{"as_of":"2026-08-04T14:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c6f95bfad25c3215cf5bb12a33edc1f0ae93a79b16b340d08a296900e7653b43","coverage":[{"denominator":54,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":54,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-12T02:18:19.764706Z","state":"measured"},{"denominator":54,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":54,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+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/2605.09196/citation-record","integrity":"/paper/2605.09196/integrity","json":"/paper/2605.09196/citation-record.json","paper":"/paper/2605.09196"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2212.03574","last_updated":"2022-12-07T11:22:42Z","snapshot_observed_at":"2026-07-06T14:27:50.004915Z","submitted_at":"2022-12-07T11:22:42Z","title":"Learning rigid dynamics with face interaction graph networks","version":1},"cited_work":{"arxiv_id":"2212.03574","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2212.03574","snapshot_observed_at":"2026-07-04T13:29:51.722831Z","title":"Learning rigid dynamics with face interaction graph networks","venue":null,"work_id":"8e505b38-6004-4ae1-baa3-336c4208c4a4","year":2022},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"cited_paper":"/paper/2212.03574","citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:27b028614cf269ecc91658256801187610b06aa58c089c78c7932afcebfef5eb","observation_id":"0d5d41bc-714a-4fe2-be8c-6168ffbb4534","resolution":{"observed_at":"2026-05-12T02:21:16.892748Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Genesis: A universal and generative physics engine for robotics and beyond","venue":null,"work_id":"3352cd64-e78a-43dc-9cbd-99021f8b9065","year":2024},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:f14fd7576678abe9eae474ae247ec9290a80084c91749418f6e14eaf8573761f","observation_id":"2aa67b51-0567-42f0-a068-00bc3322c491","resolution":{"observed_at":"2026-05-12T23:21:56.579894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Interaction networks for learning about objects, relations and physics.Advances in neural information processing systems, 29","venue":null,"work_id":"9664e214-5ad4-4c41-aca1-32fc3aad0a49","year":2016},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:e47e0e90c9b868a195140e688327a28b3775ad2085ec938aa3641b4c76bc0252","observation_id":"37e15897-5b44-4902-8e25-a3396b66200c","resolution":{"observed_at":"2026-05-12T23:21:56.644667Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Deep regression on manifolds: a 3d rotation case study","venue":null,"work_id":"d07c8786-0f0e-476a-a11c-28c8e31ed3d7","year":2021},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:0777b7f7d7ba6511683ce5797bf342bac649ed73252e13c406f94b0debac888c","observation_id":"ad844f94-6010-4fc8-b399-ccec4c37c973","resolution":{"observed_at":"2026-05-12T23:21:56.622078Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2017.798902","doi":"10.1109/icra.2017.7989023","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"SE3-Nets: Learning rigid body motion using deep neural networks","venue":null,"work_id":"4685f82d-eb8b-46ea-b316-deb951df9a7c","year":2017},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:3156fec33543464d6eb2d4e5701aabbc185655f0b0c092d8c1f22f260daa33aa","observation_id":"c87cd3e4-3b22-411e-9ac6-f38c6914d566","resolution":{"observed_at":"2026-05-12T02:21:15.666832Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1612.00341","last_updated":"2017-03-04T17:44:06Z","snapshot_observed_at":"2026-07-06T05:20:59.857848Z","submitted_at":"2016-12-01T16:39:04Z","title":"A Compositional Object-Based Approach to Learning Physical Dynamics","version":2},"cited_work":{"arxiv_id":"1612.00341","doi":null,"metadata_source":"pith","pith_arxiv_id":"1612.00341","snapshot_observed_at":"2026-07-04T13:19:51.057173Z","title":"A Compositional Object-Based Approach to Learning Physical