{"as_of":"2026-07-31T13:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0ee82868324314ba94541f30b9f740ce7f01900cffd95c50191cc4aad8a712a5","coverage":[{"denominator":67,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":67,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-28T22:06:53.667826Z","state":"measured"},{"denominator":67,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":67,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-07-31T06:34:12.847434+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/2606.00336/citation-record","integrity":"/paper/2606.00336/integrity","json":"/paper/2606.00336/citation-record.json","paper":"/paper/2606.00336"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:06:53.667826Z","title":"Training diffusion models with reinforcement learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:94ab6f99b107fdafe86a44beafc5c5930d2fb7d70914c414105f7a68dfe00ad1","observation_id":"ebfa7e27-6730-4141-888f-3dfbb0820abb","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"On learning, representing, and generalizing a task in a humanoid robot","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:c2500237773a55ae2fe70e01fe41eb9bb27b54924fa6a516538895f5d1c6eb24","observation_id":"acd3669b-2d31-4efd-81b0-f8c39ac3f291","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"Playfusion: Skill acquisition via diffusion from language-annotated play","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:349559a172430b56e4dea1b69207fdc348cb3292ee7a9162bd041443b1db348e","observation_id":"5523ba04-8e2e-4ae7-a513-9b0e0e17e6f6","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"Diffusion policy: Visuomotor policy learning via action diffusion","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:7783f7d2be487459766d27af70977037ac101aee5e9f28c70c3a48986683d46a","observation_id":"b800828e-b981-4da4-92e2-02eab391c712","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1206.6398","last_updated":"2012-09-03T16:05:45Z","snapshot_observed_at":"2026-07-06T02:50:47.096675Z","submitted_at":"2012-06-27T19:59:59Z","title":"Learning Parameterized Skills","version":2},"cited_work":{"arxiv_id":"1206.6398","doi":null,"metadata_source":"pith","pith_arxiv_id":"1206.6398","snapshot_observed_at":"2026-07-04T13:09:50.154846Z","title":"Learning Parameterized Skills","venue":"cs.LG","work_id":"d650b555-4508-45d6-98d6-5dc2de6e76c5","year":2012},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"cited_paper":"/paper/1206.6398","citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:05d14ea6ed08e7d9ccc70fc8bec2fc999dae0b6b44cf0f7eda1d26106d799d9a","observation_id":"fb219284-5be9-40e1-8dd6-01bda163024a","resolution":{"observed_at":"2026-07-01T19:46:10.573222Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:06:53.667826Z","title":"Accelerating robotic reinforcement learning via parameterized action primitives","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:56e3f180e556eb5c12e075e401e9593809b2529aaaa95b75c6d002289f6fc386","observation_id":"8a1e4a56-d158-4015-aa69-663a12e3d11c","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"and Nichol, A","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:bcef88ce2d7c6d9bbb9168100d971733c29c1162e31dfe0a469728cbd146514a","observation_id":"f0e9bcc6-b6c7-41e7-a4ea-dcd6b5dba9ce","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"Diffusion-based reinforcement learning via q-weighted variational policy optimization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:9d2d58b99a950251f489385c5edac6aa5a43a4cce9e98e5d1af00a5ce5908435","observation_id":"ae7aa8a9-f8f8-4890-a46c-cf52e3d199ce","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2505.18763","doi":"10.48550/arxiv.2505.18763","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Genpo: Generative diffusion models meet on-policy reinforcement learning.arXiv preprint arXiv:2505.18763","venue":null,"work_id":"65c22dd7-f8a4-4a7a-b033-67bda3afce6e","year":2025},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:e02caffe4bb319461535897811b2e2c77218c9d1ec619817f2bffec30a1f95b7","observation_id":"e7cd3f83-d169-4b8a-9064-96355615e012","resolution":{"observed_at":"2026-06-28T22:12:41.439804Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:06:53.667826Z","title":"and Mordatch, I","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:5e49dd46db067081471499de53d9551515968f3e04d14046bd0c190dbc1e9236","observation_id":"d7b62315-428d-4c57-a1b7-d2e7288f3664","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"B., Dieleman, S., Fergus, R., Sohl-Dickstein, J., Doucet, A., and Grathwohl, W","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:c3f22a9fa54ff02fdd09f04683bb92476644bb53d969994c5b87cdac961e4dd0","observation_id":"d9d80901-b7b1-4ca9-ad00-7d32c2384e0a","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"Learning universal policies via text-guided video generation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:0848b283d00a5689c1762ca48829753a36aed349646c63782ae8a69417a4ed2a","observation_id":"82f0f766-3913-4eb1-9414-9858678af472","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"A., Wahid, A., Downs, L., Adrianos, A., Hsu, C.