{"as_of":"2026-08-22T03:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d05e32e1a14eb57791e58fdefb2a855ed16c015482b10eb8297f0ddedb24d37f","coverage":[{"denominator":44,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":44,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T00:18:38.022261Z","state":"measured"},{"denominator":44,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":44,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+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/2501.18229/citation-record","integrity":"/paper/2501.18229/integrity","json":"/paper/2501.18229/citation-record.json","paper":"/paper/2501.18229"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2312.01097","last_updated":"2023-12-02T10:07:17Z","snapshot_observed_at":"2026-08-16T14:38:15.369869Z","submitted_at":"2023-12-02T10:07:17Z","title":"Planning as In-Painting: A Diffusion-Based Embodied Task Planning Framework for Environments under Uncertainty","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.01097","snapshot_observed_at":"2026-08-10T00:18:37.449639Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.449639Z"},"links":{"cited_paper":"/paper/2312.01097","citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:2d39c6878565db132a43597284065c125572dc2a1224a53cdea2bce0feaf17fc","observation_id":"c1949482-e3b6-412c-a3e9-2adf01818b29","resolution":{"observed_at":"2026-08-10T00:18:37.449639Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:40.803886Z","title":"Compositional Diffusion-Based Continuous Constraint Solvers,","venue":null,"work_id":"d3047e97-31ac-4194-9062-e94636c6be9c","year":2023},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.520148Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:0f97c67ce74ffc092033bc190c29a1ba4c824c1ebbf085a52d5272c1832dfa1b","observation_id":"707c54b9-dbd0-480e-9dbe-1384ed14de7a","resolution":{"observed_at":"2026-08-10T00:18:40.852250Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.12609","last_updated":"2024-02-12T07:50:24Z","snapshot_observed_at":"2026-08-16T14:50:46.901349Z","submitted_at":"2023-10-19T09:39:07Z","title":"Denoising Heat-inspired Diffusion with Insulators for Collision Free Motion Planning","version":4},"cited_work":{"arxiv_id":"2310.12609","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.12609","snapshot_observed_at":"2026-08-10T00:18:39.590944Z","title":"Denoising Heat-inspired Diffusion with Insulators for Collision Free Motion Planning","venue":"cs.RO","work_id":"d8da3c5e-fa68-4193-8ede-2eabebcea171","year":2023},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.523859Z"},"links":{"cited_paper":"/paper/2310.12609","citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:90e5965706f1f586c40f7004153b46c0e22c4f27fa55ce3da45135cbad778a8a","observation_id":"386d0de7-52bd-4b3e-bc68-9ad3cb96016e","resolution":{"observed_at":"2026-08-10T00:18:39.595998Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:40.776036Z","title":"DiMSam: Diffusion models as samplers for task and motion planning under partial observability,","venue":null,"work_id":"8c8cceba-d6d9-45e1-99de-6ac3f8be23cf","year":2023},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.528596Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:70f03f4f22f6c4a244aee49bc5ff3b87ea6a333acebf09f45a46d91b8c7c719e","observation_id":"97f53ed3-9682-451f-a16c-5e49e08e8c57","resolution":{"observed_at":"2026-08-10T00:18:40.780090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:40.764772Z","title":"Structdiffusion: Language-guided creation of physically-valid structures using unseen objects,","venue":null,"work_id":"f2965ef4-893b-40fc-85d1-96c1e0b0b31f","year":2023},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.534780Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:1795d8ddbdcf56c729ec28150dd65cf8774c1ba67be6284e82360ed48723e186","observation_id":"fcd55765-f1be-4e75-86d5-d88f26e37c79","resolution":{"observed_at":"2026-08-10T00:18:40.769231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:40.754634Z","title":"Plan- ning with diffusion for flexible behavior