Dynamics","venue":"cs.AI","work_id":"1a6f9fab-1bad-4551-bd46-536f9d81d505","year":2016},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"cited_paper":"/paper/1612.00341","citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:5e1e51969fbb39acc354e3c518645fa5483da5ba72e31e9c273010e2fdcdd502","observation_id":"76b86b2f-cd05-403f-b2d8-2d86fa8ffe6a","resolution":{"observed_at":"2026-05-12T02:21:16.916940Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Virtual elastic objects","venue":null,"work_id":"694d06b8-19ff-4c6d-af32-488cc647b46f","year":2022},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:ba4b04e81ce2d02c72b8a83cd89748fe3213434645b13504e5d300082ac18438","observation_id":"bbfd4ef6-12e5-4a06-818b-0184c7c15289","resolution":{"observed_at":"2026-05-12T23:21:56.685697Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.03902","last_updated":"2021-10-27T17:16:56Z","snapshot_observed_at":"2026-07-06T10:12:35.310463Z","submitted_at":"2020-11-08T04:33:54Z","title":"Learning Neural Event Functions for Ordinary Differential Equations","version":4},"cited_work":{"arxiv_id":"2011.03902","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2011.03902","snapshot_observed_at":"2026-07-03T04:17:37.102691Z","title":"Learning neural event functions for ordinary differential equations","venue":null,"work_id":"4a9fe6fd-cbcf-45ed-8711-b614a26bc15a","year":2011},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"cited_paper":"/paper/2011.03902","citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:3f077e97b340bea44fd04573d976c68b3189829452d33d161fcdcb228d09b457","observation_id":"54e649f7-7806-4a50-a432-b7f538a0b196","resolution":{"observed_at":"2026-05-12T02:21:16.909561Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Pybullet, a python module for physics simulation for games, robotics and machine learning","venue":null,"work_id":"92001978-86f8-409d-a343-9ee2a9a7ba01","year":2016},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:01e26690efe58a5f754e5bc9cb1955091b756b225046840beb655801e6438310","observation_id":"acd37efc-1a19-4633-9354-4d40aa28d23e","resolution":{"observed_at":"2026-05-12T23:21:56.648073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-07-09T10:36:10.633869Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","venue":null,"work_id":"1bdc18bb-17d9-44c5-8f2b-ca096572a66b","year":2019},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:8aa2752db9e9b3e97ff8401a93c4904c067fa92bd579ed4b6f49874b2e2a5de8","observation_id":"f16ebe8d-5219-4b9b-80fa-ea8ece7c06ab","resolution":{"observed_at":"2026-05-12T23:21:56.640826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13281","last_updated":"2021-06-24T19:09:12Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T19:09:12Z","title":"Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation","version":1},"cited_work":{"arxiv_id":"2106.13281","doi":"10.48550/arxiv.2106.13281","metadata_source":"pith","pith_arxiv_id":"2106.13281","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Brax–a differentiable physics engine for large scale rigid body simulation","venue":"cs.RO","work_id":"0ec42bc1-d52a-4486-a223-298fabbf8504","year":2021},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"cited_paper":"/paper/2106.13281","citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:4cec94ac6942d6561c303b64e17f3e26bb20447689ca9c0b7c3c04fb048d028f","observation_id":"7135d664-6a18-4e3a-ac5d-b9be6758984f","resolution":{"observed_at":"2026-05-12T02:21:16.905621Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-03T14:38:10.488566+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-03T14:38:10.488566+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-07-10T22:57:42.777153Z","title":"Fast r-cnn","venue":null,"work_id":"b734ebda-f4bc-4627-a1c6-29f231a6e7e4","year":2015},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:39996015c1a382b626242942481d0d896516fb96f75c09928a8b01fe68af4e12","observation_id":"c5ae0abe-09c1-44cc-bed0-8139b4557e6f","resolution":{"observed_at":"2026-05-12T23:21:56.655940Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Clustering