-Y., and Chi, C","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:03500f4619a4244494e11dc50f08433f469183be5f78f507b2b266ca7a236ae9","observation_id":"67806d8a-68d1-492e-a71b-e4e9a0605d34","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"Meta learning shared hierarchies","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:79df96f9bc2424f4805e42580aaff755bad99c36602bc67f854e6e6589b075a0","observation_id":"9972518a-6607-4f84-b697-ebea4deddd69","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"Meta-learning parameterized skills","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:e1c9f4704757e95902cc8db78670d1c23d7873d3c8d9c3d3eec465be2b6dcc50","observation_id":"cbcac1b9-ab56-4b8b-92c6-3cb385f3f178","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:ebe08eeeb370d58985b95239bfb22fa6f3885d8f144a4500f9416eb7621f3927","observation_id":"ac966eea-419c-4835-8c78-099f5f734455","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"Learning parameterized skills from demonstrations","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:3eb48b9c65cc9dd584dc6e79b650f2ac74acfbeef5d38002aebd14d157917bea","observation_id":"54b191e6-7127-440b-bb7d-ec78eeb0c589","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"Isometric representation learning for disentangled latent space of diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:148842cd219402bafd2901e719706646f2f6d06626a87ef779e607084bf8c229","observation_id":"db59d93a-56d2-48c1-87b2-99c95586ecd7","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:8fa4b02bc20aaf548d68fff9bb2de22bbff66d7784b598ba692feccd11134b4d","observation_id":"5790cfb2-b009-4c0f-b99f-80289c861a49","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"Denoising diffusion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:52e142b4c9edfafd58e5733df7998c2bfd4f260d97ac3c8e4c8558e7f0a97354","observation_id":"be5101ad-c14a-45a8-a39f-e55cf07b6811","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"5743.2025","doi":"10.1109/icra55743.2025","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"In: 2025 IEEE International Conference on Robotics and Automation (ICRA), pp","venue":null,"work_id":"69abf874-f559-4fcb-a658-fd7f3a6a2304","year":2025},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:d38558589174afb18ee002510843ea4f98225b80969aef2c2a312a7623f3166b","observation_id":"da5b9116-6389-4de4-9863-a5e2fc254261","resolution":{"observed_at":"2026-06-28T22:12:41.436867Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-12T00:49:15.263628+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-12T00:49:15.263628+00:00","source":"openalex_status_cache"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:06:53.667826Z","title":"Multimodal deep generative models for trajectory prediction: A conditional variational autoencoder approach","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:f851a3739e5276d82514f09824d2497c39e0057ace557d1aa97998b4bcf7ec4b","observation_id":"1f6e0e95-ca81-4188-a278-d9ded12e17e2","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.06356","last_updated":"2024-04-09T14:46:48Z","snapshot_observed_at":"2026-07-06T17:57:48.153936Z","submitted_at":"2024-04-09T14:46:48Z","title":"Policy-Guided Diffusion","version":1},"cited_work":{"arxiv_id":"2404.06356","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.06356","snapshot_observed_at":"2026-07-02T11:46:56.124252Z","title":"Janner, M., Li, Q., and Levine, S","venue":null,"work_id":"f39e3076-ea89-4c9a-991b-6e4d83a0231e","year":2024},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"cited_paper":"/paper/2404.06356","citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:4cfd37cc936a0b2f55baedd737280009752319e3e03c8afc802e44a08c56d462","observation_id":"32e7c6df-9415-4118-b575-249051fd5ec4","resolution":{"observed_at":"2026-07-01T19:46:10.575819Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:06:53.667826Z","title":"B., and Levine, S","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:784a74946d54b95317b3ccca7f6e92220305150d907b356f58d89fecfb16cf22","observation_id":"31a72e15-676e-4dc1-9e45-649e3b29b1bc","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"Towards diverse behaviors: A benchmark for imitation learning with human demonstrations","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:b1bf1f8a96175456b91b94c0166c335c34763e5b029ff614b96532f5cd378680","observation_id":"37986a48-44e3-46be-8261-479d9fda3f42","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"Efficient