{"as_of":"2026-08-08T17:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f7dd2dc66c8e22dacba52ba950904eda6a5cd2f1636ad7bcad67f3a4ae5fedc0","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":18,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":18,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":18,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:20:41.812673Z","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":"2026-07-03T04:17:36.914370Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2503.04975","last_updated":"2025-03-06T21:10:12Z","snapshot_observed_at":"2026-08-07T17:23:30.914733Z","submitted_at":"2025-03-06T21:10:12Z","title":"Energy-Weighted Flow Matching for Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.04975","snapshot_observed_at":"2026-08-07T14:13:13.112158Z","title":"Energy-weighted flow matching for offline reinforcement learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.19717","last_updated":"2025-08-20T08:55:32Z","snapshot_observed_at":"2026-08-08T04:30:02.798412Z","submitted_at":"2025-05-26T09:06:34Z","title":"Extremum Flow Matching for Offline Goal Conditioned Reinforcement Learning","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T14:13:13.112158Z"},"links":{"cited_paper":"/paper/2503.04975","citing_paper":"/paper/2505.19717"},"observation_digest":"sha256:3ff6644b0a2e6ed3eef438fa701d1700960a10dcc30509b807120acd3b29cb85","observation_id":"8205e69e-8374-4dde-aea1-9753110244a2","resolution":{"observed_at":"2026-08-07T14:13:13.112158Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.04975","last_updated":"2025-03-06T21:10:12Z","snapshot_observed_at":"2026-08-07T17:23:30.914733Z","submitted_at":"2025-03-06T21:10:12Z","title":"Energy-Weighted Flow Matching for Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.04975","snapshot_observed_at":"2026-08-07T14:20:41.812673Z","title":"Energy-weighted flow matching for offline reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.20350","last_updated":"2025-05-26T03:42:20Z","snapshot_observed_at":"2026-08-08T01:01:01.114971Z","submitted_at":"2025-05-26T03:42:20Z","title":"Decision Flow Policy Optimization","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-07T14:20:41.812673Z"},"links":{"cited_paper":"/paper/2503.04975","citing_paper":"/paper/2505.20350"},"observation_digest":"sha256:2c5e30fda9f2bb2060db3a0d33695ac518b729d16808352bde766149c5bf018e","observation_id":"863b8cb8-2f9d-4b82-9b87-409e89188567","resolution":{"observed_at":"2026-08-07T14:20:41.812673Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.04975","last_updated":"2025-03-06T21:10:12Z","snapshot_observed_at":"2026-08-07T17:23:30.914733Z","submitted_at":"2025-03-06T21:10:12Z","title":"Energy-Weighted Flow Matching for Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2503.04975","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.04975","snapshot_observed_at":"2026-07-03T04:17:36.914370Z","title":"Energy-weighted flow matching for offline reinforcement learning","venue":null,"work_id":"a3aa6122-6b44-4df6-9041-e6de954d3a70","year":2025},"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":44,"source":"pdf_text","source_observed_at":"2026-05-17T21:55:46.183007Z"},"links":{"cited_paper":"/paper/2503.04975","citing_paper":"/paper/2506.15799"},"observation_digest":"sha256:4ca5ba79fdb624158c16c371b8d12ea242889ff569b3efd0209a8ae15bffe497","observation_id":"6070c92e-c9cb-4651-9f72-f8d6858d621e","resolution":{"observed_at":"2026-05-17T21:55:46.427116Z","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":"2503.04975","last_updated":"2025-03-06T21:10:12Z","snapshot_observed_at":"2026-08-07T17:23:30.914733Z","submitted_at":"2025-03-06T21:10:12Z","title":"Energy-Weighted Flow Matching for Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2503.04975","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.04975","snapshot_observed_at":"2026-07-03T04:17:36.914370Z","title":"Energy-weighted flow matching for offline reinforcement learning","venue":null,"work_id":"a3aa6122-6b44-4df6-9041-e6de954d3a70","year":2025},"citing_paper":{"arxiv_id":"2509.03726","last_updated":"2026-04-21T00:07:07Z","snapshot_observed_at":"2026-07-06T22:23:10.158908Z","submitted_at":"2025-09-03T21:16:03Z","title":"Energy-Weighted