synthesis,","venue":null,"work_id":"994d9a61-33d7-414b-ba07-139c9d3737c6","year":2022},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.538792Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:fbab51cd2dda03f9aa5e3df81ba4b5cd75b0d7cbb9fe4536a5929924b0f7e385","observation_id":"47284b64-5556-402f-bdf2-2f7ecb24a471","resolution":{"observed_at":"2026-08-10T00:18:40.757967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:39.576025Z","title":"Cross- way diffusion: Improving diffusion-based visuomotor policy via self-supervised learning,","venue":null,"work_id":"5bb693a0-6b63-4ec6-a7b5-b5bdab22a9f0","year":2024},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.543106Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:e4b7dd596fabab2eeb508ed260bdaf582daebcb81205158ebf3866e9a1a04cd9","observation_id":"4b2c0294-a9f0-49b0-8516-e39a90a4a64f","resolution":{"observed_at":"2026-08-10T00:18:39.579871Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:40.744282Z","title":"Shelving, stacking, hanging: Relational pose diffusion for multi- modal rearrangement,","venue":null,"work_id":"e773a610-6d1e-4da7-ac8f-31ebd6f183df","year":2023},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.547675Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:43fb0d5c3b1b2c7add3c0390acd02ec5bd9ef9772711cfc1c6f5632936322c6f","observation_id":"394fc50c-08c2-45f2-9d40-e976620fa5b5","resolution":{"observed_at":"2026-08-10T00:18:40.748053Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:40.734540Z","title":"MidasTouch: Monte-Carlo inference over distributions across sliding touch,","venue":null,"work_id":"bfda6025-59d1-4257-8b14-e714057eb45d","year":2022},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.551650Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:b47b896f4be48ccd45d8f9d2312bd8f87270e072a065c807d4050282245b8368","observation_id":"0d9ae708-d3e6-47da-9435-d05518f13167","resolution":{"observed_at":"2026-08-10T00:18:40.738606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:40.723974Z","title":"Se (3)-diffusionfields: Learning smooth cost func- tions for joint grasp and motion optimization through diffusion,","venue":null,"work_id":"3d13addb-e968-4281-9aa2-ed8a257b7576","year":2023},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.555365Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:05209875b49dc6819d0ad4c3897804b0bfa71e386c22a49c0a6a2c685cfa67b1","observation_id":"dfeab4d5-e2c1-4c21-9bfb-a9734795b46a","resolution":{"observed_at":"2026-08-10T00:18:40.728002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:40.714223Z","title":"Unidexgrasp: Uni- versal robotic dexterous grasping via learning diverse proposal generation and goal-conditioned policy,","venue":null,"work_id":"efe151e8-e76e-4ee7-bc1f-b05057c38591","year":2023},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.558830Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:992d5191ae09f57fbd54fbec49aa73fe8df3ad06fc8a85885599266968df3af9","observation_id":"7db13a86-47de-4e3b-864e-351a3ff41c72","resolution":{"observed_at":"2026-08-10T00:18:40.717553Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:40.705773Z","title":"Motion planning diffusion: Learning and planning of robot motions with diffusion models,","venue":null,"work_id":"a120a2b8-d133-487e-b4c0-c1c047a95793","year":2023},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.563252Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:f818e0a46ecdbeea7da6d1e4f7c3d2cd51397f3c927fa14b401f97cea9621d7c","observation_id":"036ff20d-b9ea-4bb7-9327-eb3a4bfa7404","resolution":{"observed_at":"2026-08-10T00:18:40.709318Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:40.642561Z","title":"Edmp: Ensemble-of-costs-guided diffusion for motion plan- ning,","venue":null,"work_id":"1753a16c-66dc-4349-98b8-448dfbd37a8c","year":2024},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.569656Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:ece8680e9efa5f73f7298dc0cd073bac3678d41ca2215721c1b552d05ef5a48e","observation_id":"dcc25e7a-1591-4922-8525-681a8fd6a6e9","resolution":{"observed_at":"2026-08-10T00:18:40.668391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:40.531745Z","title":"Po- tential based diffusion motion