diffusion policies for offline reinforcement learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:ad50adcf9182721b7df60029c8ad9cd7e411b1ddc00c7d6673ca59d2ef95a987","observation_id":"f214821f-013e-46d9-ba5d-3be4ccce2719","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"Elucidating the design space of diffusion-based generative models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:e41c475bce8d0c1c370e2cce9ffdd53950e888ad17a00b83edf6a41fd4e49e5b","observation_id":"95b0ba9b-d5cb-4835-97e7-da98d1601600","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:f9b430ab850def5b78d77dc8794beace8641749e5558d1169319945325984e10","observation_id":"d0ca7b53-ab91-4371-a2d0-0930c2acd825","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"J., Shafiullah, N","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:8b7ca4eb67daed271f3d7e34b500a432c61123e7e4df29e9355ca12f7bb6e002","observation_id":"6a1c969a-e4dc-4006-9e42-2c85b9946e6f","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2506.03067","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-01T19:46:10.588146Z","title":"arXiv preprint arXiv:2506.03067 (2025)","venue":null,"work_id":"a0a0679f-3e06-48a2-82af-7c004fef8792","year":2025},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:3253b6dccd0c715aa3111feb915e9fdf7bc10f335aea46ab9071a5e74d3e8ddb","observation_id":"134187a8-1271-467b-b77f-3f7fecc09879","resolution":{"observed_at":"2026-07-01T19:46:10.589506Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2511.22119","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-01T19:46:10.585570Z","title":"C., Zhai, J., and Ma, S","venue":null,"work_id":"89e52b85-846b-4544-9ae2-18600877128f","year":2025},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:63462297eec57b94a9ef0c1eada11ebc2a14df10166beb34637a4a9cc0dac2b4","observation_id":"e421e0c9-0527-47aa-acb0-6e0adc1030c5","resolution":{"observed_at":"2026-07-01T19:46:10.587084Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:06:53.667826Z","title":"Learning multimodal behaviors from scratch with diffusion policy gradient","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:5861ffa3167439c55ddf54f36d9d09b54a7adca96fdb8fa308cdc0bb2d1a875b","observation_id":"1237deaa-b744-452b-b8a3-468cd3567dd2","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"Learning multimodal behaviors from scratch with diffusion policy gradient","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:7c12c5481795692a924e661a9602999c55de7aefa0fccd113d9de726ec48383c","observation_id":"2b0b9a1d-a970-416c-964d-3ec082f89dfe","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2508.06266","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T20:46:33.616024Z","title":"Adpro: a test-time adaptive diffusion policy via manifold-constrained denoising and task-aware initialization for robotic manipulation","venue":null,"work_id":"fb9eac89-2868-41f4-a065-a668af27d943","year":2025},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:8069766858f156ac6efa49f206ac555115f94bf35faf78b84043dd81db050d69","observation_id":"40949db7-f35e-48f4-a70e-33fe1cda58fc","resolution":{"observed_at":"2026-07-01T19:46:10.584208Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:06:53.667826Z","title":"Learning latent plans from play","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:0163e35f88cf137ab8d1a0fd3ec5435de60d5e8459404af7293c1678ca9b0ff6","observation_id":"fc996a73-1205-4349-986f-62d8e3c8a333","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.22963","last_updated":"2026-05-19T22:38:20Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T21:53:36Z","title":"Reinforcement Learning with Discrete Diffusion Policies for Combinatorial Action Spaces","version":3},"cited_work":{"arxiv_id":"2509.22963","doi":null,"metadata_source":"pith","pith_arxiv_id":"2509.22963","snapshot_observed_at":"2026-07-04T08:59:43.288570Z","title":"Reinforcement learning with discrete diffusion policies for combinatorial action spaces","venue":"cs.LG","work_id":"9235b9bf-c840-46fe-bb2b-bf56eb8e1837","year":2025},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"cited_paper":"/paper/2509.22963","citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:e558b8d460839f7233b39241623f9dd67cf4d275ee49389a6687b07fa312f465","observation_id":"745cd51c-8c8a-499f-a74f-bb9e2119dbd4","resolution":{"observed_at":"2026-07-01T19:46:10.586635Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2505.18876","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-02T08:36:48.690590Z","title":"Diffusionrl: Efficient training of diffusion policies for robotic grasping using rl-adapted large- scale