to minimize the maximum intercluster distance.Theoretical computer science, 38:293–306","venue":null,"work_id":"ff3985bf-db5c-4568-82da-50b2de8ca4cc","year":1985},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:605865b846a6626298fad399e832b6f88ac397e2dcc31f90763f24f2cc395b01","observation_id":"d0bf41ed-4245-4c9e-89f0-49875b03f897","resolution":{"observed_at":"2026-05-12T23:21:56.689033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"33a6458d-3e14-4caf-804e-68ed2b495066","year":2022},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:e1b2d1d9994a6ea467b3a420d9522165f760aec3b9220c7b7ab05bb43a849739","observation_id":"1049b8d8-c436-411d-b84f-0d7740245cf7","resolution":{"observed_at":"2026-05-12T23:21:56.594654Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Rotary position embedding for vision transformer","venue":null,"work_id":"32292b6a-d6b5-41de-b707-4dee5f8f4016","year":2024},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:d47389e17323e0dde1db67469add24ba5d2cd6e7e7d2c97dba03e2cbc19af5e5","observation_id":"fa0a8fbc-95cf-4f3f-afa4-2cc5c81a1f95","resolution":{"observed_at":"2026-05-12T23:21:56.591050Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.00935","last_updated":"2020-02-14T06:21:07Z","snapshot_observed_at":"2026-07-06T08:26:23.273408Z","submitted_at":"2019-10-01T05:00:26Z","title":"DiffTaichi: Differentiable Programming for Physical Simulation","version":3},"cited_work":{"arxiv_id":"1910.00935","doi":"10.48550/arxiv.1910.00935","metadata_source":"arxiv_reference","pith_arxiv_id":"1910.00935","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Difftaichi: Differentiable programming for physical simulation","venue":null,"work_id":"06509811-3543-4663-b998-31dd83e8aa20","year":1910},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"cited_paper":"/paper/1910.00935","citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:50796e69625a6c57e7fdef742e87f56eac0a342365e5eabc1f957a0a9f36f965","observation_id":"85fa477f-aa74-449a-9c01-6975045d1cb9","resolution":{"observed_at":"2026-05-12T02:21:16.888824Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-03T14:38:10.975022+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-03T14:38:10.975022+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2601.03782","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T18:47:31.636493Z","title":"arXiv preprint arXiv:2601.03782 (2026)","venue":null,"work_id":"13d28a56-ef0f-4caa-9fa7-9f494d91f062","year":2026},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:0b27d5deaa53b559e763d9f322af186e410efceb6ca6c415b535d0cf2f3c9e8b","observation_id":"247dc636-976d-44a2-b194-b6d74549aa29","resolution":{"observed_at":"2026-05-12T02:21:16.913137Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"A solution for the best rotation to relate two sets of vectors.Foundations of Crystallography, 32(5):922–923","venue":null,"work_id":"41ce67ff-63d3-4d16-ac99-abbcfbba56a8","year":1976},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:38226c3841133b410b182df5bebb92694d6c40a62e1a33624e799f358a78d7e3","observation_id":"20d93421-6bc3-46a1-a6ba-1694f367ac23","resolution":{"observed_at":"2026-05-12T23:21:56.608646Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Object dynamics modeling with hierarchical point cloud-based representations","venue":null,"work_id":"97f6e151-20e8-4b62-9c55-4c32597a0de2","year":2024},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:85d5e70c7fb70dfd84ef5b354fe6177ce91b8604919420fda8bb4d38a5449fa8","observation_id":"a16d1101-26c7-49ec-9683-37b7e87a1517","resolution":{"observed_at":"2026-05-12T23:21:56.563590Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2603.15031","last_updated":"2026-03-16T09:32:21Z","snapshot_observed_at":"2026-08-02T08:46:00.749789Z","submitted_at":"2026-03-16T09:32:21Z","title":"Attention