Flow Matching: Unlocking Continuous Normalizing Flows for Efficient and Scalable Boltzmann Sampling","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-18T18:43:13.941495Z"},"links":{"cited_paper":"/paper/2503.04975","citing_paper":"/paper/2509.03726"},"observation_digest":"sha256:e35c4e627c1c6d7473b1987ba7850edc4f597d22a915f4bc6cd705d866ee8ce6","observation_id":"1048406d-16cc-49aa-913f-5068cd742585","resolution":{"observed_at":"2026-05-18T18:46:45.407249Z","resolver_source":"arxiv_id","status":"malformed_identifier"},"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":"2503.04975","last_updated":"2025-03-06T21:10:12Z","snapshot_observed_at":"2026-08-07T17:23:30.914733Z","submitted_at":"2025-03-06T21:10:12Z","title":"Energy-Weighted Flow Matching for Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.04975","snapshot_observed_at":"2026-08-05T10:30:23.678531Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.04063","last_updated":"2025-09-04T09:48:43Z","snapshot_observed_at":"2026-08-06T19:50:15.003871Z","submitted_at":"2025-09-04T09:48:43Z","title":"Balancing Signal and Variance: Adaptive Offline RL Post-Training for VLA Flow Models","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-05T10:30:23.678531Z"},"links":{"cited_paper":"/paper/2503.04975","citing_paper":"/paper/2509.04063"},"observation_digest":"sha256:9ee653f1b58179faab19a7f010ba2e7c32dcad0a38f1d1d219148e23e7f8b90c","observation_id":"94d2adb0-a693-488f-9d59-14070dd5b872","resolution":{"observed_at":"2026-08-05T10:30:23.678531Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.04975","last_updated":"2025-03-06T21:10:12Z","snapshot_observed_at":"2026-08-07T17:23:30.914733Z","submitted_at":"2025-03-06T21:10:12Z","title":"Energy-Weighted Flow Matching for Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2503.04975","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.04975","snapshot_observed_at":"2026-07-03T04:17:36.914370Z","title":"Energy-weighted flow matching for offline reinforcement learning","venue":null,"work_id":"a3aa6122-6b44-4df6-9041-e6de954d3a70","year":2025},"citing_paper":{"arxiv_id":"2512.22597","last_updated":"2026-05-22T12:56:03Z","snapshot_observed_at":"2026-08-03T23:39:50.738932Z","submitted_at":"2025-12-27T14:00:22Z","title":"Energy-Guided Generative Modeling for Low-Energy Molecular Structure Discovery","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-05-25T07:19:33.319408Z"},"links":{"cited_paper":"/paper/2503.04975","citing_paper":"/paper/2512.22597"},"observation_digest":"sha256:31f1d99040251d076cc725124f491d55524cf03ccd54fc6f1551b3e37a0f32b3","observation_id":"4a5f37e5-1e07-4c3b-8758-2b5c9a4b57f3","resolution":{"observed_at":"2026-05-25T07:20:28.586027Z","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":"2503.04975","last_updated":"2025-03-06T21:10:12Z","snapshot_observed_at":"2026-08-07T17:23:30.914733Z","submitted_at":"2025-03-06T21:10:12Z","title":"Energy-Weighted Flow Matching for Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.04975","snapshot_observed_at":"2026-07-14T23:47:45.866615Z","title":"Energy-weighted flow matching for offline reinforcement learning.arXiv preprint arXiv:2503.04975, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.10250","last_updated":"2026-05-24T03:43:13Z","snapshot_observed_at":"2026-08-06T17:26:04.405292Z","submitted_at":"2026-03-10T22:01:13Z","title":"GeMPO: Generalized Measure Matching for Online Diffusion Reinforcement Learning","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-07-14T23:47:45.866615Z"},"links":{"cited_paper":"/paper/2503.04975","citing_paper":"/paper/2603.10250"},"observation_digest":"sha256:f5b5c5a68da9389677ccb2e37d315c0972cada09ab2a9db294842262560c9146","observation_id":"84204006-5e45-46e0-89e4-b413bdf9cc95","resolution":{"observed_at":"2026-07-14T23:47:45.866615Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.04975","last_updated":"2025-03-06T21:10:12Z","snapshot_observed_at":"2026-08-07T17:23:30.914733Z","submitted_at":"2025-03-06T21:10:12Z","title":"Energy-Weighted Flow Matching for Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2503.04975","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.04975","snapshot_observed_at":"2026-07-03T04:17:36.914370Z","title":"Energy-weighted flow matching for offline reinforcement learning","venue":null,"work_id":"a3aa6122-6b44-4df6-9041-e6de954d3a70","year":2025},"citing_paper":{"arxiv_id":"2604.10962","last_updated":"2026-04-13T03:56:37Z","snapshot_observed_at":"2026-08-02T07:19:58.094243Z","submitted_at":"2026-04-13T03:56:37Z","title":"ScoRe-Flow: Complete Distributional Control via Score-Based Reinforcement Learning for Flow Matching","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-10T16:02:36.261596Z"},"links":{"cited_paper":"/paper/2503.04975","citing_paper":"/paper/2604.10962"},"observation_digest":"sha256:6b4c57a142d5b65970c36b702decdc97dd838f5c24e2dcc0a555d26d58c7aa7d","observation_id":"a6e20c66-cabf-44a8-b02c-b6a7f6f6fbbd","resolution":{"observed_at":"2026-05-11T09:26:00.564013Z","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":"2503.04975","last_updated":"2025-03-06T21:10:12Z","snapshot_observed_at":"2026-08-07T17:23:30.914733Z","submitted_at":"2025-03-06T21:10:12Z","title":"Energy-Weighted Flow Matching for Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2503.04975","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.04975","snapshot_observed_at":"2026-07-03T04:17:36.914370Z","title":"Energy-weighted flow matching for offline reinforcement learning","venue":null,"work_id":"a3aa6122-6b44-4df6-9041-e6de954d3a70","year":2025},"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":9,"source":"pdf_text","source_observed_at":"2026-05-10T05:28:12.298066Z"},"links":{"cited_paper":"/paper/2503.04975","citing_paper":"/paper/2604.17919"},"observation_digest":"sha256:84133272d3a9e7f3ab5f89e0b16aadca57e3883497b869ade4f217a337104ec6","observation_id":"3f0949f7-ff61-4725-847c-30abeceb54e5","resolution":{"observed_at":"2026-05-10T06:51:46.547398Z","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":"2503.04975","last_updated":"2025-03-06T21:10:12Z","snapshot_observed_at":"2026-08-07T17:23:30.914733Z","submitted_at":"2025-03-06T21:10:12Z","title":"Energy-Weighted Flow Matching for Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2503.04975","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.04975","snapshot_observed_at":"2026-07-03T04:17:36.914370Z","title":"Energy-weighted flow matching for offline reinforcement learning","venue":null,"work_id":"a3aa6122-6b44-4df6-9041-e6de954d3a70","year":2025},"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":74,"source":"arxiv_source","source_observed_at":"2026-05-10T16:25:25.739019Z"},"links":{"cited_paper":"/paper/2503.04975","citing_paper":"/paper/2605.01663"},"observation_digest":"sha256:59a9fc62af10f584488b9fb0792f9e2bd4497bbdbb9aa24d465c42cff1402073","observation_id":"51ee294b-35f5-4a7b-8e6c-82d53ee7a862","resolution":{"observed_at":"2026-05-11T08:56:00.636875Z","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":"2503.04975","last_updated":"2025-03-06T21:10:12Z","snapshot_observed_at":"2026-08-07T17:23:30.914733Z","submitted_at":"2025-03-06T21:10:12Z","title":"Energy-Weighted Flow Matching for Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2503.04975","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.04975","snapshot_observed_at":"2026-07-03T04:17:36.914370Z","title":"Energy-weighted flow matching for offline reinforcement learning","venue":null,"work_id":"a3aa6122-6b44-4df6-9041-e6de954d3a70","year":2025},"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":36,"source":"arxiv_source","source_observed_at":"2026-05-12T02:12:58.528130Z"},"links":{"cited_paper":"/paper/2503.04975","citing_paper":"/paper/2605.08253"},"observation_digest":"sha256:418fef40ceca9d4f2ae8096e6e4f226c9dfc16c12db318d67535489bce3f254b","observation_id":"87e11243-4697-4730-8082-7d22e11a4bcd","resolution":{"observed_at":"2026-05-12T02:16:16.244558Z","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":"2503.04975","last_updated":"2025-03-06T21:10:12Z","snapshot_observed_at":"2026-08-07T17:23:30.914733Z","submitted_at":"2025-03-06T21:10:12Z","title":"Energy-Weighted