datasets","venue":null,"work_id":"4034c3a5-507e-428b-96ef-f441f9259be0","year":2026},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:c5b0034043458915003a951a5693ac27f4cdd4d37faee132d12546120f6201f4","observation_id":"5c929978-0fd0-4b78-b53a-3a882504d061","resolution":{"observed_at":"2026-07-01T19:46:10.584489Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:06:53.667826Z","title":"What matters in learning from offline demonstrations for robot manipulation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:3ea9eea616a2872d0bc0da67f1db5dc64bf75a418dd70f5015249740ae125b75","observation_id":"aa1d5580-9fc2-4aa5-bcf0-e1a2b7b01d6d","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1609/aaai.v30i1.10226","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:12:41.431531Z","title":null,"venue":null,"work_id":"440cd3bf-a5bd-424d-b26d-c184077fc179","year":2016},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:2b8a518be85b5eb9b245bc3a62198091730318dec89d4a0220519441f79cae5a","observation_id":"7c18e306-3c31-43b5-af9c-b601893bf378","resolution":{"observed_at":"2026-06-28T22:12:41.433252Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2733.2024","doi":"10.1109/cvpr52733.2024","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Emogen: Emotional image content generation with text-to-image diffusion models","venue":null,"work_id":"7efbc2dd-b0f2-4f71-bb1c-d2fcf110d805","year":2024},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:8f6a5b613de8fc2af9b8ac98dae581e81401001c40d34e46334b55fff7da4eb1","observation_id":"ec59aab9-0bce-4d22-a02e-c891b6ae2a19","resolution":{"observed_at":"2026-06-28T22:12:41.430475Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-12T03:19:26.559218+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-12T03:19:26.559218+00:00","source":"openalex_status_cache"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.347667","doi":"10.1109/tnnls.2024.3476671","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"B., Shanbhag, A","venue":null,"work_id":"4278b429-a8e5-41f6-ab79-25156914ce32","year":2025},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:bf42fda1ed2860793af867984b997e2f3284448887e60b3021f851d8817c5a0d","observation_id":"c5b49a7e-abc5-4ba1-af95-0f0437909b08","resolution":{"observed_at":"2026-06-28T22:12:41.442767Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:06:53.667826Z","title":"and Dhariwal, P","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:cbbeb00c0f6839fe161d4a89d1bce6cf48988bf8f683539bd94e58955d0f8017","observation_id":"d2bb50a6-10eb-465b-88ad-620c68ad4c5b","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2512.01809","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T09:19:43.461685Z","title":"Much ado about noising: Dispelling the myths of gener- ative robotic control","venue":null,"work_id":"cab94379-b09c-4084-8de9-d49329d27cd0","year":2025},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:b35bbb9c975495d867d78199d2043e8ad7343a9ff974db257c99e233ddd5b9fe","observation_id":"fea1803e-a66b-4bb4-9238-733a8628ac70","resolution":{"observed_at":"2026-07-01T19:46:10.570986Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2512.04926","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-02T02:36:27.541346Z","title":"Semantics lead the way: Harmonizing semantic and texture modeling with asynchronous latent diffusion","venue":null,"work_id":"ef210542-9023-4ce5-9f0c-092f35cd5ce5","year":2025},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:a8728e5b9e846ca025fd11f0e595234b0cde3ecaee88250498148e4d04c7b976","observation_id":"be054aa4-a912-4b96-8681-7a66714595e9","resolution":{"observed_at":"2026-07-01T19:46:10.562850Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.01245","last_updated":"2023-02-02T17:27:27Z","snapshot_observed_at":"2026-07-06T14:47:44.812529Z","submitted_at":"2023-02-02T17:27:27Z","title":"Whistler waves generated inside magnetic dips in the young solar wind: observations of the Search-Coil Magnetometer on board Parker Solar Probe","version":1},"cited_work":{"arxiv_id":"2302.01245","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.01245","snapshot_observed_at":"2026-07-01T19:46:10.587708Z","title":"Unsupervised discovery of semantic latent directions in diffusion models","venue":null,"work_id":"04968f39-77a6-464a-9b18-e5c98883ab29","year":2023},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"cited_paper":"/paper/2302.01245","citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:f47618195fd3ddae5abfca93a8b5f3178c7d17bf9978936ecad02b46620d7911","observation_id":"1ee8d552-5595-4489-86d2-a09103e3d33c","resolution":{"observed_at":"2026-07-01T19:46:10.589118Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:06:53.667826Z","title":"Film: Visual reasoning with a general conditioning