Residuals","version":1},"cited_work":{"arxiv_id":"2603.15031","doi":"10.48550/arxiv.2603.15031","metadata_source":"pith","pith_arxiv_id":"2603.15031","snapshot_observed_at":"2026-07-10T14:57:14.484456Z","title":"Attention Residuals","venue":"cs.CL","work_id":"7356447b-f55f-41d1-b128-b3a54c4c879d","year":2026},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"cited_paper":"/paper/2603.15031","citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:1ce6ecab178c8d5a5873ea07fbfe82270ac33c1618db2201c5daeecef0f32635","observation_id":"7eb30562-3fc6-4402-8f3f-02245e83f7b2","resolution":{"observed_at":"2026-05-21T06:39:04.582456Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-05-25T12:53:25.083932+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-25T12:53:25.083932+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Mosca: Dynamic gaussian fusion from casual videos via 4d motion scaffolds","venue":null,"work_id":"05830441-adf5-4fde-a796-0802ca9054df","year":2025},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:5da9e80c91fc7a93780511baae05ac62257cf26f95d9a93154027da0303d59c1","observation_id":"5a16705b-ece6-41b0-a695-0076b29954d7","resolution":{"observed_at":"2026-05-12T23:21:56.567159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-07-09T19:36:29.521451Z","title":"Decoupled weight decay regularization","venue":null,"work_id":"139a920c-85a1-42e5-937e-f14d907436d5","year":2019},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:b477ac68db05edd3dfaee6331ed4dbaa1c85dc800b9332f35a443f406ada98fb","observation_id":"d393a8a9-cf5f-4f86-9d78-a62c0c8b8d29","resolution":{"observed_at":"2026-05-12T23:21:56.671098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Warp: A high-performance python framework for gpu simulation and graphics","venue":null,"work_id":"d59438be-4e41-4e2f-ae2b-af7108ce263d","year":2022},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:115c7b5c205b9c342978ae7d4676c2ae2addb8d230d9566762ef54ef92c42509","observation_id":"c2dc37af-b214-43c3-9c9a-a50ddd658854","resolution":{"observed_at":"2026-05-12T23:21:56.604666Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.10470","last_updated":"2021-08-25T23:42:59Z","snapshot_observed_at":"2026-07-06T11:40:56.544714Z","submitted_at":"2021-08-24T01:38:11Z","title":"Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning","version":2},"cited_work":{"arxiv_id":"2108.10470","doi":"10.48550/arxiv.2108.10470","metadata_source":"pith","pith_arxiv_id":"2108.10470","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning","venue":"cs.RO","work_id":"a21210c8-5b8f-429a-accc-fb4ca1efd19d","year":2021},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"cited_paper":"/paper/2108.10470","citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:9d79449c36612915b416c75c62f990a7a70034c1a06802a1b983e6237bb3136a","observation_id":"8247794e-3a2f-4450-bd63-24ae8be5aa79","resolution":{"observed_at":"2026-05-12T21:46:21.566811Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2510.13794","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T09:39:46.893763Z","title":"Mimickit: A reinforcement learning framework for motion imitation and control","venue":null,"work_id":"65749f98-8ec2-47cf-b9f3-eeb6a58c6f97","year":2025},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:03171b0c13d41ff41d53618532311011dcd29529327fbff102ec1035276b674a","observation_id":"247a3888-70b3-4d3d-92a6-1585a125b870","resolution":{"observed_at":"2026-05-12T02:21:16.935873Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Amp: Adversarial motion priors for stylized physics-based character control.ACM Transactions on Graphics (ToG), 40(4):1–20","venue":null,"work_id":"6bac932b-bae2-41cb-b07d-ca5a9be44b54","year":2021},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:7c15aae92b3e3420c4e135ab77ca2af41b4c6a7ee16733b2e9676e6afb4e8269","observation_id":"736f4a7b-8b41-4de9-8d86-c319583476b8","resolution":{"observed_at":"2026-05-12T23:21:56.663013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Ase: Large-scale reusable adversarial skill embeddings