planning,","venue":null,"work_id":"f605aca0-f55b-48ec-b9d7-cbcea01e460b","year":2024},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.573875Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:a6ed76a7c565d97eb698aa45f198bd34febc2926f4f9d985fba26230e20fd274","observation_id":"060b28ae-2bce-4fc0-9d24-31131353924b","resolution":{"observed_at":"2026-08-10T00:18:40.590100Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:40.312228Z","title":"Chomp: Gradient optimization techniques for efficient motion planning,","venue":null,"work_id":"75fea9d8-9a86-4ad8-beb1-bc4db36d0b27","year":2009},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.578852Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:b089ffe2d5c8e9ff135773706df3bb1052fd1491044dd220776bcd042d86766e","observation_id":"d3c67ef0-933c-49dc-acc6-06cac38f7a3e","resolution":{"observed_at":"2026-08-10T00:18:40.456451Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:40.259885Z","title":"Storm: An integrated framework for fast joint- space model-predictive control for reactive manipula- tion,","venue":null,"work_id":"8a9ffa2c-a6c0-4160-9995-d085cfb4908e","year":2022},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.583233Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:0108fb143b0b0203b7f6e509d9a6d90888b22589ac1982b68af65a5932eb67cb","observation_id":"6d6c2d0e-143c-429f-9f62-3309820c2245","resolution":{"observed_at":"2026-08-10T00:18:40.263798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:40.248109Z","title":"Finding locally optimal, collision-free trajectories with sequential convex optimization,","venue":null,"work_id":"51783585-14fc-459e-86f7-439e6f60f634","year":null},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.586854Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:37661b230269afe65e9a9ab78d0a41777e7938109cb2fa719374d2dc2c61ea89","observation_id":"15614c29-38ac-4c1d-b64e-6cc2ecde7b6c","resolution":{"observed_at":"2026-08-10T00:18:40.252684Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:39.530233Z","title":"A formal basis for the heuristic determination of minimum cost paths,","venue":null,"work_id":"3eb47888-8f10-4219-9274-b05f84a1a109","year":1968},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.596025Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:7a58587bdad749c6fc59297cc80e5b2d6e3a734983c559d8968c6fecd1a0465c","observation_id":"a42ad02b-5b1a-4a7e-9dc1-6ee1f7506625","resolution":{"observed_at":"2026-08-10T00:18:39.537247Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:40.235227Z","title":"Anytime dynamic a*: An anytime, replanning algorithm.,","venue":null,"work_id":"51ba76c8-c319-4d5b-b94f-e561629a0aa3","year":2005},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.600589Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:33ea559f2b83b754ce4a69d21c7896de3a73378c91cd815a9655c22cc0a01214","observation_id":"d795b838-7204-479d-a6eb-9232dfa2a823","resolution":{"observed_at":"2026-08-10T00:18:40.239649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:40.219630Z","title":"Ara*: Anytime a* with provable bounds on sub-optimality.,","venue":null,"work_id":"8e95d298-7203-4379-86c0-fa1258afe2ed","year":2003},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.604527Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:99e329df6854ce0ecc552f8b0677c7601a05bbfe538abb675b15e590481cc8ce","observation_id":"2255d56e-ecf3-4661-9aab-73d629f78340","resolution":{"observed_at":"2026-08-10T00:18:40.224319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:40.207384Z","title":"Rrt-connect: An efficient approach to single-query path planning,","venue":null,"work_id":"5a43cfc8-6add-43ab-a462-535778e8743e","year":2000},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.608519Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:0fd1b455a6715ad12e62035d6a1896cd8330e1cf1321395f45925382ccad4034","observation_id":"01dbc707-ee6e-4349-a77d-bf404ebf3577","resolution":{"observed_at":"2026-08-10T00:18:40.212051Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:40.195943Z","title":"Rapidly-exploring random trees : A new tool for path