layer","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:cefa81199633d65386ae667679f9b8baaec4cce9b45946eeb3f283860b7eb637","observation_id":"fabc09f7-8831-49ce-99c4-0247a2e366f6","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.20176","last_updated":"2025-03-26T03:04:42Z","snapshot_observed_at":"2026-07-06T20:58:46.814850Z","submitted_at":"2025-03-26T03:04:42Z","title":"Offline Reinforcement Learning with Discrete Diffusion Skills","version":1},"cited_work":{"arxiv_id":"2503.20176","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.20176","snapshot_observed_at":"2026-07-01T19:46:10.577388Z","title":"Offline reinforcement learning with discrete diffusion skills","venue":null,"work_id":"a42f08cd-a443-42d9-9782-efc4cd2b4e9d","year":2025},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"cited_paper":"/paper/2503.20176","citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:d019218e54ccfb81ae97be495a7b16b18381c11f86b7be06fddc7ed351ec4d63","observation_id":"2a6a8679-48d4-4054-be27-4893c952db1a","resolution":{"observed_at":"2026-07-01T19:46:10.578880Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2018.00049","doi":"10.1109/cluster.2018.00049","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"what it’s like","venue":null,"work_id":"8cb4e8a1-cf9f-49d6-a759-7a74df640bcb","year":2018},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:12c2e755d2ad51ead09be05ca60d9b60458a80ee7589c32d2c14a2b3bcd7a5dd","observation_id":"bd87b5e7-953c-44c2-85e8-c14c9b67db2b","resolution":{"observed_at":"2026-06-28T22:12:41.426622Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:06:53.667826Z","title":"Goal-conditioned imitation learning using score-based diffusion policies","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:8c0402c5f96ecac715bb0209c1d113cfb80831db3968f3dc8fa6c8473706071e","observation_id":"281be52d-4645-4233-babb-5fd530ba30df","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"Forward kl regularized preference optimization for aligning diffusion policies","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:73cb9b941ead9eec1087fe3b546567cdecdf9ab308f280274d8cd50da40290de","observation_id":"20ce23d7-6e6c-4dbf-b432-121a952ce75f","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"A., Maheswaranathan, N., and Ganguli, S","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:332207e4a39cd7407eade52f44cb70819f2b39b14c5a755f488314f33d076da0","observation_id":"d09f5707-dc87-47a0-9066-25d24e62ed5e","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"and Ermon, S","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:6f305bfb729248b9a94afcc4b09418cb8953c3ca5ffc84dc4ec14c1b5bdcb55c","observation_id":"d210b952-c234-4d1b-af5e-f5015a79965c","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"P., Kumar, A., Ermon, S., and Poole, B","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:1b1b791018143dd98f5b6ca6f8e8be90e1531b3201c54e7aeb0a739065d7282e","observation_id":"85fbd79b-0865-4f0a-aa9c-ad4df26c034e","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"S., Precup, D., and Singh, S","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:783f00aab02564291f769013d966110f42f1c7765610cbf7a3950090f542e845","observation_id":"aa01e397-793d-4495-af6a-9d950ded8cc7","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"S., Osindero, S., Schaul, T., Heess, N., Jaderberg, M., Silver, D., and Kavukcuoglu, K","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:e5362560dbbe125469eeeaa8665212bb4b34d9f33dcb04e2722ba2abe70ae89e","observation_id":"7820bf70-c0b5-4454-917f-011cf51a0457","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"Steering your diffusion policy with latent space reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:d348ccca6260bf7e188bbf7f77a59cd68bddf07216b9ad00b8237a8298891165","observation_id":"4b6d73a9-503d-4559-813a-f20faff96c78","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"J., and Zhou, M","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:fc29eec655dee98b595a5f340754bb02752d471b7603ee5a166a8d40ef3ab3c5","observation_id":"2151f40f-0a84-435e-a984-f34437b6aea0","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2511.01374","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-01T19:46:10.571843Z","title":"Learning intractable multimodal policies with reparameterization and diversity regularization","venue":null,"work_id":"bf926312-e2b8-469e-b01e-e8abef8272e8","year":2025},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:9d32c129043e872f3d04e9f76d6360077bd6e5114fade986aff766f0602aec65","observation_id":"ca44ac1f-b8f3-4aac-8476-f7a998b9d57f","resolution":{"observed_at":"2026-07-01T19:46:10.573267Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:06:53.667826Z","title":"Diffusion models for robotic manipulation: A