for physically simulated characters.ACM Transactions On Graphics (TOG), 41(4):1–17","venue":null,"work_id":"b9965e13-8120-4e2d-bfd7-090dd6490a4f","year":2022},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:953e703a72dd5d0fc604fb6840d37109cac547d7399d465c404e1477999ca65a","observation_id":"70beceea-16df-4255-bb7d-311a66d7138a","resolution":{"observed_at":"2026-05-12T23:21:56.612803Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-07-10T10:07:01.194897Z","title":"FiLM: Visual reasoning with a general conditioning layer","venue":null,"work_id":"36bc07d8-99f9-49e1-beed-7698081c6a62","year":2018},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:c879e905088eba3da9b56c36ec6c2508132eef839fd9c880ad73bf1de9a5e212","observation_id":"0818f408-600b-4573-848d-19ecce864dd6","resolution":{"observed_at":"2026-05-12T23:21:56.636954Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Learning mesh- based simulation with graph networks","venue":null,"work_id":"d2a1bce9-45b5-492e-881f-d6edabb899da","year":2020},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:1abaa14961b9de3a8a1ddb28c44fafda22b654795a814b0a0f8c66f81872054b","observation_id":"6ce65a52-d005-41a2-a4fb-ca00408d4c41","resolution":{"observed_at":"2026-05-12T23:21:56.692144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Pointnet: Deep learning on point sets for 3d classification and segmentation","venue":null,"work_id":"351cafb4-4d2f-4a8f-b300-0dcccd20d245","year":2017},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:6bedfc19397b5c65aaac2668e746060ea62adfedf11d3cb1686d1945b08579be","observation_id":"56da8852-bc2d-448c-bff1-bd96f901cc97","resolution":{"observed_at":"2026-05-12T23:21:56.598742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Pointnet++: Deep hierarchical feature learning on point sets in a metric space.Advances in neural information processing systems, 30","venue":null,"work_id":"1fe8254d-9c13-4b41-b35a-4b58761444b3","year":2017},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:27e63ed0c965a88d5848b5be5cdebab952aa0442ccf5b593ffe52f41868fa74b","observation_id":"883a3d00-16a2-44ee-a0f9-a8c6683fdb8a","resolution":{"observed_at":"2026-05-12T23:21:56.575339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.06708","last_updated":"2025-05-10T17:15:49Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-10T17:15:49Z","title":"Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free","version":1},"cited_work":{"arxiv_id":"2505.06708","doi":"10.48550/arxiv.2505.06708","metadata_source":"pith","pith_arxiv_id":"2505.06708","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free","venue":"cs.CL","work_id":"35cc586b-44f1-4948-a84b-866e8335e649","year":2025},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"cited_paper":"/paper/2505.06708","citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:0d5125132c1301676f478bc836f2593ffab6c4f9da30cd58339013826a64adb4","observation_id":"54d58277-a2c2-4cba-90b5-e89ed8ef434c","resolution":{"observed_at":"2026-05-12T09:04:34.965110Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Learning rigid-body simulators over implicit shapes for large- scale scenes and vision.Advances in Neural Information Processing Systems, 37:125809– 125838","venue":null,"work_id":"fe8b3f1b-7bbb-4baf-9578-ba9909d920d3","year":2024},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:187de5f759db0abcb00adeaa4c1516d76309ef619170d900808ce6e9e0407f67","observation_id":"98da547b-14b2-4c67-ab04-2d6f4e641076","resolution":{"observed_at":"2026-05-12T23:21:56.583657Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-07-10T17:57:26.595007Z","title":"Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063","venue":null,"work_id":"b5fd43ff-336f-45fd-933c-ffddf3003880","year":2024},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:5264d5f2245fe32c836f4b2d529aaef2c690cc8aff5a27039f3ed52ae2955cca","observation_id":"dcf1997b-7802-4083-9450-d6acb0df1098","resolution":{"observed_at":"2026-05-12T23:21:56.633301Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-07-06T22:42:58.894866Z","title":"Mujoco: A physics engine for model-based control","venue":null,"work_id":"1f8d1169-4b4f-438f-9b09-f3b07f8a3f37","year":2012},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:562a665292f0c20a08139bc0220302a3290b8572d4acb1fbb3b7144495b800eb","observation_id":"88a50912-8123-4ed2-ab51-0ec637a5297f","resolution":{"observed_at":"2026-05-12T23:21:56.625441Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Lion: Latent point diffusion models for 3d shape generation.Advances in Neural Information Processing Systems, 35:10021–10039","venue":null,"work_id":"a6fbdcc0-63c5-4a66-9a2b-d16bfcbcc9c3","year":2022},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:a06c820303b2c1d1fd251c82fb95c819d66d870e23928eac71aa4414eb779b16","observation_id":"fa672602-2495-4cb2-bcf3-9cd5887432d4","resolution":{"observed_at":"2026-05-12T23:21:56.615906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-07-10T15:47:23.567466Z","title":"Attention is all you need.Advances in neural information processing systems, 30","venue":null,"work_id":"751efe07-5e91-415c-b3d1-f4734aa26960","year":2017},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:02c850b280ddeb526bee15dd89c10b6079b9f6fb23c0af4214cf5140fbf49520","observation_id":"7c1fa0b9-baf7-4667-8989-e47d1af18ac0","resolution":{"observed_at":"2026-05-12T23:21:56.675106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"6-PACK: Category-level 6D pose tracker with anchor-based keypoints","venue":null,"work_id":"250283c5-2812-406a-809d-56dc21221edd","year":null},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:c178ee75d634514dc9976111d5b2e390d4472e8cb40af0ca6738b353dfb75cc0","observation_id":"471daf49-cf25-45e7-8811-0ecf55778859","resolution":{"observed_at":"2026-05-12T23:21:56.695224Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"0945.2020","doi":"10.1109/icra40945.2020","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Monocular visual-inertial odometry in low-textured environments with smooth gradients: A fully dense direct ﬁltering approach","venue":null,"work_id":"2965aef7-d5f1-495c-86c3-83cc126c23dc","year":2020},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:8aed65fe3e28abbd9ea816bf486a889e5d5a70b35e75e49483b5504623000810","observation_id":"462209a2-a626-47f3-8583-c8d447becb05","resolution":{"observed_at":"2026-05-12T02:21:15.672778Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Tracking everything everywhere all at once","venue":null,"work_id":"7b21873a-0e74-4859-b401-42d72a551b4f","year":2023},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:1c84e29d5f168eeea6af590705b78d96bc9f7c8e46f4a48f4f60f1f6da831325","observation_id":"e7165f81-1cdb-484b-aed7-2b20a0ff576c","resolution":{"observed_at":"2026-05-12T23:21:56.666960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Integrating physics and topology in neural networks for learning rigid body dynamics.Nature Communications, 16(1):6867","venue":null,"work_id":"ba86be98-635a-43cd-8964-7ba94d739d92","year":2025},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:e519b0b10699454065fa8048cd7eeaab6e14677db3d595c5b1ef2acfb5b68e69","observation_id":"dcb77510-77e9-4bf3-b682-24dea48b0f6a","resolution":{"observed_at":"2026-05-12T23:21:56.659532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.05359","last_updated":"2023-12-08T20:45:34Z","snapshot_observed_at":"2026-08-04T13:14:18.450215Z","submitted_at":"2023-12-08T20:45:34Z","title":"Learning 3D Particle-based Simulators from RGB-D Videos","version":1},"cited_work":{"arxiv_id":"2312.05359","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2312.05359","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning 3d particle-based simulators from rgb-d