{"as_of":"2026-08-18T09:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3363ae83ad4b0514662a1f0ca2fef00de888546ed7eed8ee49b5b14116120494","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T21:28:15.475264Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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/2607.16090/citation-record","integrity":"/paper/2607.16090/integrity","json":"/paper/2607.16090/citation-record.json","paper":"/paper/2607.16090"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T21:28:11.644672Z","title":"Sim-to-real transfer in deep reinforcement learning for robotics: a survey,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:11.644672Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:3e08dc4427bff83dcb79af8a9441af73e8541d5dccb2cca01f98203396541142","observation_id":"13e9cff9-cc29-4f1d-93ed-ecb8239ceaf9","resolution":{"observed_at":"2026-08-01T21:28:11.644672Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.13187","last_updated":"2025-03-08T06:36:16Z","snapshot_observed_at":"2026-08-17T04:40:29.817573Z","submitted_at":"2025-02-18T12:57:29Z","title":"A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.13187","snapshot_observed_at":"2026-08-01T21:28:11.692533Z","title":"A survey of sim-to-real methods in rl: Progress, prospects and challenges with foundation models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:11.692533Z"},"links":{"cited_paper":"/paper/2502.13187","citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:dae35482bd37406c0f88b8278d9eb523f7c7b600e259fd868e428bbbff863e96","observation_id":"fab31d8e-7175-45bd-af90-2706d3f1784d","resolution":{"observed_at":"2026-08-01T21:28:11.692533Z","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":"2026-08-01T21:28:11.760684Z","title":"Cross-domain policy adaptation via value-guided data filtering,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:11.760684Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:707286a5c022c8ac79c1d828e7e7f44e096038d94b7114c358ec13350edf6861","observation_id":"7e7f6796-6be9-46c2-8694-d8d3763fe008","resolution":{"observed_at":"2026-08-01T21:28:11.760684Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.15369","last_updated":"2024-05-24T09:06:12Z","snapshot_observed_at":"2026-08-18T09:09:34.133221Z","submitted_at":"2024-05-24T09:06:12Z","title":"Cross-Domain Policy Adaptation by Capturing Representation Mismatch","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.15369","snapshot_observed_at":"2026-08-01T21:28:11.858021Z","title":"Cross-domain policy adaptation by capturing representation mismatch,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:11.858021Z"},"links":{"cited_paper":"/paper/2405.15369","citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:e6a90933e898964befafc47e8073cc6da71ebfdc7952947294d5ae260ebe5a83","observation_id":"2cfc9c8d-43dc-45d3-831b-729f463753eb","resolution":{"observed_at":"2026-08-01T21:28:11.858021Z","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":"2026-08-01T21:28:11.970399Z","title":"Sim-to- real transfer of robotic control with dynamics randomization,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:11.970399Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:72d3cfabfd03a44dae036177fccc1a24c507ece1f82360ba10e4caf1ca8146d3","observation_id":"50ff98de-e947-4a68-a42d-94ae7e3e4413","resolution":{"observed_at":"2026-08-01T21:28:11.970399Z","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":"2026-08-01T21:28:12.083042Z","title":"Active domain randomization,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:12.083042Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:ccd11a8ea62125633f2570d258d4b6e9ec182438432172f40199af450620879d","observation_id":"42bd4a18-288b-403c-bc35-f0cc644cebc3","resolution":{"observed_at":"2026-08-01T21:28:12.083042Z","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":"2026-08-01T21:28:12.198886Z","title":"Flow-based domain randomization for learning and sequencing robotic skills,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:12.198886Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:ef6317676754d6aaeb283395804edcbceded6b766ff0e618a9985ffe28074deb","observation_id":"86ae8323-bfa1-4ee1-aeb1-4126fb4aec47","resolution":{"observed_at":"2026-08-01T21:28:12.198886Z","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":"2026-08-01T21:28:12.309371Z","title":"Closing the sim-to-real loop: Adapting simula- tion randomization with real world experience,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:12.309371Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:0f8019280866b106c031bef763707d6063eea5291f19bd8bbf7c6261cb349407","observation_id":"05179d9e-a730-49fd-95cb-ca610be47ad7","resolution":{"observed_at":"2026-08-01T21:28:12.309371Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.13916","last_updated":"2021-04-14T23:38:31Z","snapshot_observed_at":"2026-08-15T05:32:12.454205Z","submitted_at":"2020-06-24T17:47:37Z","title":"Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.13916","snapshot_observed_at":"2026-08-01T21:28:12.466094Z","title":"Off-dynamics