Flow Matching for Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2503.04975","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.04975","snapshot_observed_at":"2026-07-03T04:17:36.914370Z","title":"Energy-weighted flow matching for offline reinforcement learning","venue":null,"work_id":"a3aa6122-6b44-4df6-9041-e6de954d3a70","year":2025},"citing_paper":{"arxiv_id":"2605.11726","last_updated":"2026-05-13T15:38:02Z","snapshot_observed_at":"2026-07-06T23:23:30.499023Z","submitted_at":"2026-05-12T08:09:42Z","title":"Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-05-13T07:03:00.503644Z"},"links":{"cited_paper":"/paper/2503.04975","citing_paper":"/paper/2605.11726"},"observation_digest":"sha256:729667167b8bcebc5f581862bd33c2588e05b71438d5f5b11d3d4b14cfd7bceb","observation_id":"f90e15fa-9a45-4f93-aa93-b9c8892a09fe","resolution":{"observed_at":"2026-05-13T07:07:28.035467Z","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":"2503.04975","last_updated":"2025-03-06T21:10:12Z","snapshot_observed_at":"2026-08-07T17:23:30.914733Z","submitted_at":"2025-03-06T21:10:12Z","title":"Energy-Weighted Flow Matching for Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2503.04975","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.04975","snapshot_observed_at":"2026-07-03T04:17:36.914370Z","title":"Energy-weighted flow matching for offline reinforcement learning","venue":null,"work_id":"a3aa6122-6b44-4df6-9041-e6de954d3a70","year":2025},"citing_paper":{"arxiv_id":"2605.11726","last_updated":"2026-05-13T15:38:02Z","snapshot_observed_at":"2026-07-06T23:23:30.499023Z","submitted_at":"2026-05-12T08:09:42Z","title":"Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-05-14T21:06:01.667173Z"},"links":{"cited_paper":"/paper/2503.04975","citing_paper":"/paper/2605.11726"},"observation_digest":"sha256:ec84da9970f6fb94a50fed999792445e996bd7826ec0a8f94a5747766088f5d8","observation_id":"f393a682-3814-49ab-bdcd-b63ffe42f1e6","resolution":{"observed_at":"2026-05-14T21:19:28.253263Z","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":"2503.04975","last_updated":"2025-03-06T21:10:12Z","snapshot_observed_at":"2026-08-07T17:23:30.914733Z","submitted_at":"2025-03-06T21:10:12Z","title":"Energy-Weighted Flow Matching for Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2503.04975","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.04975","snapshot_observed_at":"2026-07-03T04:17:36.914370Z","title":"Energy-weighted flow matching for offline reinforcement learning","venue":null,"work_id":"a3aa6122-6b44-4df6-9041-e6de954d3a70","year":2025},"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":61,"source":"pdf_text","source_observed_at":"2026-06-29T08:44:53.969301Z"},"links":{"cited_paper":"/paper/2503.04975","citing_paper":"/paper/2605.29398"},"observation_digest":"sha256:9cd32fe687b3eabfaf57c87bc5defd9f9a8340175b39fa39910f945298050592","observation_id":"64f22c4e-39f9-4d5a-a369-00ac0139900b","resolution":{"observed_at":"2026-06-29T08:53:16.403111Z","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":"2503.04975","last_updated":"2025-03-06T21:10:12Z","snapshot_observed_at":"2026-08-07T17:23:30.914733Z","submitted_at":"2025-03-06T21:10:12Z","title":"Energy-Weighted Flow Matching for Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2503.04975","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.04975","snapshot_observed_at":"2026-07-03T04:17:36.914370Z","title":"Energy-weighted flow matching for offline reinforcement learning","venue":null,"work_id":"a3aa6122-6b44-4df6-9041-e6de954d3a70","year":2025},"citing_paper":{"arxiv_id":"2606.11087","last_updated":"2026-06-09T16:45:57Z","snapshot_observed_at":"2026-08-02T20:45:09.177143Z","submitted_at":"2026-06-09T16:45:57Z","title":"Test-Time Gradient Guidance of Flow Policies in Reinforcement