{"as_of":"2026-08-21T01:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ef30c40a11318539082a0e96e7ff99af0534829bab3f9fd7127a357fcc4e5c54","coverage":[{"denominator":55,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":55,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T14:27:51.625205Z","state":"measured"},{"denominator":55,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":55,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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/2608.09876/citation-record","integrity":"/paper/2608.09876/integrity","json":"/paper/2608.09876/citation-record.json","paper":"/paper/2608.09876"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:27:51.423709Z","title":"Advances in neural information processing systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.423709Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:4127ffabb80262eb466128b79afaa88a3efe940228388f1d91bdcb3734340e41","observation_id":"7fe3c009-5c96-4f10-8906-85fd8dda5d75","resolution":{"observed_at":"2026-08-15T14:27:51.423709Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:27:51.428201Z","title":"International conference on machine learning , pages=","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.428201Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:e2dbbda4a8016d89825ae0f3918c4a7fd4a6b54a20fc63fb2db70f61e3d5c2d4","observation_id":"94e27941-d722-4af0-9818-02a76be18730","resolution":{"observed_at":"2026-08-15T14:27:51.428201Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.01603","last_updated":"2020-03-17T17:10:58Z","snapshot_observed_at":"2026-08-20T13:32:55.916408Z","submitted_at":"2019-12-03T18:57:16Z","title":"Dream to Control: Learning Behaviors by Latent Imagination","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.01603","snapshot_observed_at":"2026-08-15T14:27:51.431964Z","title":"arXiv preprint arXiv:1912.01603 , year=","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.431964Z"},"links":{"cited_paper":"/paper/1912.01603","citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:8a6b87e18fa4a97ca347ab692094723df81a84dca3328a9cc565e45895d06053","observation_id":"2f2855ef-c7cd-4118-8e3a-41398fb0be03","resolution":{"observed_at":"2026-08-15T14:27:51.431964Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.04104","last_updated":"2024-04-17T17:41:20Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-01-10T18:12:16Z","title":"Mastering Diverse Domains through World Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.04104","snapshot_observed_at":"2026-08-15T14:27:51.436106Z","title":"arXiv preprint arXiv:2301.04104 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.436106Z"},"links":{"cited_paper":"/paper/2301.04104","citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:8ff1549d2d4e27b698974d2ebf68e09b2021cf03833d21c4b0db15b175fd15db","observation_id":"fa88e5a1-a1f8-4459-a10c-80c87a46c50c","resolution":{"observed_at":"2026-08-15T14:27:51.436106Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2511.08544","last_updated":"2025-11-14T08:38:32Z","snapshot_observed_at":"2026-08-20T11:45:33.231931Z","submitted_at":"2025-11-11T18:21:55Z","title":"LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2511.08544","snapshot_observed_at":"2026-08-15T14:27:51.440251Z","title":"arXiv preprint arXiv:2511.08544 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.440251Z"},"links":{"cited_paper":"/paper/2511.08544","citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:90f15deb8c708048ceb5f3dcc07ebbce02017bc465886a19fad844894bee0480","observation_id":"12f4d6ce-0bc2-4de9-8720-76f133b76864","resolution":{"observed_at":"2026-08-15T14:27:51.440251Z","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":"2026-08-15T14:27:52.518376Z","title":"International Conference on Machine Learning , pages=","venue":null,"work_id":"f2b7917f-d24d-49df-a9c0-a8b6dc734547","year":2025},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.444162Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:8b4c96c8683d3c7164127819e05949026ade3b4616f213f667b1a377012a8006","observation_id":"ea577eee-c274-4129-8ea5-d0f8c6494dd2","resolution":{"observed_at":"2026-08-15T14:27:52.521958Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T14:27:52.507736Z","title":"2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=","venue":null,"work_id":"44c78ce3-3b08-4999-a274-9c585d1ce3df","year":2025},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.448073Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:faabca8e784c48ac292440080991eab1907d310401316e3c5b39f01303b290f8","observation_id":"7f35051c-3489-42ad-9138-a2b891d9e1c7","resolution":{"observed_at":"2026-08-15T14:27:52.511428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T14:27:51.451997Z","title":"arXiv