planning,","venue":null,"work_id":"5581a0fa-b8d3-4920-b691-5fc3c11f1a22","year":1998},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.612592Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:f862dcce73aaa83211170b1e8143cdf12a037fa390d910a7ba62bf66252b09cb","observation_id":"3b916908-36a3-455e-a158-5e56129b73b8","resolution":{"observed_at":"2026-08-10T00:18:40.200587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:39.367328Z","title":"Real-time model predictive control for quadrotors,","venue":null,"work_id":"45ec7adb-36d8-4bab-aaa9-dd4b44754c76","year":2014},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.616536Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:8c1e1d1e4b6f8e25edc1cd3bb612e1ee4e15bf9017b086d904e60db9be0b579d","observation_id":"3e63b89a-b690-40d9-a308-772fdf677ad3","resolution":{"observed_at":"2026-08-10T00:18:39.370842Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:39.193573Z","title":"An integrated system for real- time model predictive control of humanoid robots,","venue":null,"work_id":"952904bf-6500-4a2b-b8cb-4ce95fd9d10c","year":2013},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.667134Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:c9c416a86547e4c53308237f79cf5ecf8cc5a3e1fc8bf477fcb028314fa2af4d","observation_id":"65e6893d-f186-4916-b3c2-0392cb6c5c04","resolution":{"observed_at":"2026-08-10T00:18:39.199210Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1509.01149","last_updated":"2015-10-28T13:23:51Z","snapshot_observed_at":"2026-08-14T22:33:53.863727Z","submitted_at":"2015-09-03T16:18:30Z","title":"Model Predictive Path Integral Control using Covariance Variable Importance Sampling","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1509.01149","snapshot_observed_at":"2026-08-10T00:18:37.745993Z","title":"Model predictive path integral control using co- variance variable importance sampling,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.745993Z"},"links":{"cited_paper":"/paper/1509.01149","citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:52f8e46e4e308e03280c9bbe090ea7eeaec9dd5553b80f99723829e4f6c6c217","observation_id":"9f88381b-ce58-466e-92cb-5967dfa7c9e3","resolution":{"observed_at":"2026-08-10T00:18:37.745993Z","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":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:38.993091Z","title":"Aggressive driving with model predictive path integral control,","venue":null,"work_id":"df389e24-01a5-4ab9-8925-2a0c9727a971","year":2016},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.801597Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:dbf40c02a060906d96820b3f22e3f3a50defc75567102af830dd9085da876b27","observation_id":"9e5dc470-fcca-414b-8128-67a9f0d4b286","resolution":{"observed_at":"2026-08-10T00:18:38.997066Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:40.182220Z","title":"Mo- tions in microseconds via vectorized sampling-based planning,","venue":null,"work_id":"dc140dcd-7d89-4bce-9f96-3a2f2810fa0f","year":2024},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.925558Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:f39e1a2b6161a146cb5e28edb39511df5b28d5a96e1400818c21831a7f819abd","observation_id":"1d112954-2709-40b3-97ad-f9becdd99115","resolution":{"observed_at":"2026-08-10T00:18:40.186585Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:38.751517Z","title":"Hyperplan: A framework for motion planning algo- rithm selection and parameter optimization,","venue":null,"work_id":"0a0cbf88-6670-4ff1-87c6-fba50ed693f2","year":2021},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.976402Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:4003597ea72b0eda3bef74b2d947a9ad1af31b8756b258d9914fbf5d22268c79","observation_id":"11efa560-006f-45a8-b7f2-ea0a0dcd1179","resolution":{"observed_at":"2026-08-10T00:18:38.785938Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:38.432581Z","title":"Motion planning networks,","venue":null,"work_id":"6cb84a5a-ba21-443f-aa7e-c9bff4f07cfe","year":2019},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.979459Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:ad0c24b023ca679bdc5182072d9e8a2d307f8f1b0d0f9d34cb93d1700a780ff8","observation_id":"8643ee4a-52c1-43a4-afbb-b1b8d577c6d2","resolution":{"observed_at":"2026-08-10T00:18:38.536109Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:40.157310Z","title":"Motion