{"as_of":"2026-08-08T18:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:118cb28d7575dc2718857dc0aef35bae0b79e445d5a48a59de55385272c187bd","coverage":[{"denominator":33,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":33,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T23:47:03.627074Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2608.05774/citation-record","integrity":"/paper/2608.05774/integrity","json":"/paper/2608.05774/citation-record.json","paper":"/paper/2608.05774"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:47:04.256066Z","title":"Point2vec for self-supervised representa- tion learning on point clouds","venue":null,"work_id":"e95849a9-dd79-436e-a743-f14817faf46f","year":2023},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.448929Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:a1ebca0e03934c1665f53aeb8240c4d7a712ef9649384d024b46e1baeb29320d","observation_id":"c3f37127-811a-4418-a3ac-63c8525cabb6","resolution":{"observed_at":"2026-08-07T23:47:04.260610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:47:04.241661Z","title":"Referit3d: Neural listeners for fine-grained 3d object identification in real-world scenes","venue":null,"work_id":"275a1c7d-be05-4102-b60c-dccb60022521","year":2020},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.455946Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:ec0c9a2738c316bae11a611af18b4ca6d63f982ef2eeea4b472e696b4cb4ae4c","observation_id":"29fe8fd6-dbfa-421b-b974-d4222045f63d","resolution":{"observed_at":"2026-08-07T23:47:04.246442Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:47:04.226438Z","title":"Self-supervised learning from images with a joint-embedding predictive architecture","venue":null,"work_id":"a8b02ec4-c17e-43f8-a26e-c9fc0110b5e8","year":2023},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.461963Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:9ded368ab08d124835551c10dd11085062f09521d653720638fe4d15169d9d4a","observation_id":"868524de-30d5-4666-bd46-21f0b39aeebb","resolution":{"observed_at":"2026-08-07T23:47:04.231903Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.09985","last_updated":"2025-06-11T17:57:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-06-11T17:57:09Z","title":"V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.09985","snapshot_observed_at":"2026-08-07T23:47:03.467935Z","title":"V-jepa 2: Self-supervised video models enable understanding, prediction and planning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.467935Z"},"links":{"cited_paper":"/paper/2506.09985","citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:c7fe5e18ceadbc9922e3aee6f44b2f4914e392ddb07adf205be3bd3387e80cdb","observation_id":"c8a66b04-5d89-4855-8518-234c151800b3","resolution":{"observed_at":"2026-08-07T23:47:03.467935Z","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":"2026-08-07T23:47:04.211132Z","title":"data2vec: A general framework for self-supervised learning in speech, vision and language","venue":null,"work_id":"9eff683f-e8c9-4c0c-be60-2fb265a31e4d","year":2022},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.473563Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:02d94f56a4fcfcf0dcdc31c12b4b135a405879bc3dbbd86a101f719513802a54","observation_id":"1ebcde51-2340-4672-8992-839a283a13c5","resolution":{"observed_at":"2026-08-07T23:47:04.216037Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:47:04.196423Z","title":"Revisiting feature prediction for learning visual representations from video (v-jepa).Transactions on Machine Learning Research (TMLR), 2024","venue":null,"work_id":"1f98bb89-f42b-4d20-bbcf-f434c9c37318","year":2024},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.478952Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:655cbba49c6c73b86f24a9a64a6f02663efa883f5521980b2e2252fdc5827ad0","observation_id":"ebe60a60-94fc-4191-83cb-5a0e60fffc81","resolution":{"observed_at":"2026-08-07T23:47:04.201475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:47:04.181004Z","title":"Arkitscenes: A diverse real-world dataset for 3d indoor scene understanding using mobile rgb-d data","venue":null,"work_id":"724327a0-5138-4526-a78a-31c9059592da","year":2021},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.484806Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:8754925ea98fff3f36e81ef51ea0c06e5833fa5efa08bdf3c2f22a0e1fedae80","observation_id":"38d3db9b-9719-4aff-9650-0734e4f53120","resolution":{"observed_at":"2026-08-07T23:47:04.185907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:47:04.166398Z","title":"Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner","venue":null,"work_id":"ff80834a-7796-4a31-a81d-ffc2d9b28499","year":2017},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.490038Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:6215214ae7ccee8eba02d780b3a77d627b4add293fb309f4de9cb3b0b6126cd0","observation_id":"82597952-896d-42b3-9ea1-aabcc411277a","resolution":{"observed_at":"2026-08-07T23:47:04.171257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.00504","last_updated":"2024-03-01T13:05:38Z","snapshot_observed_at":"2026-07-06T17:38:04.972649Z","submitted_at":"2024-03-01T13:05:38Z","title":"Learning