preprint arXiv:2606.15768 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.451997Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:8ab6986f4a3e81b9611ed6fe95c91582cfc981c8dcfcea60c0febc0f73e6a3e5","observation_id":"77ed20f0-8307-442b-bbb9-b96143578a97","resolution":{"observed_at":"2026-08-15T14:27:51.451997Z","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":"2026-08-15T14:27:52.496956Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":"1f0f7dc8-23d7-4e31-944c-cdccdf70f562","year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.456760Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:5845f1339803f1722e222647ce83c9ca6be50424b3856aa2374aaddd472c4a98","observation_id":"d6d3aca9-f7ac-4847-b3f3-9d7db7732bb1","resolution":{"observed_at":"2026-08-15T14:27:52.500587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T14:27:51.460749Z","title":"Advances in neural information processing systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.460749Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:db7aceb6bacfeba7414a9dabebc6e0c198cba8e4b731b81cc1fb2135a03b52f3","observation_id":"6066543d-f33e-47eb-8144-e113fcd0cf9d","resolution":{"observed_at":"2026-08-15T14:27:51.460749Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.04630","last_updated":"2020-07-30T05:22:58Z","snapshot_observed_at":"2026-08-20T21:06:23.277315Z","submitted_at":"2020-03-10T10:55:25Z","title":"Lagrangian Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.04630","snapshot_observed_at":"2026-08-15T14:27:51.465198Z","title":"arXiv preprint arXiv:2003.04630 , year =","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.465198Z"},"links":{"cited_paper":"/paper/2003.04630","citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:0aca72ffaeea2ae21437a70855f3188e862b3670a64acc12f1bc78628d8e46b2","observation_id":"9e2d0cd6-8758-4182-b672-f23a10cbd00a","resolution":{"observed_at":"2026-08-15T14:27:51.465198Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.08860","last_updated":"2020-04-30T02:53:24Z","snapshot_observed_at":"2026-08-20T18:49:08.658632Z","submitted_at":"2020-02-20T16:44:10Z","title":"Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.08860","snapshot_observed_at":"2026-08-15T14:27:51.469524Z","title":"arXiv preprint arXiv:2002.08860 , year=","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.469524Z"},"links":{"cited_paper":"/paper/2002.08860","citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:eaa4a8308391d7e8584b86f0aeeaea02dcde33047f915b19d03426f4814d55ce","observation_id":"c978c644-d380-4c87-91dc-87e4623d6203","resolution":{"observed_at":"2026-08-15T14:27:51.469524Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.08024","last_updated":"2021-07-16T17:31:54Z","snapshot_observed_at":"2026-08-16T18:09:29.290884Z","submitted_at":"2021-07-16T17:31:54Z","title":"Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.08024","snapshot_observed_at":"2026-08-15T14:27:51.474379Z","title":"arXiv preprint arXiv:2107.08024 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.474379Z"},"links":{"cited_paper":"/paper/2107.08024","citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:fc083ed0abbeb0a853e3b3d5c3568e76f887a04dba66ae9eff47d9d4ad0460be","observation_id":"4f6cf274-50c3-4fbf-8202-9258cef671e0","resolution":{"observed_at":"2026-08-15T14:27:51.474379Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2606.23444","last_updated":"2026-06-23T04:50:32Z","snapshot_observed_at":"2026-08-02T06:26:16.707206Z","submitted_at":"2026-06-22T15:00:59Z","title":"SkyJEPA: Learning Long-Horizon World Models for Zero-Shot Sim-to-Real Control of Quadrotors","version":2},"cited_work":{"arxiv_id":"2606.23444","doi":null,"metadata_source":"pith","pith_arxiv_id":"2606.23444","snapshot_observed_at":"2026-08-15T14:27:52.088167Z","title":"SkyJEPA: Learning Long-Horizon World Models for Zero-Shot Sim-to-Real Control of Quadrotors","venue":"cs.RO","work_id":"658f0abc-4314-4404-af70-4d04a0682128","year":2026},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.478224Z"},"links":{"cited_paper":"/paper/2606.23444","citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:a22079f07c77b0fa0f681efaf50e4ceddf42aed1ca119b05e711de76dbe10003","observation_id":"f5cb148a-de69-451e-8331-9fd6ecef6b79","resolution":{"observed_at":"2026-08-15T14:27:52.092102Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T14:27:51.482225Z","title":"Journal of Computational physics , volume=","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.482225Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:581ed42784792cad461960211c1a904b2d559ffb2b6b8fcfae14527ca66f86c8","observation_id":"2e8745f7-f511-47c5-a6c1-baba525a0c0f","resolution":{"observed_at":"2026-08-15T14:27:51.482225Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:27:51.485974Z","title":", author=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.485974Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:f7c704786fe2e386852ccf6e41c042f0cfdc68c96b4eccd5bce707b48fad4d83","observation_id":"2980e4cd-0344-42e0-979d-7fae32149912","resolution":{"observed_at":"2026-08-15T14:27:51.485974Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:27:51.490028Z","title":"The international journal of robotics research , volume=","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.490028Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:398b5c1e4d86922953fcf1aca2f2d0b24164fe12f07c09568a24bc5e75ed1547","observation_id":"2b4b0c48-de9a-433a-8c49-840f8425a8e3","resolution":{"observed_at":"2026-08-15T14:27:51.490028Z","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":"2026-08-15T14:27:52.458431Z","title":"IEEE Transactions on Robotics , volume=","venue":null,"work_id":"fd6d8d84-1ff5-4884-b18b-e9a164b3210e","year":2020},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.494139Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:f8e2cb1e169a7c3635c42d16e6c548b97b038f62d6186eba976fb1736ea2624b","observation_id":"7210a465-c968-4bea-ab42-eb9a61faaa1f","resolution":{"observed_at":"2026-08-15T14:27:52.462520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T14:27:52.447139Z","title":"IEEE Transactions on Geoscience and Remote Sensing , volume=","venue":null,"work_id":"ce746dc3-6970-489b-a01a-6c999dac06d2","year":2020},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.497788Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:836d4858495276e8074c7984f1ced849222336a8f9df7a1ccb1c1dd2300db8e2","observation_id":"4886ecb7-adf6-4843-a4fe-3e79fec20936","resolution":{"observed_at":"2026-08-15T14:27:52.451178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T14:27:51.501348Z","title":"Computers & Geosciences , volume=","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.501348Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:f11dc282a0c5889b0c47703b408d117796b714406c2769256ad63061cb1f4e01","observation_id":"517b357c-4dab-4067-8098-a3b9a9630aca","resolution":{"observed_at":"2026-08-15T14:27:51.501348Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.00120","last_updated":"2023-03-01T15:23:49Z","snapshot_observed_at":"2026-08-18T13:06:31.011970Z","submitted_at":"2022-09-30T22:34:54Z","title":"NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.00120","snapshot_observed_at":"2026-08-15T14:27:51.504915Z","title":"arXiv preprint arXiv:2210.00120 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.504915Z"},"links":{"cited_paper":"/paper/2210.00120","citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:a46ea95725a504c06a5ac15c7b60aae78b493d0b02654b9710641fb652537cc9","observation_id":"d4834340-8778-4b0e-ab4d-34ee0c7d54f7","resolution":{"observed_at":"2026-08-15T14:27:51.504915Z","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":"2026-08-15T14:27:52.427704Z","title":"Robotics: Science and Systems , year =","venue":null,"work_id":"8ef37f76-bd0e-4201-841f-05125f863a70","year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.508756Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:15370b50e3c915a42ded525d1c2fefe5fb3fd1185e008d340650d61d15ae0cdd","observation_id":"ddf3a970-3340-4148-89d1-43af2575ea3d","resolution":{"observed_at":"2026-08-15T14:27:52.431531Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T14:27:52.416382Z","title":"IEEE Transactions on Robotics , year=","venue":null,"work_id":"2f1ad2a0-bf40-4f27-acaa-5b244d268a96","year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.512214Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:c052a632fc3468471cb1cbfe849c2759e35b96b0404bb814b272487953dfd68c","observation_id":"8b51d590-5286-400e-a373-3631cf6a2d75","resolution":{"observed_at":"2026-08-15T14:27:52.420090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2606.22143","last_updated":"2026-06-20T16:49:17Z","snapshot_observed_at":"2026-08-06T08:59:30.209123Z","submitted_at":"2026-06-20T16:49:17Z","title":"Physics-Informed Eikonal Caging for Whole-Arm Manipulation Planning","version":1},"cited_work":{"arxiv_id":"2606.22143","doi":null,"metadata_source":"pith","pith_arxiv_id":"2606.22143","snapshot_observed_at":"2026-08-15T14:27:52.062596Z","title":"Physics-Informed