{"as_of":"2026-08-23T22:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1c05dca0674d3d39b8cf7de25a8ff34ac86381c4b9adfbfc6ebebf8d351d2f2e","coverage":[{"denominator":70,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":70,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T20:05:05.572103Z","state":"measured"},{"denominator":70,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":70,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+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/2511.21256/citation-record","integrity":"/paper/2511.21256/integrity","json":"/paper/2511.21256/citation-record.json","paper":"/paper/2511.21256"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T20:05:04.374574Z","title":"Deep generative modeling of lidar data","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:04.374574Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:23cce2f8a67b929a4f3619332c054e43aab7807526cab7c0f9aaee459e143956","observation_id":"868e0d8d-90d4-4f1b-b37b-d650134482ed","resolution":{"observed_at":"2026-08-03T20:05:04.374574Z","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":"2026-08-03T20:05:04.462068Z","title":"nuscenes: A multi- modal dataset for autonomous driving","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:04.462068Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:9bdaf6e5357f7ff2f3a7ecbe9e6b3bc24abb4a1ed0946280da848cb19cfb5b32","observation_id":"ca8b743b-9cd2-409c-968d-db73d54e1025","resolution":{"observed_at":"2026-08-03T20:05:04.462068Z","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":"2026-08-03T20:05:04.543719Z","title":"Diffusion forcing: Next-token prediction meets full-sequence diffu- sion.Advances in Neural Information Processing Systems, 37:24081–24125, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:04.543719Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:d3d976eafce9bab8334bd362ae132c44edda46144eb183173cf551de5d95a8e1","observation_id":"81447220-9e6e-4a4c-a210-d927280c0fa1","resolution":{"observed_at":"2026-08-03T20:05:04.543719Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.15765","last_updated":"2023-05-25T06:22:10Z","snapshot_observed_at":"2026-08-20T01:19:33.681363Z","submitted_at":"2023-05-25T06:22:10Z","title":"Language-Guided 3D Object Detection in Point Cloud for Autonomous Driving","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.15765","snapshot_observed_at":"2026-08-03T20:05:04.665585Z","title":"Language-guided 3d object detection in point cloud for autonomous driving.arXiv preprint arXiv:2305.15765, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:04.665585Z"},"links":{"cited_paper":"/paper/2305.15765","citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:6648bdeeae7f4f8ee3b632327a92727f103c15034eb795a66ec2383dcb0e7fa4","observation_id":"42014308-cfe3-42e6-bdc3-d54e827e6270","resolution":{"observed_at":"2026-08-03T20:05:04.665585Z","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":"2026-08-03T20:05:04.767515Z","title":"Transfuser: Imitation with transformer-based sensor fusion for autonomous driv- ing.IEEE transactions on pattern analysis and machine in- telligence, 45:12878–12895, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:04.767515Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:67c2ea402fbc4989f613167fb8b00a0ea864b07f0ba0b863a77f2fc4ffde29e0","observation_id":"94267b73-8720-4dc0-a6f4-3a07697722bd","resolution":{"observed_at":"2026-08-03T20:05:04.767515Z","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":"2026-08-03T20:05:04.875418Z","title":"Openscene: The largest up-to-date 3d occupancy prediction benchmark in autonomous driv- ing","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:04.875418Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:2c8c8680df218c6355ffe41e32518b80d061646e1d488ac21a2d34f2725cfd6c","observation_id":"402243c4-36c8-456b-ba35-051c052441ee","resolution":{"observed_at":"2026-08-03T20:05:04.875418Z","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":"2026-08-03T20:05:04.934062Z","title":"Streetscapes: Large-scale consistent street view generation using autore- gressive video diffusion","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:04.934062Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:b79bfa51ed96b91113a036d2352d2026a4a3961c86adbc7522465ab698244540","observation_id":"15478a01-cde0-4c62-bc1e-2152a84090ce","resolution":{"observed_at":"2026-08-03T20:05:04.934062Z","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":"2026-08-03T20:05:04.973623Z","title":"Carla: An open urban driv- ing simulator","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:04.973623Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:3081964d6836af8535c6d33e50f3cbf758b555534e1d2b1121f25ee154abeb5b","observation_id":"8fc305dc-e1e8-44a1-991a-7612cd5b259c","resolution":{"observed_at":"2026-08-03T20:05:04.973623Z","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":"2026-08-03T20:05:05.031533Z","title":"A point set generation network for 3d object reconstruction from a single image","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.031533Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:88806e516495b86e5d1741292bb395d50840259cf089f1101588f12448e1c8c6","observation_id":"345512fc-b9b5-4dc4-9a6d-dc5599f78733","resolution":{"observed_at":"2026-08-03T20:05:05.031533Z","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":"2026-08-03T20:05:05.089596Z","title":"Vision meets robotics: The KITTI dataset.Inter- national Journal of Robotics Research, 32(11):1231 – 