survey","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:c6689a6e58531593ac302d2b1bd67c465210b5ea2fceab859b2eef382be131f7","observation_id":"6592bb4a-d460-46f8-9e00-bbed3a62f482","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"5743.2025","doi":"10.1109/icra55743.2025","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"In: 2025 IEEE International Conference on Robotics and Automation (ICRA), pp","venue":null,"work_id":"69abf874-f559-4fcb-a658-fd7f3a6a2304","year":2025},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:b3c31572ad857a24250586762e89fbef754a4a93fe2d90501573741cc26890c1","observation_id":"d2399360-1f24-4721-a22c-f62fb822861b","resolution":{"observed_at":"2026-06-28T22:12:41.446023Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-12T00:49:15.263628+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-12T00:49:15.263628+00:00","source":"openalex_status_cache"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2510.12253","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-01T19:46:10.563568Z","title":"Diffusion models for reinforcement learning: Foundations, taxonomy, and development","venue":null,"work_id":"1f9e4539-78a6-48af-a97a-dadd39d6c7e7","year":2025},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:a0cc689d4caa51cbbe6d0c43ec7ce23e51e0d826dfa1551e55288d8004614a43","observation_id":"dd38142f-f180-44df-9092-d0d10f72071d","resolution":{"observed_at":"2026-07-01T19:46:10.565037Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:06:53.667826Z","title":"Diffusion- ES : Gradient-free planning with diffusion for autonomous driving and zero-shot instruction following","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:89fa019289c39732864f55f6b3f04ecb4ab7604dd7816aef2dd8a988a8a257be","observation_id":"82af1bac-4229-48f9-8c2a-6325bc3d03fc","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"Efficient task-specific conditional diffusion policies: Shortcut model acceleration and so (3) optimization","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:b1082439e432c3858c92a5a289d9cd08309c0d2fe21fd5e8c9ca18b66dfab26c","observation_id":"50d123b8-00ae-4711-875e-bc8c60158445","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"Model-based reinforcement learning for parameterized action spaces","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:13aa5564ee5f2d246d6b09a8a405be3ceec1bedceda449249fa6d5c62a1d11ee","observation_id":"946fe76e-a1da-44b9-ba5a-31cf365f8927","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"D., Huang, F., and Kolobov, A","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:51421184754addfe1b4cdbc12d28f4a158309afc52029d6d4526f6dd1825ba02","observation_id":"7978ef9f-0840-4746-9d55-876dc366515e","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","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":"2026-06-28T22:06:53.667826Z","title":"N., and Gao, R","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:254692ab2efc3d10c2de3eaf53f46b911ecdb7ce52a3834c8bf2d895431eb1c5","observation_id":"abd60a2b-3ffb-495c-9573-6798dc212d05","resolution":{"observed_at":"2026-06-28T22:06:53.667826Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.01223","last_updated":"2024-02-23T14:42:57Z","snapshot_observed_at":"2026-07-06T16:42:12.176043Z","submitted_at":"2023-11-02T13:23:39Z","title":"Diffusion Models for Reinforcement Learning: A Survey","version":4},"cited_work":{"arxiv_id":"2311.01223","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.01223","snapshot_observed_at":"2026-07-03T04:27:36.344632Z","title":"arXiv preprint arXiv:2311.01223 , year=","venue":null,"work_id":"c5ea28eb-7029-4113-8e6c-fa31145e9892","year":2023},"citing_paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-06-28T22:06:53.667826Z"},"links":{"cited_paper":"/paper/2311.01223","citing_paper":"/paper/2606.00336"},"observation_digest":"sha256:c70e38cc6f14375e93cc590e5642193c6a166581c0980d475cfae89550733226","observation_id":"f084e249-8118-4c72-9fb9-e11ca282f95a","resolution":{"observed_at":"2026-07-01T19:46:10.592151Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2606.00336","last_updated":"2026-05-29T20:21:50Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-07-06T23:41:01.066075Z","submitted_at":"2026-05-29T20:21:50Z","title":"From Noise to Control: Parameterized Diffusion Policies"},"reference_resolution":{"displayed":67,"state_counts":{"malformed_identifier":0,"metadata_mismatch":7,"parse_uncertain":0,"unresolved":46,"verified_exact":14,"verified_fuzzy":0},"total_outbound_references":67},"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":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"thesis":"As of 31 July 2026, this Paper Citation Record lists 67 of 67 outbound references and 0 inbound Pith citation observations for arXiv:2606.00336."}