{"as_of":"2026-08-08T18:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7599815f47a41ea0173587584ab4b9b40af077b1f0f11619f5ef0959e50966ba","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T20:38:32.067287Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-03T04:27:36.357585Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2310.07297","last_updated":"2024-03-15T03:42:03Z","snapshot_observed_at":"2026-07-06T16:31:03.559653Z","submitted_at":"2023-10-11T08:31:26Z","title":"Score Regularized Policy Optimization through Diffusion Behavior","version":3},"cited_work":{"arxiv_id":"2310.07297","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.07297","snapshot_observed_at":"2026-07-03T04:27:36.357585Z","title":"arXiv preprint arXiv:2310.07297 , year=","venue":null,"work_id":"cf9be0b3-342a-43d8-aa7f-aac19b0de249","year":2023},"citing_paper":{"arxiv_id":"2506.15799","last_updated":"2025-06-25T19:09:52Z","snapshot_observed_at":"2026-08-08T08:13:32.220639Z","submitted_at":"2025-06-18T18:35:57Z","title":"Steering Your Diffusion Policy with Latent Space Reinforcement Learning","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-05-17T21:55:46.183007Z"},"links":{"cited_paper":"/paper/2310.07297","citing_paper":"/paper/2506.15799"},"observation_digest":"sha256:f4a2b722dd110692ddefcb4bfea704189a6689c183beb9e1442c7952310f76a7","observation_id":"d21a81c5-2540-4161-b310-5a0870610646","resolution":{"observed_at":"2026-05-17T21:55:46.479870Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.07297","last_updated":"2024-03-15T03:42:03Z","snapshot_observed_at":"2026-07-06T16:31:03.559653Z","submitted_at":"2023-10-11T08:31:26Z","title":"Score Regularized Policy Optimization through Diffusion Behavior","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.07297","snapshot_observed_at":"2026-08-06T20:38:32.067287Z","title":"Score regularized policy optimization through diffusion behavior","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02356","last_updated":"2025-07-03T06:41:03Z","snapshot_observed_at":"2026-08-06T20:29:15.590198Z","submitted_at":"2025-07-03T06:41:03Z","title":"Offline Reinforcement Learning with Penalized Action Noise Injection","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-06T20:38:32.067287Z"},"links":{"cited_paper":"/paper/2310.07297","citing_paper":"/paper/2507.02356"},"observation_digest":"sha256:cb2f6a791985caef6ac2baf836bfb20ebb9e4922d3c48d948d43ac446588f70d","observation_id":"f7d77ab3-7008-4262-a7e8-eb0341d5621b","resolution":{"observed_at":"2026-08-06T20:38:32.067287Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.07297","last_updated":"2024-03-15T03:42:03Z","snapshot_observed_at":"2026-07-06T16:31:03.559653Z","submitted_at":"2023-10-11T08:31:26Z","title":"Score Regularized Policy Optimization through Diffusion Behavior","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.07297","snapshot_observed_at":"2026-07-14T23:46:32.301737Z","title":"Score reg- ularized policy optimization through diffusion behavior","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.10263","last_updated":"2026-07-01T06:52:08Z","snapshot_observed_at":"2026-08-07T08:16:48.418429Z","submitted_at":"2026-03-10T22:49:46Z","title":"From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-14T23:46:32.301737Z"},"links":{"cited_paper":"/paper/2310.07297","citing_paper":"/paper/2603.10263"},"observation_digest":"sha256:2769ebb2148cfcc9b156ccc8912acaa78df3e03ecb207882b27c7d9e6ad5bfff","observation_id":"d6fb52d4-c22f-446b-b8bb-f4bf0807ccaa","resolution":{"observed_at":"2026-07-14T23:46:32.301737Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.07297","last_updated":"2024-03-15T03:42:03Z","snapshot_observed_at":"2026-07-06T16:31:03.559653Z","submitted_at":"2023-10-11T08:31:26Z","title":"Score Regularized Policy Optimization through Diffusion Behavior","version":3},"cited_work":{"arxiv_id":"2310.07297","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.07297","snapshot_observed_at":"2026-07-03T04:27:36.357585Z","title":"arXiv preprint arXiv:2310.07297 , year=","venue":null,"work_id":"cf9be0b3-342a-43d8-aa7f-aac19b0de249","year":2023},"citing_paper":{"arxiv_id":"2604.17919","last_updated":"2026-05-05T15:00:45Z","snapshot_observed_at":"2026-07-06T23:04:55.190916Z","submitted_at":"2026-04-20T07:54:36Z","title":"Fisher Decorator: Refining Flow Policy via a Local Transport Map","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-10T05:28:12.298066Z"},"links":{"cited_paper":"/paper/2310.07297","citing_paper":"/paper/2604.17919"},"observation_digest":"sha256:ca61036199ced485f0c1e31a88f04882cc9bf1344af3f0190288cfacb29f7e35","observation_id":"1d192dac-cf14-4ec6-9a65-16360f99cb38","resolution":{"observed_at":"2026-05-10T06:51:46.558176Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.07297","last_updated":"2024-03-15T03:42:03Z","snapshot_observed_at":"2026-07-06T16:31:03.559653Z","submitted_at":"2023-10-11T08:31:26Z","title":"Score Regularized Policy Optimization through Diffusion Behavior","version":3},"cited_work":{"arxiv_id":"2310.07297","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.07297","snapshot_observed_at":"2026-07-03T04:27:36.357585Z","title":"arXiv preprint arXiv:2310.07297 , year=","venue":null,"work_id":"cf9be0b3-342a-43d8-aa7f-aac19b0de249","year":2023},"citing_paper":{"arxiv_id":"2605.01663","last_updated":"2026-05-28T02:21:27Z","snapshot_observed_at":"2026-07-06T23:14:52.417213Z","submitted_at":"2026-05-03T01:32:11Z","title":"Towards Efficient and Expressive Offline RL via Flow-Anchored Noise-conditioned Q-Learning","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-05-10T16:25:25.739019Z"},"links":{"cited_paper":"/paper/2310.07297","citing_paper":"/paper/2605.01663"},"observation_digest":"sha256:c7035c6b3bf7636f4a83872c46214e70484195de9795408f766d3dc79b1a708b","observation_id":"f5beaba2-02ee-4f9c-a0dd-0ae027d9e460","resolution":{"observed_at":"2026-05-11T08:56:00.773020Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.07297","last_updated":"2024-03-15T03:42:03Z","snapshot_observed_at":"2026-07-06T16:31:03.559653Z","submitted_at":"2023-10-11T08:31:26Z","title":"Score Regularized Policy Optimization through Diffusion Behavior","version":3},"cited_work":{"arxiv_id":"2310.07297","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.07297","snapshot_observed_at":"2026-07-03T04:27:36.357585Z","title":"arXiv preprint arXiv:2310.07297 , year=","venue":null,"work_id":"cf9be0b3-342a-43d8-aa7f-aac19b0de249","year":2023},"citing_paper":{"arxiv_id":"2605.08202","last_updated":"2026-05-06T01:21:53Z","snapshot_observed_at":"2026-07-06T23:20:28.875586Z","submitted_at":"2026-05-06T01:21:53Z","title":"Beyond Penalization: Diffusion-based Out-of-Distribution Detection and Selective Regularization in Offline Reinforcement Learning","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-05-12T02:17:25.783688Z"},"links":{"cited_paper":"/paper/2310.07297","citing_paper":"/paper/2605.08202"},"observation_digest":"sha256:e51f909edb18a6467293ef0b13ffd5b9e1b17a22f700f0ad20edb8fb9b2ced42","observation_id":"cb921262-cf60-4f3b-8af1-a231886ac316","resolution":{"observed_at":"2026-05-12T07:41:48.333368Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.07297","last_updated":"2024-03-15T03:42:03Z","snapshot_observed_at":"2026-07-06T16:31:03.559653Z","submitted_at":"2023-10-11T08:31:26Z","title":"Score Regularized Policy Optimization through Diffusion Behavior","version":3},"cited_work":{"arxiv_id":"2310.07297","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.07297","snapshot_observed_at":"2026-07-03T04:27:36.357585Z","title":"arXiv preprint arXiv:2310.07297 , year=","venue":null,"work_id":"cf9be0b3-342a-43d8-aa7f-aac19b0de249","year":2023},"citing_paper":{"arxiv_id":"2605.08253","last_updated":"2026-06-03T21:28:18Z","snapshot_observed_at":"2026-08-02T18:39:18.875350Z","submitted_at":"2026-05-07T19:05:01Z","title":"Path-Coupled Bellman Flows for Distributional Reinforcement