Eikonal Caging for Whole-Arm Manipulation Planning","venue":"cs.RO","work_id":"8d5de6b9-c303-4d03-abce-0074ebbeb0d6","year":2026},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.515608Z"},"links":{"cited_paper":"/paper/2606.22143","citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:7dc104cefb063276a0264cb6972bfad72003cdac1797a40a5523c906611e1787","observation_id":"8e1af2d9-fdde-411a-b0cd-960b298d5252","resolution":{"observed_at":"2026-08-15T14:27:52.067021Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T14:27:52.405218Z","title":"IEEE Robotics and Automation Letters , volume=","venue":null,"work_id":"7da35cdc-5d10-418f-9aae-d7e7ed2acdae","year":2021},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.520255Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:229368f4899a2c99357012d848558fd1b37277c23a4530f9866e6e67af2e85c2","observation_id":"f6f6a56c-8c70-42ce-b865-2eda6e0e63f9","resolution":{"observed_at":"2026-08-15T14:27:52.409299Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T14:27:52.393770Z","title":"IEEE Robotics and Automation Letters , volume=","venue":null,"work_id":"4bcafa80-c02a-49f9-aa5d-e9ea2c72ee95","year":2022},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.523660Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:cd09c95dfe2f64393c639967b85e964ce44cbdca6555f1478719d6b4df3cd5fb","observation_id":"35dd8f8c-30bc-436d-a88c-0539ec2f374c","resolution":{"observed_at":"2026-08-15T14:27:52.397633Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T14:27:52.383379Z","title":"2022 , doi =","venue":null,"work_id":"a0fd93ab-41d7-4470-9f86-a2c0bb43f9a2","year":2022},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.527241Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:8def2ed482a2e7a77f4397b3b1e12f56ae059d1f26fc3618c910ee5600b099fb","observation_id":"5b1fcf93-db44-4163-9902-da5c9aff633a","resolution":{"observed_at":"2026-08-15T14:27:52.386786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2012.02924","last_updated":"2021-08-10T04:45:16Z","snapshot_observed_at":"2026-08-20T04:55:23.722390Z","submitted_at":"2020-12-05T02:14:17Z","title":"iGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenes","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.02924","snapshot_observed_at":"2026-08-15T14:27:51.530626Z","title":"arXiv preprint arXiv:2012.02924 , year=","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.530626Z"},"links":{"cited_paper":"/paper/2012.02924","citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:85656348874c2c20330ce14874f7e9900bf996649d51cb2c3c933b3731973207","observation_id":"030adc8e-bc30-4566-bb39-b4fd302f0056","resolution":{"observed_at":"2026-08-15T14:27:51.530626Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.08238","last_updated":"2021-09-16T22:01:24Z","snapshot_observed_at":"2026-08-15T09:50:41.483833Z","submitted_at":"2021-09-16T22:01:24Z","title":"Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.08238","snapshot_observed_at":"2026-08-15T14:27:51.533986Z","title":"arXiv preprint arXiv:2109.08238 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.533986Z"},"links":{"cited_paper":"/paper/2109.08238","citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:d62dfaaaa1a5053a499ce1b0bd4aa4bed96f9b62ab8428bb49c674110a66e667","observation_id":"d39c982b-9001-486c-a597-508fae68f944","resolution":{"observed_at":"2026-08-15T14:27:51.533986Z","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":"2026-08-15T14:27:52.372200Z","title":"Recent Mathematical Methods in Dynamic Programming: Proceedings of the Conference held in Rome, Italy, March 26--28, 1984 , pages=","venue":null,"work_id":"27a6c41a-3ab5-4f59-8590-fba8c1daab5d","year":1984},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.537652Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:7e1f5897ca0b4200ed6c542e78d2f4fe73dd043efe2f06024d1327509e813a73","observation_id":"3294c620-a0d8-4074-8c77-3aa07e7bd6ad","resolution":{"observed_at":"2026-08-15T14:27:52.376548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T14:27:51.540990Z","title":"arXiv preprint arXiv:2512.10942 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.540990Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:bba2328e093bda98e8ab3e528cbce24de6bd58343a5637c40a8d505fe6add227","observation_id":"ed5da39d-6811-4bd1-bec6-ecc73ed03f19","resolution":{"observed_at":"2026-08-15T14:27:51.540990Z","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":"2606.16076","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:27:51.970070Z","title":"arXiv preprint arXiv:2606.16076 , year=","venue":null,"work_id":"557c54a8-86ca-4183-b1e0-5bb226032508","year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.544634Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:addbbeb155d5eff40081fb287a53fe32cd20ee14de2e5ea2a7cef56239ae7694","observation_id":"cf5b2f5b-fa32-4e5d-bff9-4e147ed9a890","resolution":{"observed_at":"2026-08-15T14:27:51.975338Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T14:27:51.548629Z","title":"Proceedings of the IEEE , volume=","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.548629Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:e7f31e6d7cfa457d1eeffd4ca109afd639e1573876c35261e5456dae134d3d3e","observation_id":"81b51305-aa5d-4577-a9fe-19580bb69f9d","resolution":{"observed_at":"2026-08-15T14:27:51.548629Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.16693","last_updated":"2025-05-03T12:21:40Z","snapshot_observed_at":"2026-08-20T12:07:29.463962Z","submitted_at":"2025-04-23T13:27:07Z","title":"PIN-WM: Learning Physics-INformed World Models for Non-Prehensile Manipulation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.16693","snapshot_observed_at":"2026-08-15T14:27:51.552081Z","title":"arXiv preprint arXiv:2504.16693 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.552081Z"},"links":{"cited_paper":"/paper/2504.16693","citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:51c622531dbccaa95af5a6206b7f9cde0090575752296662aa13996912b354aa","observation_id":"18897452-fb25-4b94-83ba-2e40c48b5c45","resolution":{"observed_at":"2026-08-15T14:27:51.552081Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:27:51.555747Z","title":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.555747Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:eda0d8885b7b5b2800e5eec01f39711511a68ab2368238155f2ee2719143a263","observation_id":"3fc8147d-2629-46a0-ba94-9f47bf2009be","resolution":{"observed_at":"2026-08-15T14:27:51.555747Z","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":"2026-08-15T14:27:52.348584Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":"59cce4aa-7206-450d-9242-bd86b5cd4948","year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.559160Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:18af65f718f5f28d1b77bd46f23bab0052e3cbc33a9b49ebbf72482f5f5c6da9","observation_id":"d2f12b44-832d-4b66-92c3-a2ca8c55b1db","resolution":{"observed_at":"2026-08-15T14:27:52.352069Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T14:27:52.337784Z","title":"IEEE Transactions on Robotics , volume=","venue":null,"work_id":"06ede0d7-866a-41da-962e-28955d7adf31","year":2011},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.562451Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:ecaee853b2fb0917e2e84a43bb0c14e0ca6e432e879bfda1572bc6868d8a1b37","observation_id":"dcd5d2e2-e6db-4e76-a069-70f674e1e49f","resolution":{"observed_at":"2026-08-15T14:27:52.341570Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T14:27:51.566160Z","title":"2, 2022-06-27 , author=","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.566160Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:662d6864424ef6693e1f83e54d019e84618ce64369d8a0b519539dfb25e87ea5","observation_id":"b834b4af-c82b-419d-8780-e9a28226e5e8","resolution":{"observed_at":"2026-08-15T14:27:51.566160Z","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":"2026-08-15T14:27:52.319530Z","title":"Aerospace Science and Technology , pages=","venue":null,"work_id":"f0966552-0874-485b-a275-4ae9961010cf","year":2025},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.569437Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:73983fffa1d932798ec29d54cf5ad6a7d5f2b94f831f30714f6ec16664c5e1f8","observation_id":"77d032fb-4f8b-4dd2-ac1a-612337781d0f","resolution":{"observed_at":"2026-08-15T14:27:52.323547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T14:27:52.308620Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":"150e886e-12fe-43cd-8328-134ab7528403","year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.572922Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:82995cd6d5b6f41df88f7a48c27bc4509eec14ea578cbb245c872c531e4b5b7e","observation_id":"a2947c98-e2be-4fed-b681-0667768815b7","resolution":{"observed_at":"2026-08-15T14:27:52.312598Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2605.18303","last_updated":"2026-05-18T12:20:54Z","snapshot_observed_at":"2026-08-16T09:34:51.576817Z","submitted_at":"2026-05-18T12:20:54Z","title":"PH-Dreamer: A Physics-Driven World Model via Port-Hamiltonian Generative