Learning","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-06-27T14:05:01.073951Z"},"links":{"cited_paper":"/paper/2503.04975","citing_paper":"/paper/2606.11087"},"observation_digest":"sha256:572414d9d77398775d567d2fdac5522d3a3a551e98c77c377fd6678a319bff0c","observation_id":"acfaa4a8-dac4-4390-b7d6-f0befc83a709","resolution":{"observed_at":"2026-07-03T04:17:36.915862Z","resolver_source":"arxiv_id","status":"malformed_identifier"},"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":"2503.04975","last_updated":"2025-03-06T21:10:12Z","snapshot_observed_at":"2026-08-07T17:23:30.914733Z","submitted_at":"2025-03-06T21:10:12Z","title":"Energy-Weighted Flow Matching for Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2503.04975","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.04975","snapshot_observed_at":"2026-07-03T04:17:36.914370Z","title":"Energy-weighted flow matching for offline reinforcement learning","venue":null,"work_id":"a3aa6122-6b44-4df6-9041-e6de954d3a70","year":2025},"citing_paper":{"arxiv_id":"2606.29820","last_updated":"2026-06-29T05:57:33Z","snapshot_observed_at":"2026-08-02T04:45:58.960142Z","submitted_at":"2026-06-29T05:57:33Z","title":"Dual-Flow Reinforcement Learning with State-Aware Exploration","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-30T07:16:11.130271Z"},"links":{"cited_paper":"/paper/2503.04975","citing_paper":"/paper/2606.29820"},"observation_digest":"sha256:c21f058fc98c16abfed09bfcb3e2b0cf2ce06e7c6029bc4b064afb426d64b7e1","observation_id":"27331144-09a4-4232-9a09-094465b2bc99","resolution":{"observed_at":"2026-06-30T07:24:21.817296Z","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":"2503.04975","last_updated":"2025-03-06T21:10:12Z","snapshot_observed_at":"2026-08-07T17:23:30.914733Z","submitted_at":"2025-03-06T21:10:12Z","title":"Energy-Weighted Flow Matching for Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2503.04975","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.04975","snapshot_observed_at":"2026-07-03T04:17:36.914370Z","title":"Energy-weighted flow matching for offline reinforcement learning","venue":null,"work_id":"a3aa6122-6b44-4df6-9041-e6de954d3a70","year":2025},"citing_paper":{"arxiv_id":"2606.30376","last_updated":"2026-06-29T14:37:36Z","snapshot_observed_at":"2026-08-07T13:52:35.062571Z","submitted_at":"2026-06-29T14:37:36Z","title":"FlowAWR: Online Adaptive Flow Reinforcement via Advantage-Weighted Rectification","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-30T07:10:44.244790Z"},"links":{"cited_paper":"/paper/2503.04975","citing_paper":"/paper/2606.30376"},"observation_digest":"sha256:963a9f425ae8e5ad362c2d0d1d4fc8f47d84fa9d1599160647b43c51e50b5220","observation_id":"a37c77e0-f56c-4138-b8d7-e6e7f43221b8","resolution":{"observed_at":"2026-06-30T07:14:20.938562Z","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":"2503.04975","last_updated":"2025-03-06T21:10:12Z","snapshot_observed_at":"2026-08-07T17:23:30.914733Z","submitted_at":"2025-03-06T21:10:12Z","title":"Energy-Weighted Flow Matching for Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.04975","snapshot_observed_at":"2026-07-14T08:30:10.584105Z","title":"Energy-weighted flow matching for offline reinforcement learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10892","last_updated":"2026-07-12T19:48:46Z","snapshot_observed_at":"2026-08-08T14:33:47.467368Z","submitted_at":"2026-07-12T19:48:46Z","title":"A Single Diffusion-Policy Controller for Multi-Task Block Pushing with Zero-Shot Sim-to-Real Transfer","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-14T08:30:10.584105Z"},"links":{"cited_paper":"/paper/2503.04975","citing_paper":"/paper/2607.10892"},"observation_digest":"sha256:1821a1b47d5fd18c1c31865f240c18a72fee92c2ed3c841e0c927472538ed1a3","observation_id":"4ef6ed66-eda6-4fab-87fc-b290f058f51f","resolution":{"observed_at":"2026-07-14T08:30:10.584105Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2503.04975/citation-record","integrity":"/paper/2503.04975/integrity","json":"/paper/2503.04975/citation-record.json","paper":"/paper/2503.04975"},"outbound":[],"paper":{"arxiv_id":"2503.04975","last_updated":"2025-03-06T21:10:12Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T17:23:30.914733Z","submitted_at":"2025-03-06T21:10:12Z","title":"Energy-Weighted Flow Matching for Offline Reinforcement Learning"},"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 18 inbound Pith citation observations for arXiv:2503.04975."}