Learning","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-05-12T02:12:58.528130Z"},"links":{"cited_paper":"/paper/2310.07297","citing_paper":"/paper/2605.08253"},"observation_digest":"sha256:bce656c6f61652e59131a64ecb2972ae6da76df2c0cc00b3ded523429f20ecfa","observation_id":"db9e2e38-2a12-4fde-a37c-0685532fee09","resolution":{"observed_at":"2026-05-12T02:16:16.247181Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.07297","last_updated":"2024-03-15T03:42:03Z","snapshot_observed_at":"2026-07-06T16:31:03.559653Z","submitted_at":"2023-10-11T08:31:26Z","title":"Score Regularized Policy Optimization through Diffusion Behavior","version":3},"cited_work":{"arxiv_id":"2310.07297","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.07297","snapshot_observed_at":"2026-07-03T04:27:36.357585Z","title":"arXiv preprint arXiv:2310.07297 , year=","venue":null,"work_id":"cf9be0b3-342a-43d8-aa7f-aac19b0de249","year":2023},"citing_paper":{"arxiv_id":"2605.27877","last_updated":"2026-05-27T02:53:41Z","snapshot_observed_at":"2026-07-06T23:37:31.844094Z","submitted_at":"2026-05-27T02:53:41Z","title":"SPAR: Support-Preserving Action Rectification","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-29T14:34:39.094753Z"},"links":{"cited_paper":"/paper/2310.07297","citing_paper":"/paper/2605.27877"},"observation_digest":"sha256:2e187f51c6c048234b9217f8ef0184ebd1f7669000ddf7e41d60a4c5a2be3b1b","observation_id":"3dfc10f2-7f7f-4631-bedb-5c612630c352","resolution":{"observed_at":"2026-06-29T14:43:30.999353Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.07297","last_updated":"2024-03-15T03:42:03Z","snapshot_observed_at":"2026-07-06T16:31:03.559653Z","submitted_at":"2023-10-11T08:31:26Z","title":"Score Regularized Policy Optimization through Diffusion Behavior","version":3},"cited_work":{"arxiv_id":"2310.07297","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.07297","snapshot_observed_at":"2026-07-03T04:27:36.357585Z","title":"arXiv preprint arXiv:2310.07297 , year=","venue":null,"work_id":"cf9be0b3-342a-43d8-aa7f-aac19b0de249","year":2023},"citing_paper":{"arxiv_id":"2605.29398","last_updated":"2026-05-28T05:47:40Z","snapshot_observed_at":"2026-07-06T23:38:51.349968Z","submitted_at":"2026-05-28T05:47:40Z","title":"GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-29T08:44:53.969301Z"},"links":{"cited_paper":"/paper/2310.07297","citing_paper":"/paper/2605.29398"},"observation_digest":"sha256:405cd02b8be1dbe19257f1e764ff614b88927c92ca1cd6b7e93a501bc99f02b9","observation_id":"f1b200e0-2084-477a-9a5a-43de682006b0","resolution":{"observed_at":"2026-06-29T08:53:16.397932Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.07297","last_updated":"2024-03-15T03:42:03Z","snapshot_observed_at":"2026-07-06T16:31:03.559653Z","submitted_at":"2023-10-11T08:31:26Z","title":"Score Regularized Policy Optimization through Diffusion Behavior","version":3},"cited_work":{"arxiv_id":"2310.07297","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.07297","snapshot_observed_at":"2026-07-03T04:27:36.357585Z","title":"arXiv preprint arXiv:2310.07297 , year=","venue":null,"work_id":"cf9be0b3-342a-43d8-aa7f-aac19b0de249","year":2023},"citing_paper":{"arxiv_id":"2606.10613","last_updated":"2026-06-09T09:12:12Z","snapshot_observed_at":"2026-08-04T14:36:44.997787Z","submitted_at":"2026-06-09T09:12:12Z","title":"Fast and Highly Expressive Policy Learning for Offline Reinforcement Learning via Bootstrapped Flow Q-Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-27T13:57:31.180928Z"},"links":{"cited_paper":"/paper/2310.07297","citing_paper":"/paper/2606.10613"},"observation_digest":"sha256:07eb72c35363bb134faaa547aa5db0b8a3a09a93f031efed804d9a57c9612183","observation_id":"dc086fae-516b-4571-ac7b-7ddde9870d3e","resolution":{"observed_at":"2026-07-03T04:27:36.359291Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2310.07297/citation-record","integrity":"/paper/2310.07297/integrity","json":"/paper/2310.07297/citation-record.json","paper":"/paper/2310.07297"},"outbound":[],"paper":{"arxiv_id":"2310.07297","last_updated":"2024-03-15T03:42:03Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T16:31:03.559653Z","submitted_at":"2023-10-11T08:31:26Z","title":"Score Regularized Policy Optimization through Diffusion Behavior"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2310.07297."}