policy networks,","venue":null,"work_id":"026ba67c-7014-4576-8202-21b0b7d02147","year":2023},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.983304Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:7b50253247f089637d21182a4babd912dd29b38ba9ff9aed56fb7975656b3cc2","observation_id":"2969d305-e4c4-42a1-af24-ad428fb558bc","resolution":{"observed_at":"2026-08-10T00:18:40.161474Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:40.106264Z","title":"Dipper: Diffusion-based 2d path planner applied on legged robots,","venue":null,"work_id":"8731c186-dd1c-42b2-b990-9ab1ba896169","year":2024},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.986899Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:2b3cd2efc2e6de9ac4f6c09ecb1cde4cf1dd5fbe54afd2c21471b80a9aa018f6","observation_id":"27cd7ce1-2350-4aa0-bb94-6cd0d7301937","resolution":{"observed_at":"2026-08-10T00:18:40.149181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:39.693503Z","title":"Diffusion-es: Gradient-free planning with diffusion for autonomous and instruction-guided driving,","venue":null,"work_id":"333b3a4c-a7fa-4e05-8bc4-f84abe783167","year":2024},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.994077Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:4b40662de89c5fd8e549e7610179e341340e07def36f06611a55f66b81e7e0f7","observation_id":"9ece4940-acd1-4881-a262-1615584f7ead","resolution":{"observed_at":"2026-08-10T00:18:39.847399Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:39.661057Z","title":"Dpm-solver: A fast ode solver for diffusion proba- bilistic model sampling in around 10 steps,","venue":null,"work_id":"0b58643b-8f6d-4dad-94bc-b7859896252f","year":2022},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.997476Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:a322c9f49c92bbb3b375fb98453b9f9166288e72ec1d88c8d028e7bcd237c1f9","observation_id":"f0c8f56e-1429-4452-813e-ee672ff93567","resolution":{"observed_at":"2026-08-10T00:18:39.666808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-08-11T15:38:14.931716Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-08-10T00:18:38.001159Z","title":"Denoising diffusion implicit models,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:38.001159Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:47e1da891a3d5a89a2738d2afb5c1903d703d15b99b63202da6bdf7215fe9b64","observation_id":"6e453ac0-4855-4ad0-94fa-dbdab97e56e8","resolution":{"observed_at":"2026-08-10T00:18:38.001159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:39.648814Z","title":"Deep unsupervised learning us- ing nonequilibrium thermodynamics,","venue":null,"work_id":"842c2a0d-f601-4517-b46b-1d94d2e328fd","year":2015},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:38.004945Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:66e846a9359af40aacaa72cc46d8af3173f7e8caa69e85516a429b513aca8a84","observation_id":"1d247b44-4d63-477b-9bd3-0259dc7bffcb","resolution":{"observed_at":"2026-08-10T00:18:39.653139Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T00:18:38.008324Z","title":"Denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:38.008324Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:3fd724429aa504a0df5f7d6a32771270541e71c473755b102eb0580db6f68c41","observation_id":"5cb789e5-cdfe-47a6-a7cf-3cb2db843846","resolution":{"observed_at":"2026-08-10T00:18:38.008324Z","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":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:38.256102Z","title":"Rrt-connect: An efficient approach to single-query path planning,","venue":null,"work_id":"095951ac-ea07-4323-8293-b4890394359b","year":2000},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:38.011543Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:9628ee63f5d837ca3df295d618e0d7430300b7fb5961f9d258f662e7561cf9f0","observation_id":"44b439ea-a815-44e0-bb96-74230d4ed5ae","resolution":{"observed_at":"2026-08-10T00:18:38.261355Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:39.628664Z","title":"Coumans and