Dynamics","version":1},"cited_work":{"arxiv_id":"2605.18303","doi":null,"metadata_source":"pith","pith_arxiv_id":"2605.18303","snapshot_observed_at":"2026-08-15T14:27:51.893933Z","title":"PH-Dreamer: A Physics-Driven World Model via Port-Hamiltonian Generative Dynamics","venue":"cs.LG","work_id":"49eaf0d2-241e-42b0-b29a-b1f15017eb18","year":2026},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.576521Z"},"links":{"cited_paper":"/paper/2605.18303","citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:443ba3f0c95659bd4961e9a259841a34ff4f4483204135f431a2679766b1e198","observation_id":"53077f85-d423-46a2-8a32-8d6cd7f6a332","resolution":{"observed_at":"2026-08-15T14:27:51.898809Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.12077","last_updated":"2024-03-01T04:10:17Z","snapshot_observed_at":"2026-08-13T05:43:26.424756Z","submitted_at":"2019-09-26T13:13:16Z","title":"Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.12077","snapshot_observed_at":"2026-08-15T14:27:51.580184Z","title":"arXiv preprint arXiv:1909.12077 , year=","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.580184Z"},"links":{"cited_paper":"/paper/1909.12077","citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:601e5e3bf6a992cc7d4b1d0e2ccf531a8314ef822ec87f5d2fc40824432851cf","observation_id":"76ea927d-a949-49d4-a0bd-5adb956369ee","resolution":{"observed_at":"2026-08-15T14:27:51.580184Z","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":"2026-08-15T14:27:52.297882Z","title":"7th Annual Learning for Dynamics & Control Conference, 04-06 June, 2025, Ann Arbor, Michigan, USA , pages=","venue":null,"work_id":"a42065c0-9794-4180-abce-a7f5934f7987","year":2025},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.583923Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:a256cd4a0b0f4362d0c8922b2a17712e8c87c857cf781c21f17c3fce616ec870","observation_id":"fed09167-cb75-413e-b1ef-036b4681427e","resolution":{"observed_at":"2026-08-15T14:27:52.301854Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T14:27:52.287204Z","title":"The International Journal of Robotics Research , volume=","venue":null,"work_id":"1387c76d-c97a-4ae1-9bcc-5cdeffda96a6","year":2002},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.587195Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:06b36e3792afa29806e7d44566bbc439f2b04619724eda4d6185f0c30ed65c85","observation_id":"22ee2c43-54db-42c1-b71f-07ef40547237","resolution":{"observed_at":"2026-08-15T14:27:52.290973Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T14:27:51.590620Z","title":"IEEE/ASME Transactions on Mechatronics , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.590620Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:9fd0935d627196ffef2e3fe9ad99274f4993d4a401dc0c54aa52d6238b252448","observation_id":"7440691b-3d24-4da5-9bd1-69959659a9c0","resolution":{"observed_at":"2026-08-15T14:27:51.590620Z","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":"2026-08-15T14:27:52.269738Z","title":", author=","venue":null,"work_id":"5deef63a-14a9-4bce-af24-a54bddbc9e4d","year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.593892Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:1aa399da35f037541c9933ba74f90d48f5a6845e10d411691ca5b7847321a9f8","observation_id":"dabb03dd-a52d-4ae6-9869-8c5831457292","resolution":{"observed_at":"2026-08-15T14:27:52.273700Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T14:27:52.257105Z","title":"IEEE Transactions on Robotics , volume=","venue":null,"work_id":"3cf053c0-f623-44d2-8625-281d98b437a3","year":2017},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.597534Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:00bb75b8a9d4c6f736040e99bb5a8e3a4959fcbb45557afe8a3254cbc11bbbd5","observation_id":"31d28beb-22bd-4adb-98aa-9bd96ca31fce","resolution":{"observed_at":"2026-08-15T14:27:52.261830Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T14:27:51.601227Z","title":"arXiv preprint arXiv:2602.11291 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.601227Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:7f4665eaf35f1bd2726f78f3bf852beaf516b1d5d436bed175169ab4449e57d9","observation_id":"8d9ce185-cfce-4ce2-b00a-eb2029a42e55","resolution":{"observed_at":"2026-08-15T14:27:51.601227Z","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":"2026-08-15T14:27:52.244918Z","title":"Proceedings of the AAAI Conference on Artificial Intelligence , volume=","venue":null,"work_id":"42911b45-8460-4f57-9317-3eb3e366f986","year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.604826Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:cc9c858ab7dcd46f77a6fe77e3d80af8239f2b43fc10fa4ad375a99b36379823","observation_id":"2bf8b593-a4cb-4c0f-81d8-5462a19c425f","resolution":{"observed_at":"2026-08-15T14:27:52.248623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2603.19312","last_updated":"2026-06-03T18:50:40Z","snapshot_observed_at":"2026-08-15T13:44:41.952181Z","submitted_at":"2026-03-13T19:48:14Z","title":"LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2603.19312","snapshot_observed_at":"2026-08-15T14:27:51.607958Z","title":"arXiv preprint arXiv:2603.19312 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.607958Z"},"links":{"cited_paper":"/paper/2603.19312","citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:f0decdb7f96502626e6533f6ba79993d2b9718ebbc7331849082e2558c20045a","observation_id":"210f6cba-4bfc-4d07-9c1e-34b325d6d5ef","resolution":{"observed_at":"2026-08-15T14:27:51.607958Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.04490","last_updated":"2019-07-10T02:31:51Z","snapshot_observed_at":"2026-08-20T07:42:26.694481Z","submitted_at":"2019-07-10T02:31:51Z","title":"Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.04490","snapshot_observed_at":"2026-08-15T14:27:51.611483Z","title":"arXiv preprint arXiv:1907.04490 , year=","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.611483Z"},"links":{"cited_paper":"/paper/1907.04490","citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:7648ea8cf08a75a322744a1a1438c61288cdae1b885b1dfe40bc3bd38e0595d0","observation_id":"d0115dae-6f37-4342-a4b8-a67e89054a5f","resolution":{"observed_at":"2026-08-15T14:27:51.611483Z","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":"2607.03339","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T14:27:51.793225Z","title":"arXiv preprint arXiv:2607.03339 , year=","venue":null,"work_id":"783b88c7-0f4b-4ad2-a129-60419b2208c8","year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.615007Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:8bbcf7ae23e07a9a6ea93e2d9e1b5ffebd1590e680f9fcae4cccc9fda09e15e7","observation_id":"bb3b3773-a531-4234-beaa-e349d49fae1d","resolution":{"observed_at":"2026-08-15T14:27:51.799483Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.04034","last_updated":"2021-07-08T17:59:59Z","snapshot_observed_at":"2026-08-18T15:06:14.253218Z","submitted_at":"2021-07-08T17:59:59Z","title":"RMA: Rapid Motor Adaptation for Legged Robots","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.04034","snapshot_observed_at":"2026-08-15T14:27:51.618116Z","title":"arXiv preprint arXiv:2107.04034 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.618116Z"},"links":{"cited_paper":"/paper/2107.04034","citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:c2fb575278af74fd32e9039336212ac1b4269671bd1420f500be6ab05077cd10","observation_id":"22f67bb5-9af8-4775-b43d-02775f8fd791","resolution":{"observed_at":"2026-08-15T14:27:51.618116Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2602.15922","last_updated":"2026-02-17T15:04:02Z","snapshot_observed_at":"2026-08-09T12:54:21.149243Z","submitted_at":"2026-02-17T15:04:02Z","title":"World Action Models are Zero-shot Policies","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2602.15922","snapshot_observed_at":"2026-08-15T14:27:51.621676Z","title":"arXiv preprint arXiv:2602.15922 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.621676Z"},"links":{"cited_paper":"/paper/2602.15922","citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:78024766d0bd8b83b25a72e9173447861030c3e9062ac83318bc37bd70c0f2ed","observation_id":"2c8a8230-8428-4347-b18e-f986976863e4","resolution":{"observed_at":"2026-08-15T14:27:51.621676Z","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":"2026-08-15T14:27:52.233627Z","title":"1997 , publisher=","venue":null,"work_id":"fb7374d4-3fa4-43d8-a89f-fabe1e75104a","year":1997},"citing_paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-15T14:27:51.625205Z"},"links":{"citing_paper":"/paper/2608.09876"},"observation_digest":"sha256:1e858e4fc43e766a00aef0df32fc9a49c549c96310025f0e21a8252c7c391be0","observation_id":"59790a54-14bc-4c74-8563-318fdd66338e","resolution":{"observed_at":"2026-08-15T14:27:52.237427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2608.09876","last_updated":"2026-08-10T17:31:18Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-20T23:07:02.707884Z","submitted_at":"2026-08-10T17:31:18Z","title":"Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning"},"reference_resolution":{"displayed":55,"state_counts":{"malformed_identifier":0,"metadata_mismatch":3,"parse_uncertain":0,"unresolved":29,"verified_exact":2,"verified_fuzzy":21},"total_outbound_references":55},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2608.09876."}