Z","title":"High-Performance Neural Networks for Visual Object Classification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1102.0183","snapshot_observed_at":"2026-08-06T22:30:07.777894Z","title":"High- performance neural networks for visual object classification","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.21367","last_updated":"2025-06-26T15:16:35Z","snapshot_observed_at":"2026-08-06T22:23:44.435650Z","submitted_at":"2025-06-26T15:16:35Z","title":"rQdia: Regularizing Q-Value Distributions With Image Augmentation","version":1},"reference_index":2012,"source":"pdf_text","source_observed_at":"2026-08-06T22:30:07.777894Z"},"links":{"cited_paper":"/paper/1102.0183","citing_paper":"/paper/2506.21367"},"observation_digest":"sha256:ee50699c37cfee661e238b53a25ef4f5ffbd87c20b046a6620ef7ade99d899f9","observation_id":"b934294f-33b4-4de7-928a-2ea0279e4e1c","resolution":{"observed_at":"2026-08-06T22:30:07.777894Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.00690","last_updated":"2018-01-02T15:48:14Z","snapshot_observed_at":"2026-08-01T20:24:08.300098Z","submitted_at":"2018-01-02T15:48:14Z","title":"DeepMind Control Suite","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.00690","snapshot_observed_at":"2026-08-06T22:30:08.646686Z","title":"Deepmind control suite","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.21367","last_updated":"2025-06-26T15:16:35Z","snapshot_observed_at":"2026-08-06T22:23:44.435650Z","submitted_at":"2025-06-26T15:16:35Z","title":"rQdia: Regularizing Q-Value Distributions With Image Augmentation","version":1},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-06T22:30:08.646686Z"},"links":{"cited_paper":"/paper/1801.00690","citing_paper":"/paper/2506.21367"},"observation_digest":"sha256:ee5b1161f8898be35412966273f072bd1450204bce78aacdda37edee27365ee8","observation_id":"69566d21-3b66-42e4-b757-8d852cfba287","resolution":{"observed_at":"2026-08-06T22:30:08.646686Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.05243","last_updated":"2019-06-12T16:57:00Z","snapshot_observed_at":"2026-08-06T12:11:44.996719Z","submitted_at":"2019-06-12T16:57:00Z","title":"When to use parametric models in reinforcement learning?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.05243","snapshot_observed_at":"2026-08-06T22:30:08.695739Z","title":"When to use parametric models in reinforce- ment learning? arXiv preprint arXiv:1906.05243,","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2506.21367","last_updated":"2025-06-26T15:16:35Z","snapshot_observed_at":"2026-08-06T22:23:44.435650Z","submitted_at":"2025-06-26T15:16:35Z","title":"rQdia: Regularizing Q-Value Distributions With Image Augmentation","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-06T22:30:08.695739Z"},"links":{"cited_paper":"/paper/1906.05243","citing_paper":"/paper/2506.21367"},"observation_digest":"sha256:30d5e0fdb19a8e0456c039dd9b6fe10d4373c61e01974b3df50e7a80dfa87ac1","observation_id":"d85823e7-6441-4987-97c7-f16b87857138","resolution":{"observed_at":"2026-08-06T22:30:08.695739Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.01905","last_updated":"2018-01-31T09:05:10Z","snapshot_observed_at":"2026-08-06T12:54:16.561808Z","submitted_at":"2017-06-06T18:09:29Z","title":"Parameter Space Noise for Exploration","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.01905","snapshot_observed_at":"2026-08-06T22:30:08.204326Z","title":"Parameter space noise for exploration","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.21367","last_updated":"2025-06-26T15:16:35Z","snapshot_observed_at":"2026-08-06T22:23:44.435650Z","submitted_at":"2025-06-26T15:16:35Z","title":"rQdia: Regularizing Q-Value Distributions With Image Augmentation","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-06T22:30:08.204326Z"},"links":{"cited_paper":"/paper/1706.01905","citing_paper":"/paper/2506.21367"},"observation_digest":"sha256:766baf8a627a8034b2defd17c38393d1d9a8f030a981c7f5ac5056463249cc3e","observation_id":"3fb7184f-5586-441a-95ad-94a36d5ceed8","resolution":{"observed_at":"2026-08-06T22:30:08.204326Z","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":"2026-08-06T22:30:09.345764Z","title":"Learning actionable rep- resentations from visual observations","venue":null,"work_id":"b449a72e-be14-4c2f-8ef6-0cc1bc441997","year":2018},"citing_paper":{"arxiv_id":"2506.21367","last_updated":"2025-06-26T15:16:35Z","snapshot_observed_at":"2026-08-06T22:23:44.435650Z","submitted_at":"2025-06-26T15:16:35Z","title":"rQdia: Regularizing Q-Value Distributions With Image Augmentation","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-06T22:30:07.807777Z"},"links":{"citing_paper":"/paper/2506.21367"},"observation_digest":"sha256:a80eabadd17f7b75310c950360a353d1640dd7bb532344494efed92f06fa2f22","observation_id":"245849ba-a992-4097-9002-1e9c7cda04d0","resolution":{"observed_at":"2026-08-06T22:30:09.414984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:30:09.525725Z","title":"Multi-column deep neural networks for image classification","venue":null,"work_id":"b7217d81-1719-4144-b3be-49393d012690","year":2012},"citing_paper":{"arxiv_id":"2506.21367","last_updated":"2025-06-26T15:16:35Z","snapshot_observed_at":"2026-08-06T22:23:44.435650Z","submitted_at":"2025-06-26T15:16:35Z","title":"rQdia: Regularizing Q-Value Distributions With Image Augmentation","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-06T22:30:07.735878Z"},"links":{"citing_paper":"/paper/2506.21367"},"observation_digest":"sha256:31ca88974682a9baeb44ca468e74249d834eaac9da02cd91f15d80f132907044","observation_id":"a6b1b7db-e1bc-4075-8ada-c3cc0fbda0de","resolution":{"observed_at":"2026-08-06T22:30:09.630604Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1702.08892","last_updated":"2017-11-22T23:11:20Z","snapshot_observed_at":"2026-07-06T05:31:46.512758Z","submitted_at":"2017-02-28T18:06:15Z","title":"Bridging the Gap Between Value and Policy Based Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1702.08892","snapshot_observed_at":"2026-08-06T22:30:08.141742Z","title":"Bridging the gap between value and policy based reinforcement learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.21367","last_updated":"2025-06-26T15:16:35Z","snapshot_observed_at":"2026-08-06T22:23:44.435650Z","submitted_at":"2025-06-26T15:16:35Z","title":"rQdia: Regularizing Q-Value Distributions With Image Augmentation","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-06T22:30:08.141742Z"},"links":{"cited_paper":"/paper/1702.08892","citing_paper":"/paper/2506.21367"},"observation_digest":"sha256:0b1a3d5c7638d5925a702237b26a86ffd5aef73527440daf6e9113d9e2de5868","observation_id":"b0f2bf65-fcd1-4d50-9aef-2d4f5f8905e9","resolution":{"observed_at":"2026-08-06T22:30:08.141742Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.21367","last_updated":"2025-06-26T15:16:35Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T22:23:44.435650Z","submitted_at":"2025-06-26T15:16:35Z","title":"rQdia: Regularizing Q-Value Distributions With Image Augmentation"},"reference_resolution":{"displayed":21,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":1,"verified_fuzzy":5},"total_outbound_references":21},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2506.21367."}