videos","venue":null,"work_id":"0699f4a3-43ed-473b-a6bc-008a0c9f284d","year":2023},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"cited_paper":"/paper/2312.05359","citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:02e9a089c0fe0a1b97b9c02a1a103a9a5f2a92c2a52af4ece51034faf5740fde","observation_id":"0a29f1a9-549f-4b09-9be5-d96fd1664cc2","resolution":{"observed_at":"2026-05-12T02:21:16.928032Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.19800","last_updated":"2024-06-28T10:13:50Z","snapshot_observed_at":"2026-07-06T18:38:26.046574Z","submitted_at":"2024-06-28T10:13:50Z","title":"Modeling the Real World with High-Density Visual Particle Dynamics","version":1},"cited_work":{"arxiv_id":"2406.19800","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.19800","snapshot_observed_at":"2026-07-02T18:57:16.321696Z","title":"Modeling the real world with high-density visual particle dynamics","venue":null,"work_id":"8f709173-e642-41a0-97d3-6f9e577be617","year":2024},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"cited_paper":"/paper/2406.19800","citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:73b852296eb91f0ef28e62f17dc73525c735a84f3668db0765874afdbf96fd54","observation_id":"a062e1e4-9456-4854-8c87-812235f814de","resolution":{"observed_at":"2026-05-12T02:21:16.943406Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Pointflow: 3d point cloud generation with continuous normalizing flows","venue":null,"work_id":"4d0ea1ca-35f4-46bd-ba42-309ba309a8ad","year":2019},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:419d12c2a39603f0a1c485f325dba35bd43400295bf3822a3fdba8c729421646","observation_id":"57c6731a-ff98-49b2-9325-f7e803c3dd38","resolution":{"observed_at":"2026-05-12T23:21:56.628946Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.12467","last_updated":"2024-03-26T01:50:54Z","snapshot_observed_at":"2026-07-06T17:05:31.586924Z","submitted_at":"2023-12-19T05:30:08Z","title":"Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer","version":3},"cited_work":{"arxiv_id":"2312.12467","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2312.12467","snapshot_observed_at":"2026-06-29T00:02:50.452349Z","title":"Learning flexible body collision dynamics with hierarchical contact mesh transformer","venue":null,"work_id":"d33a3ec4-4c3c-4d9f-ab2c-4362c9ff17f3","year":2023},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"cited_paper":"/paper/2312.12467","citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:f21e8a689e9fec5cb7ab9a5fb46c5f66b31956290dfdeab4919a218942c1996f","observation_id":"612a8e7e-538d-47c9-bfa5-ab86cd078522","resolution":{"observed_at":"2026-05-12T02:21:16.920889Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Egode: An event-attended graph ode framework for modeling rigid dynamics.Advances in Neural Information Processing Systems, 37:59093–59118","venue":null,"work_id":"f185ab52-f025-4fec-adc4-f6ac93f872f7","year":2024},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:2b5d6eb718800c86c59161bbed74edb6f61b04153cac0c841e36e203dbb2a241","observation_id":"7f10c502-d77a-478b-9c15-ca8310a2f192","resolution":{"observed_at":"2026-05-12T23:21:56.570919Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Renderformer: Transformer- based neural rendering of triangle meshes with global illumination","venue":null,"work_id":"51135cf9-f97b-4913-a68b-6ca2e8da7ae1","year":2025},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:5f8316f68cdc4e2049045740a1eb9ba42dc255f342134d7e7bcec43c7b71e76a","observation_id":"c5161cb2-a977-451c-9997-e49ed60d8688","resolution":{"observed_at":"2026-05-12T23:21:56.587041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.03825","last_updated":"2025-05-08T08:32:16Z","snapshot_observed_at":"2026-07-06T19:28:08.374354Z","submitted_at":"2024-10-04T18:00:07Z","title":"MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion","version":2},"cited_work":{"arxiv_id":"2410.03825","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.03825","snapshot_observed_at":"2026-07-04T21:00:08.933119Z","title":"MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion","venue":"cs.CV","work_id":"eb8020a2-8be2-40c3-92cc-0feca979f95e","year":2024},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"cited_paper":"/paper/2410.03825","citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:5693f953274ccfdbb85f3bd39f58cc1fb1530fc39b3123bb3b626346ab061aa7","observation_id":"5dd94847-27cc-47b9-bb84-beb7d937df20","resolution":{"observed_at":"2026-05-15T14:41:13.559959Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-07-05T06:20:44.971576Z","title":"Point