reinforcement learning: Training for transfer with domain classifiers,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:12.466094Z"},"links":{"cited_paper":"/paper/2006.13916","citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:36929ce43f3d48fd79d6fd33cabd84207795af47fa4b05789464bb6fecdc275c","observation_id":"81679cff-648a-4f15-821a-0d5323e044cf","resolution":{"observed_at":"2026-08-01T21:28:12.466094Z","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":"2026-08-01T21:28:12.578776Z","title":"Cross-domain policy adaptation by capturing representation mismatch,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:12.578776Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:7819592dfe38799eb27a0ee2ae0a8dc72fc8f495980d30ee35552f83b54859b1","observation_id":"c5dffb96-94ba-4fec-9642-e4cea986d45a","resolution":{"observed_at":"2026-08-01T21:28:12.578776Z","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":"2026-08-01T21:28:12.728671Z","title":"Deep unsupervised learning using nonequilibrium thermodynamics,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:12.728671Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:92ea24e2f4c492837ef6140079ddc75ab57ea8cfcf694f92ba44ddcb9c52f313","observation_id":"70a1383a-6958-4460-acda-42b8062bde88","resolution":{"observed_at":"2026-08-01T21:28:12.728671Z","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":"2026-08-01T21:28:12.881843Z","title":"Denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:12.881843Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:c3cea4eec551965fdd553931918ccd905a3e8c8fdc36637deb785421bc91f63d","observation_id":"6ae36b03-ae7d-4ca2-9f8e-184bb1d745c4","resolution":{"observed_at":"2026-08-01T21:28:12.881843Z","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":"2026-08-01T21:28:12.949797Z","title":"State regularized policy optimization on data with dynamics shift,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:12.949797Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:5f50142e6fd2785d7b4edd9ba36bf8b1e29837a32d3f2f6aa2ef8ef7e7ae13a6","observation_id":"d96018b7-c9d8-4db1-a0b9-c485c7fd7749","resolution":{"observed_at":"2026-08-01T21:28:12.949797Z","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":"2026-08-01T21:28:12.987669Z","title":"Policy adaptation from foundation model feedback,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:12.987669Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:02e3d4daceb619dc18ff806652d1a0f0731e7c47ae64ac2560788e030ce8b192","observation_id":"b210eddf-bae2-4a70-85f0-1bf579aa0b5b","resolution":{"observed_at":"2026-08-01T21:28:12.987669Z","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":"2026-08-01T21:28:13.051080Z","title":"Cross- domain reinforcement learning under distinct state-action spaces via hybrid q functions","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:13.051080Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:a83aa7db7db7904475f74c6f7bd0e91f2f38a2c0952720a5b1a3e93231bc55f4","observation_id":"46aba1f9-7ad6-4d8d-a0b3-2dffc0f49207","resolution":{"observed_at":"2026-08-01T21:28:13.051080Z","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":"2026-08-01T21:28:13.118239Z","title":"Robust vi- sual domain randomization for reinforcement learning,","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:13.118239Z"},"links":{"cited_paper":"/paper/1910.10537","citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:cbbb42ea09cb59d81e1ab3e88ebccf7b1fce72a7f328944ad4075a1ca92306a7","observation_id":"2b5cd315-ee7d-4e0f-a8fc-3a4cc7c89521","resolution":{"observed_at":"2026-08-01T21:28:13.118239Z","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":"2026-08-01T21:28:13.173722Z","title":"Variance reduced domain randomization for reinforcement learning with policy gradi- ent,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:13.173722Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:fe30c04b2cdc361121551afb0f5af30c6ed384561069e1571430f9bae7e0b219","observation_id":"29540d56-3d6e-4e2b-814d-73c850e838bf","resolution":{"observed_at":"2026-08-01T21:28:13.173722Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.11347","last_updated":"2019-02-27T19:23:41Z","snapshot_observed_at":"2026-08-14T19:30:50.935609Z","submitted_at":"2018-03-30T05:47:11Z","title":"Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.11347","snapshot_observed_at":"2026-08-01T21:28:13.233561Z","title":"Learning to adapt in dynamic, real-world environments through meta-reinforcement learning,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:13.233561Z"},"links":{"cited_paper":"/paper/1803.11347","citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:73bc148bb0341fdbe70d91fc66a2cb7d9b6c93d00ae177d0d33a8c43b38fb9c8","observation_id":"7439fc17-c5ab-4736-86cd-bca1dd95c8b0","resolution":{"observed_at":"2026-08-01T21:28:13.233561Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.00350","last_updated":"2022-10-01T19:31:46Z","snapshot_observed_at":"2026-08-16T16:27:35.883363Z","submitted_at":"2022-10-01T19:31:46Z","title":"Zero-Shot