and Leveraging World Models in Visual Representation Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.00504","snapshot_observed_at":"2026-08-07T23:47:03.495059Z","title":"Learning and leveraging world models in visual representation learning.arXiv preprint arXiv:2403.00504, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.495059Z"},"links":{"cited_paper":"/paper/2403.00504","citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:850d1daa42147522b244657c8198c2bf650b713ac521c5dc4b6f041a4b051308","observation_id":"d1c13e67-c5d0-4a44-868a-e50868c7a153","resolution":{"observed_at":"2026-08-07T23:47:03.495059Z","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":"2026-08-07T23:47:04.151785Z","title":"Rel3d: A minimally contrastive benchmark for grounding spatial relations in 3d","venue":null,"work_id":"25150761-b6f9-4044-b2cb-311b747c4627","year":2020},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.500358Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:11e3e3749395f3f9bd2b5f8c994853d27b699f992cdf497ac8265d676474b871","observation_id":"5218d998-507a-4e2c-b7e5-13f2271ec6ab","resolution":{"observed_at":"2026-08-07T23:47:04.156487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:47:03.505365Z","title":"Designing and interpreting probes with control tasks","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.505365Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:f4aba5d40a4b0387a9b2a29153ca4d5b1061b953be07f390805015da53d4f1be","observation_id":"3b2d0120-c36b-43f5-b17d-fccf0e5151b5","resolution":{"observed_at":"2026-08-07T23:47:03.505365Z","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":"2026-08-07T23:47:04.128844Z","title":"Exploring data-efficient 3d scene understanding with contrastive scene contexts","venue":null,"work_id":"2a4d64d4-3a57-417e-a5ed-2d390c4ff522","year":2021},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.510745Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:1a600f19c807d4fafa8f805a330a7e2028bdabb556f62b5834f989b96433e694","observation_id":"57e62297-9673-4165-b6cf-886d8fd72fa1","resolution":{"observed_at":"2026-08-07T23:47:04.133504Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.15803","last_updated":"2024-09-24T06:53:59Z","snapshot_observed_at":"2026-07-06T19:20:59.607725Z","submitted_at":"2024-09-24T06:53:59Z","title":"3D-JEPA: A Joint Embedding Predictive Architecture for 3D Self-Supervised Representation Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.15803","snapshot_observed_at":"2026-08-07T23:47:03.516542Z","title":"3d-jepa: A joint embedding predictive architecture for 3d self-supervised representation learning.arXiv preprint arXiv:2409.15803, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.516542Z"},"links":{"cited_paper":"/paper/2409.15803","citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:aa44fff3ba3c126bbb9702ffc127428add606fd4993a03eb5e77c228df11cae4","observation_id":"9e4bf98b-a60d-4f64-bd0b-c42f945a397a","resolution":{"observed_at":"2026-08-07T23:47:03.516542Z","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":"2026-08-07T23:47:04.114278Z","title":"Self-supervised pre-training with masked shape prediction for 3d scene understanding","venue":null,"work_id":"cada9540-15fe-4f6b-89d8-215f0496a073","year":2023},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.522105Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:b731d078c8b7efc5ab37a41e036944712edb89d875c0735f1531c61cce8a1a82","observation_id":"49a2c4c0-19d3-417a-91ca-3e15badc6c44","resolution":{"observed_at":"2026-08-07T23:47:04.119570Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:47:04.100297Z","title":"A path towards autonomous machine intelligence.OpenReview preprint, 2022","venue":null,"work_id":"1e0ac0a9-16ab-4d50-a232-ffc29c992650","year":2022},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.527123Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:f943446e8e57fb2c15f5de48bd9c9d639945ca0fa4eb944a377736e68ede8700","observation_id":"43712f1c-a227-46d8-8bb2-45cdf0e92c4d","resolution":{"observed_at":"2026-08-07T23:47:04.104866Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:47:04.086088Z","title":"Sscbench: A large-scale 3d semantic scene completion benchmark for autonomous driving","venue":null,"work_id":"5ca44d6c-4497-440a-bb91-5f015287f29f","year":2024},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.533513Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:6c5f24ec8bc51566bb01db329bc14cb62164e418c082f7c1df887bed54facd8e","observation_id":"9de184af-f2ec-46f5-ab16-d47e59571bc2","resolution":{"observed_at":"2026-08-07T23:47:04.090656Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:47:04.070799Z","title":"Poma-3d: The point map way to 3d scene understanding","venue":null,"work_id":"f63eca8d-ee86-4768-9331-ac9a921ec964","year":2026},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.538640Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:2d01c3de5fe0fbd57410bbf8e5a3714014ee40884a37e1dd0bc3bdcde702c446","observation_id":"7e62f88e-2c7f-4bb3-a03c-2d5dbb71804b","resolution":{"observed_at":"2026-08-07T23:47:04.076019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:47:04.055630Z","title":"Locate 3d: Real-world object localization via self-supervised learning in 3d","venue":null,"work_id":"b5b4e84a-8957-4521-b37a-a113d0e84b3d","year":2025},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.544207Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:39437ffe3642ac9858e09627858fef3ad10172b0dd0740f9b7a7808fc0ba5548","observation_id":"d88b9ff5-898a-443b-9668-a270d814cb57","resolution":{"observed_at":"2026-08-07T23:47:04.060901Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2603.14482","last_updated":"2026-06-11T10:07:56Z","snapshot_observed_at":"2026-08-06T07:23:27.418432Z","submitted_at":"2026-03-15T17:02:40Z","title":"V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2603.14482","snapshot_observed_at":"2026-08-07T23:47:03.549339Z","title":"V-jepa 2.1: Unlocking dense features in video self-supervised learning.arXiv preprint arXiv:2603.14482, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.549339Z"},"links":{"cited_paper":"/paper/2603.14482","citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:3bd5d68179d1db441a86cf1294a621710736f9dda8dbe329d8a8292a762085b0","observation_id":"0667133c-ef74-4e63-854c-9c687844ac08","resolution":{"observed_at":"2026-08-07T23:47:03.549339Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2602.11389","last_updated":"2026-05-28T17:57:16Z","snapshot_observed_at":"2026-08-03T00:15:31.913366Z","submitted_at":"2026-02-11T21:47:26Z","title":"Causal-JEPA: Learning World Models through Object-Level Latent Masking","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2602.11389","snapshot_observed_at":"2026-08-07T23:47:03.555333Z","title":"Causal-jepa: Learning world models through object-level latent masking.arXiv preprint arXiv:2602.11389, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.555333Z"},"links":{"cited_paper":"/paper/2602.11389","citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:1869a17e31f0f06bc454ee0315db04b95aa574e0a02d07908f7dc2dfa21575c6","observation_id":"32efd2e8-614c-45a7-960d-f884fa340c48","resolution":{"observed_at":"2026-08-07T23:47:03.555333Z","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":"2026-08-07T23:47:04.040406Z","title":"Tay, Wei Liu, Yonghong Tian, and Li Yuan","venue":null,"work_id":"9389a3b7-4ead-4332-8aec-5b0dbaec084b","year":2022},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.560961Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:1474a9f266380c7795d959748c777af96852e81f7967627f60546d5e5b0145cb","observation_id":"0c078f98-8ef5-48ed-8ae2-c82a8b82a6ef","resolution":{"observed_at":"2026-08-07T23:47:04.045035Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:47:04.025383Z","title":"Point-jepa: A joint embedding predictive architecture for self-supervised learning on point cloud","venue":null,"work_id":"17511627-8f59-4e3f-9754-4ed1ea18e299","year":2025},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.566233Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:cc6f554a674bd6d6c45925543eb0fd5ca42db4c4b19b58acd33020051d275ff5","observation_id":"0934e3c5-1c60-4bb1-894d-7843b6f60101","resolution":{"observed_at":"2026-08-07T23:47:04.030235Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:47:04.010413Z","title":"Learning 3d semantic scene graphs from 3d indoor reconstructions","venue":null,"work_id":"654ff4ae-c5b7-4151-a917-0105fb217f88","year":2020},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.571384Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:22ba6155b662924bc0a5304195b720c840ddaf17434c35d245826463d4edc9b1","observation_id":"0f3f0185-e062-46d7-8a33-20e20d16eb28","resolution":{"observed_at":"2026-08-07T23:47:04.015435Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:47:03.995746Z","title":"Can transformers capture spatial relations between objects? In ICLR, 2024","venue":null,"work_id":"827912e1-ef00-48ed-9444-0c7d34181a6c","year":2024},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.577858Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:3e066978396b8cd11be8b4d52accc1540acb42b80229114b3304e0fb7756498c","observation_id":"3082db8b-83f8-4deb-8462-1e01f1ee403e","resolution":{"observed_at":"2026-08-07T23:47:04.000798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:47:03.980427Z","title":"Masked scene contrast: A scalable framework for unsupervised 3d