Y","venue":null,"work_id":"ad042490-ec4a-4add-8961-af96d939a4de","year":2016},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:38.014909Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:c6218b26578671890991458c6bb4695f49b7b29c02cc8c97014c2abf2a25e712","observation_id":"03dccc0a-96a6-439d-a90b-d2ba5c120e1b","resolution":{"observed_at":"2026-08-10T00:18:39.632676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:39.617097Z","title":"Adaptively in- formed trees (ait): Fast asymptotically optimal path planning through adaptive heuristics,","venue":null,"work_id":"98a305cd-791e-44b5-8400-9ea5edace444","year":2020},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:38.018326Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:74fce39016938d24bac8456c0607aa0b63c284a8f75dc70470959742921e7144","observation_id":"7e1e8e11-83a4-491a-9db6-0bc8172543d9","resolution":{"observed_at":"2026-08-10T00:18:39.621768Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.14750","last_updated":"2021-06-25T18:26:20Z","snapshot_observed_at":"2026-08-16T19:10:03.404944Z","submitted_at":"2020-10-28T04:59:47Z","title":"Geometric Fabrics for the Acceleration-based Design of Robotic Motion","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.14750","snapshot_observed_at":"2026-08-10T00:18:38.022261Z","title":"Geometric fabrics for the acceleration-based design of robotic motion,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:38.022261Z"},"links":{"cited_paper":"/paper/2010.14750","citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:9efd67291c2b1bac699b769d98afbae2c0bb047775e862553ffe6fa494a1ab77","observation_id":"0184c17e-8b4a-47b7-8c09-a2abab61cb08","resolution":{"observed_at":"2026-08-10T00:18:38.022261Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:40.696754Z","title":"1109 / IROS55552","venue":null,"work_id":"15c38a7a-a497-4d14-bdb1-76a714131beb","year":2023},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":1923,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.566576Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:135985e6417f02bb4e568aced4dbeacc2d943ac90e163f7f7d070a21d280bda0","observation_id":"cfbaa2ed-7e67-42ba-a27b-5d238d0b865a","resolution":{"observed_at":"2026-08-10T00:18:40.700590Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T00:18:37.591452Z","title":null,"venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":2013,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.591452Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:09757a0746b92906fd142beb5c1d997656c4b8f57014ee0ce7d81a82f58168a9","observation_id":"fd265ec0-bc42-462d-8316-b6d083acb3aa","resolution":{"observed_at":"2026-08-10T00:18:37.591452Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:40.171719Z","title":"1109 / ICRA57147","venue":null,"work_id":"1afef2f8-e7a7-4c8b-8d66-fd0cd1b6050d","year":2024},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":8756,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.973743Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:81a5562e59f52f3a1e11e463cfe0658308907cb0d082a5fe3a2a5c1e129961f5","observation_id":"d79b4d86-84ca-4dec-bb28-2cd5683b97b3","resolution":{"observed_at":"2026-08-10T00:18:40.175625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:18:39.908330Z","title":"1109 / ICRA57147","venue":null,"work_id":"ddbc2062-166f-450f-ae09-93b9aed28553","year":2024},"citing_paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning","version":1},"reference_index":9270,"source":"pdf_text","source_observed_at":"2026-08-10T00:18:37.991051Z"},"links":{"citing_paper":"/paper/2501.18229"},"observation_digest":"sha256:6df39c7df04f4e3cb177165f6f2fdb338be8bcadfaad3862419a8bd0d8874502","observation_id":"f78d3658-1b26-4fed-82d6-9bc91cfe9e7b","resolution":{"observed_at":"2026-08-10T00:18:39.961213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.18229","last_updated":"2025-01-30T09:35:17Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-14T06:45:33.301190Z","submitted_at":"2025-01-30T09:35:17Z","title":"GPD: Guided Polynomial Diffusion for Motion Planning"},"reference_resolution":{"displayed":44,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":9,"verified_fuzzy":29},"total_outbound_references":44},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2501.18229."}