Policy Transfer with Disentangled Task Representation of Meta-Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.00350","snapshot_observed_at":"2026-08-01T21:28:13.303161Z","title":"Zero-shot policy transfer with disentangled task representation of meta-reinforcement learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:13.303161Z"},"links":{"cited_paper":"/paper/2210.00350","citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:3389721a96dd09bd5d076028a6abf737aab195b5d801930c6495261bf46dbbd3","observation_id":"0380270d-a53e-4c38-a3f4-9e8e33d0166a","resolution":{"observed_at":"2026-08-01T21:28:13.303161Z","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":"2026-08-01T21:28:13.394295Z","title":"Cross-domain imitation from observations,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:13.394295Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:e3beb5414a580a75c834258a31ada193297e9df45df01a8ba82bd576f4eb9a0d","observation_id":"d53f31fb-cf00-4511-a0dd-5f2313d10432","resolution":{"observed_at":"2026-08-01T21:28:13.394295Z","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":"2026-08-01T21:28:13.502547Z","title":"Cross- domain imitation learning via optimal transport,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:13.502547Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:2b11b9e3332cd980f4925e694983348bcc685145dd1ae34e49a1d39659c35d25","observation_id":"6b362640-a724-42a3-a407-843f3a6d3053","resolution":{"observed_at":"2026-08-01T21:28:13.502547Z","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":"2026-08-01T21:28:13.646786Z","title":"Off-dynamics reinforce- ment learning via domain adaptation and reward augmented imitation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:13.646786Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:7b4c38272dfdbf71ef329eba88fe68b589d9336238b52f15378560bfe904fde6","observation_id":"67647228-1e1d-4f09-8a92-ebc16229d47d","resolution":{"observed_at":"2026-08-01T21:28:13.646786Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10765","last_updated":"2024-02-16T15:39:51Z","snapshot_observed_at":"2026-08-16T14:17:56.772071Z","submitted_at":"2024-02-16T15:39:51Z","title":"Policy Learning for Off-Dynamics RL with Deficient Support","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10765","snapshot_observed_at":"2026-08-01T21:28:13.768720Z","title":"Policy learning for off- dynamics rl with deficient support,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:13.768720Z"},"links":{"cited_paper":"/paper/2402.10765","citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:df05ad13258a3506fd7db3c72d2fc1e75c777d37e77ee98ded92dfd1ac132544","observation_id":"8019d70a-5651-43c9-ad82-224dee2c0b2e","resolution":{"observed_at":"2026-08-01T21:28:13.768720Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.06192","last_updated":"2024-05-10T02:21:42Z","snapshot_observed_at":"2026-08-16T13:53:52.156341Z","submitted_at":"2024-05-10T02:21:42Z","title":"Contrastive Representation for Data Filtering in Cross-Domain Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.06192","snapshot_observed_at":"2026-08-01T21:28:13.889037Z","title":"Contrastive representation for data filtering in cross-domain offline reinforcement learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:13.889037Z"},"links":{"cited_paper":"/paper/2405.06192","citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:78854784fc26d53cfda20f6481fff5a906169061ab3c83cc6ed68f2dbf053e95","observation_id":"a4535ce1-c27e-494c-bd81-208c3e88a284","resolution":{"observed_at":"2026-08-01T21:28:13.889037Z","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":"2026-08-01T21:28:14.047408Z","title":"Efficient diffusion policies for offline reinforcement learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:14.047408Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:09c47a942ac8d96d6aff82b77d48ae1f1c46c70bd0ad36a86ba52734bd64b11c","observation_id":"dbf63394-979c-4e62-8a27-1e15494b9d66","resolution":{"observed_at":"2026-08-01T21:28:14.047408Z","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":"2026-08-01T21:28:14.174342Z","title":"Diffusion policy: Visuomotor policy learning via action diffusion,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:14.174342Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:95944ab55a98832f57023351bc52bf7d0d8d496f01dc96283eaaccac4cd34196","observation_id":"a68fc35b-7b83-46c8-936a-6d6124469278","resolution":{"observed_at":"2026-08-01T21:28:14.174342Z","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":"2026-08-01T21:28:14.314378Z","title":"Synthetic experience replay,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:14.314378Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:39b2a4a18d4460717eaf07a47787f009c9f1fbd33976abb2ccac37e78b13d3c8","observation_id":"43f281fb-eda3-4d4e-9cb6-9aa444d12aea","resolution":{"observed_at":"2026-08-01T21:28:14.314378Z","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":"2026-08-01T21:28:14.467066Z","title":"Diffusion model is an effective planner and data synthesizer for multi-task