transformer","venue":null,"work_id":"5b19eeed-dfad-4a6b-9f18-bd20311a470f","year":2021},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:1ce307e20ca72c2098183fb2d0f2e39e6a77f33ac68da15965cdce85bdcda5ca","observation_id":"b4264813-e345-4091-af41-a92f29862a8d","resolution":{"observed_at":"2026-05-12T23:21:56.682118Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.20995","last_updated":"2025-04-29T17:59:30Z","snapshot_observed_at":"2026-07-06T21:16:36.894196Z","submitted_at":"2025-04-29T17:59:30Z","title":"TesserAct: Learning 4D Embodied World Models","version":1},"cited_work":{"arxiv_id":"2504.20995","doi":"10.48550/arxiv.2504.20995","metadata_source":"pith","pith_arxiv_id":"2504.20995","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"arXiv preprint arXiv:2504.20995 (2025)","venue":"cs.CV","work_id":"3a971c23-d9fe-4291-9743-6a2b015ce29b","year":2025},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"cited_paper":"/paper/2504.20995","citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:0af4ed4f56642f59ebe4cc36757a52237fba1dea4bd81bef725ddaef0281cd43","observation_id":"05f28094-eb7d-4135-a90a-2f9dc5729c1b","resolution":{"observed_at":"2026-05-12T02:21:16.932112Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Extending lagrangian and hamiltonian neural networks with differentiable contact models.Advances in Neural Information Processing Systems, 34:21910–21922","venue":null,"work_id":"90a32800-8d59-4075-ae26-617ea56766bd","year":2021},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:64cfd624d589f57a7a583b72477a61fad66726611f8cbfc00745f6acb7651033","observation_id":"2fdcaed7-d187-44c0-b624-b7694e7ed7a2","resolution":{"observed_at":"2026-05-12T23:21:56.678788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"the decoder object tokens","venue":null,"work_id":"a1a52fbc-d1bc-46e6-af87-23ae174ec11c","year":2019},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:4d7f9dd005330fb1763ed633f3e24217b3f4a090782a8c488decba4488413512","observation_id":"822af38c-e5fe-4932-adf8-782f493e8a66","resolution":{"observed_at":"2026-05-12T23:21:56.619110Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"988191f1-d252-4bff-8c74-6659aa4ab113","year":null},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:348195b304b651f9c8755cc9935249e06d1239f4adb0af2a8afaf84a7e7886d0","observation_id":"9cf939fa-8a27-4f90-bb49-c56850c43c62","resolution":{"observed_at":"2026-05-12T23:21:56.651995Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Meshes exceeding vertex limits undergo quadric decimation","venue":null,"work_id":"f2e660d7-1943-4cf7-8863-5249ac9b183c","year":null},"citing_paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-12T02:18:19.764706Z"},"links":{"citing_paper":"/paper/2605.09196"},"observation_digest":"sha256:b072a3e4ab903b943d9706a674e8d900a663a7ae179252f02ba580b947bee819","observation_id":"21c3ded2-3e8d-47d3-a34f-d27e4bb2e163","resolution":{"observed_at":"2026-05-12T23:21:56.601784Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.09196","last_updated":"2026-05-09T22:31:09Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:31:09Z","title":"RigidFormer: Learning Rigid Dynamics using Transformers"},"reference_resolution":{"displayed":54,"state_counts":{"malformed_identifier":0,"metadata_mismatch":3,"parse_uncertain":0,"unresolved":2,"verified_exact":14,"verified_fuzzy":35},"total_outbound_references":54},"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":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 4 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2605.09196."}