representation learning","venue":null,"work_id":"1a802deb-eb38-4dcc-a99a-9430f4f0b4b3","year":2023},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.583014Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:6c3f3811d3d21041a5962b53a4855b66d0e1fce6d3bd43d0b220590f81b716ba","observation_id":"4389e447-d652-4b32-82eb-465d9bdfc187","resolution":{"observed_at":"2026-08-07T23:47:03.985460Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:47:03.591061Z","title":"Point transformer v3: Simpler, faster, stronger","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.591061Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:234606d9ead8a937ce4f7c43072910d482cfeaf72d2d65a4c97158ef89869761","observation_id":"b95c63f7-76e5-42e3-ae81-baa6f8a136cc","resolution":{"observed_at":"2026-08-07T23:47:03.591061Z","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":"2026-08-07T23:47:03.955668Z","title":"Sonata: Self-supervised learning of reliable point representations","venue":null,"work_id":"5158efed-6539-44b3-9085-583758ed65d0","year":2025},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.596281Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:2d5d4b28039c84dcbffa71506e99c364bb7ff6dc05b1c601d6145d698649d119","observation_id":"34b77b7e-21ed-4d69-9de1-435bec68f8cf","resolution":{"observed_at":"2026-08-07T23:47:03.960441Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:47:03.940844Z","title":"Qi, Leonidas Guibas, and Or Litany","venue":null,"work_id":"da3295a2-5632-4b56-99f6-b1f293a0fd11","year":2020},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.601707Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:4df974b255dabe242f93c8f865b4259f90e80adcb1b5b1e1a1743f5df4111627","observation_id":"9a092eb8-79d5-4934-82b5-3032ee07f90b","resolution":{"observed_at":"2026-08-07T23:47:03.945560Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:47:03.925548Z","title":"Monocular occupancy prediction for scalable indoor scenes","venue":null,"work_id":"b72899a2-6b20-4b18-bd7e-8151588aff73","year":2024},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.607166Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:258bd9686d15821bfa7bb1dd03d9f583773a486d4393e8c470d66d67945b9fb0","observation_id":"4dbdd838-d74c-4c6a-a981-5bee32a07adf","resolution":{"observed_at":"2026-08-07T23:47:03.930219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:47:03.909398Z","title":"Point-m2ae: Multi-scale masked autoencoders for hierarchical point cloud pre-training","venue":null,"work_id":"d89c00d2-5760-44c5-8b13-c56fedd46de0","year":2022},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.612436Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:2d6ec66a3101eedf6e33681fbddf75b9697b2c124460d698ed0f8cc848207a20","observation_id":"96975961-b43e-4196-884e-fac01488ae8e","resolution":{"observed_at":"2026-08-07T23:47:03.915056Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:47:03.616702Z","title":"Concerto: Joint 2d-3d self-supervised learning emerges spatial representations","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.616702Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:c803ce3513647ab5ff7723a805bf2d2c0d2ccb07048e00c652496067f6360068","observation_id":"ec619e4e-22b6-494b-8e4e-601a00fd28c8","resolution":{"observed_at":"2026-08-07T23:47:03.616702Z","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":"2026-08-07T23:47:03.620887Z","title":"Self-supervised jepa-based world models for lidar occupancy completion and forecasting.arXiv preprint arXiv:2602.12540, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.620887Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:e4a17d5230570befaa9eeb783e1a6752bb32a39a759168c935d185e89da89df8","observation_id":"1e8c0b06-0298-4f1d-8324-07dff51387d2","resolution":{"observed_at":"2026-08-07T23:47:03.620887Z","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":"5343.1319","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T23:47:03.724561Z","title":"union-hole","venue":null,"work_id":"cd688a9f-4cd4-4094-bcc3-932e6bfebafc","year":2026},"citing_paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T23:47:03.627074Z"},"links":{"citing_paper":"/paper/2608.05774"},"observation_digest":"sha256:a21719239820247279991f6db9cb528895cc3f94d04002dbf4777fd9c48b5d22","observation_id":"8d0a8f03-3d8b-4838-a10a-02af6b096134","resolution":{"observed_at":"2026-08-07T23:47:03.732772Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2608.05774","last_updated":"2026-08-06T09:09:16Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-08T18:16:02.238488Z","submitted_at":"2026-08-06T09:09:16Z","title":"SR-JEPA: Learning Predictive Latent State in 3D Scenes"},"reference_resolution":{"displayed":33,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":9,"verified_exact":1,"verified_fuzzy":23},"total_outbound_references":33},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2608.05774."}