reinforcement learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:14.467066Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:a616c12e30779dac0ea77370b36e67558d3a178ef87933be124731c4b31aeae7","observation_id":"27007d8b-332b-490c-9204-4ec514cc0fbd","resolution":{"observed_at":"2026-08-01T21:28:14.467066Z","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":"2026-08-01T21:28:14.587035Z","title":"Diffusion actor-critic with entropy reg- ulator,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:14.587035Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:99e7a370ebe6379246239391e295cbe9b9f19fe081915d1c0c63a7b12428b669","observation_id":"356b4e43-e4d4-4c26-acc5-4208647eba43","resolution":{"observed_at":"2026-08-01T21:28:14.587035Z","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":"2026-08-01T21:28:14.747184Z","title":"Madiff: Offline multi-agent learning with diffusion models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:14.747184Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:ccf1821f515dc445fd1a5aabc6063bc39e090a884723ff5ce3da203e7464179b","observation_id":"5c31cf64-3ff8-4e26-a5ba-f4149b4bc0d3","resolution":{"observed_at":"2026-08-01T21:28:14.747184Z","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":"2026-08-01T21:28:14.907734Z","title":"Dmc: Nearest neighbor guidance diffusion model for offline cross- domain reinforcement learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:14.907734Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:43b605a636a0d6b0e8ae4e44b513b4563be89659a99b489ffed2584252d2e215","observation_id":"2757a530-bcbb-4643-8c50-4128015baaaa","resolution":{"observed_at":"2026-08-01T21:28:14.907734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.05905","last_updated":"2019-01-29T12:10:47Z","snapshot_observed_at":"2026-08-17T07:51:41.384508Z","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":"2026-08-01T21:28:15.011402Z","title":"Soft actor-critic algorithms and applications,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:15.011402Z"},"links":{"cited_paper":"/paper/1812.05905","citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:7409d5286d3f7753f8f60bb3f0f6cfcf33e7ee81422ac590945071781d64fa3d","observation_id":"0b793111-8f69-4b03-9f23-ccb2032c7b35","resolution":{"observed_at":"2026-08-01T21:28:15.011402Z","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":"2026-08-01T21:28:15.116585Z","title":"Mujoco: A physics engine for model-based control,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:15.116585Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:dd1bdfb216b8340f7c9e361139f035e84afc5fec9248be2a67dcfa205a121ac4","observation_id":"90530fc7-9249-4445-954b-2708076c0b9e","resolution":{"observed_at":"2026-08-01T21:28:15.116585Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.01540","last_updated":"2016-06-05T17:54:48Z","snapshot_observed_at":"2026-08-13T12:26:05.192883Z","submitted_at":"2016-06-05T17:54:48Z","title":"OpenAI Gym","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.01540","snapshot_observed_at":"2026-08-01T21:28:15.252598Z","title":"Openai gym,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:15.252598Z"},"links":{"cited_paper":"/paper/1606.01540","citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:6868893ae2e6409769f18676f96caa6d13fcf40c68ef8c0dca5202255f8d3e77","observation_id":"83d20bc9-9bfb-4856-82e0-87f2d275face","resolution":{"observed_at":"2026-08-01T21:28:15.252598Z","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":"2026-08-01T21:28:15.367574Z","title":"Odrl: A benchmark for off-dynamics reinforcement learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:15.367574Z"},"links":{"citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:78be49f37065cc51ddf61f061aecad6a348ed48c5deca2464750806bc75c7602","observation_id":"694a93c6-e7c4-4117-8ae2-8028d5a3a2cb","resolution":{"observed_at":"2026-08-01T21:28:15.367574Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03858","last_updated":"2021-02-15T17:29:47Z","snapshot_observed_at":"2026-08-14T18:53:32.384767Z","submitted_at":"2018-07-10T20:53:04Z","title":"Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03858","snapshot_observed_at":"2026-08-01T21:28:15.475264Z","title":"Algorithmic framework for model-based deep reinforcement learning with theoret- ical guarantees,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-01T21:28:15.475264Z"},"links":{"cited_paper":"/paper/1807.03858","citing_paper":"/paper/2607.16090"},"observation_digest":"sha256:11817da02550064ba7b121c4d1d166db00eb7d4cafd99ebeab37e5c7e767ed63","observation_id":"52c40649-af1c-4ab9-9e9f-74619d1c96a5","resolution":{"observed_at":"2026-08-01T21:28:15.475264Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.16090","last_updated":"2026-07-17T16:20:08Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T04:32:47.420500Z","submitted_at":"2026-07-17T16:20:08Z","title":"DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":36,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":36},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2607.16090."}