1237,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.089596Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:a7af88bcb789cedf03cec0be74b34e44ad3d53bf409b3e6f335621ec02483f1f","observation_id":"a559e98d-6ace-4433-841e-33d99642b907","resolution":{"observed_at":"2026-08-03T20:05:05.089596Z","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":"2026-08-03T20:05:05.205934Z","title":"Generative adversarial nets.Advances in neural information processing systems, 27, 2014","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.205934Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:503350f8a651c224d27697a4a1d6fa22259dfc2ddbadddc7c28c431882f94084","observation_id":"ff46724b-8f60-4987-ba6e-2acaeb2a9081","resolution":{"observed_at":"2026-08-03T20:05:05.205934Z","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":"2026-08-03T20:05:05.292608Z","title":"Vip3d: End-to-end visual trajectory prediction via 3d agent queries","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.292608Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:36e2103b23432cd4cb2326462d929b159857b4c18721870ff6661ebb3f08c9d4","observation_id":"06122dae-db57-4f7a-9beb-67c764f5e288","resolution":{"observed_at":"2026-08-03T20:05:05.292608Z","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":"2026-08-03T20:05:05.339979Z","title":"Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.339979Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:95e33b52c288e37d3e4139059030cf2cf0fc481cb7dee9612edada009c2a9a7e","observation_id":"3fbae4c1-87ef-4fb5-90b7-f78460b21695","resolution":{"observed_at":"2026-08-03T20:05:05.339979Z","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":"2026-08-03T20:05:05.429964Z","title":"Monocular quasi-dense 3d object tracking.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45:1992–2008, 2022","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.429964Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:f23ed7eb276372e552c6943f690db25f1bb9f8dfa8b6a7f1e780706453f2b7b8","observation_id":"e0bb5db0-e33c-4813-9d50-9b9b89d92b6d","resolution":{"observed_at":"2026-08-03T20:05:05.429964Z","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":"2026-08-03T20:05:05.441381Z","title":"Rangeldm: Fast realistic lidar point cloud generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.441381Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:e3b1086ac8b352f77f962786c0dc8a0ef552cf88f36a2e998efba8aacabd6119","observation_id":"12dd720d-ce82-4cf1-8f90-be147046d97f","resolution":{"observed_at":"2026-08-03T20:05:05.441381Z","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":"2026-08-03T20:05:05.443679Z","title":"St-p3: End-to-end vision-based au- tonomous driving via spatial-temporal feature learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.443679Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:24cca4eae65f59eb54ba4ae0b3003d576880626d0bf5c3cfbde2dbad0b4b4d82","observation_id":"d9e7484a-28e3-4448-859e-151334662b61","resolution":{"observed_at":"2026-08-03T20:05:05.443679Z","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":"2026-08-03T20:05:05.446032Z","title":"Planning-oriented autonomous driving","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.446032Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:e3b6e3c661de2da43d573b7b0dded810a9888eac904d31e88ef75291476013bf","observation_id":"ac32a964-f360-4375-b742-8e60ec9aae94","resolution":{"observed_at":"2026-08-03T20:05:05.446032Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.11790","last_updated":"2022-06-16T09:15:52Z","snapshot_observed_at":"2026-08-17T05:15:58.663240Z","submitted_at":"2021-12-22T10:48:06Z","title":"BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.11790","snapshot_observed_at":"2026-08-03T20:05:05.448233Z","title":"Bevdet: High-performance multi-camera 3d object de- tection in bird-eye-view.arXiv preprint arXiv:2112.11790,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.448233Z"},"links":{"cited_paper":"/paper/2112.11790","citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:bad31ec54b808796db3633022ab67c216c14d7d5250d4ca03ed63204a8daecd5","observation_id":"2f3539b3-1751-4bfb-bfcd-a7a14e727a43","resolution":{"observed_at":"2026-08-03T20:05:05.448233Z","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":"2026-08-03T20:05:05.451136Z","title":"Bench2drive: Towards multi-ability bench- marking of closed-loop end-to-end autonomous driving.Ad- vances in Neural Information Processing Systems, 37:819– 844, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.451136Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:60fae1c08612d83feb496f1a346a9ccd198707507cdccb6203bfc356d5944fc7","observation_id":"19110f12-dd70-47b2-a824-da47d1c9758a","resolution":{"observed_at":"2026-08-03T20:05:05.451136Z","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":"2026-08-03T20:05:05.453448Z","title":"Vad: Vectorized scene representa- tion for efficient autonomous driving","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.453448Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:a360e1182e6842143ff16b98be5dd8a510cd2902c2963740da3fcc5c150eb194","observation_id":"5606a897-e9c5-434e-8682-86462a487101","resolution":{"observed_at":"2026-08-03T20:05:05.453448Z","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":"2026-08-03T20:05:05.456157Z","title":"Towards learning-based planning: 9 The nuplan benchmark for real-world autonomous driving","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.456157Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:abacaa1a453b8789518c3abbebbb4833b0cccca2dab196a8faddcee90da741ad","observation_id":"361c6741-dba7-4656-a438-b697faf27527","resolution":{"observed_at":"2026-08-03T20:05:05.456157Z","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":"2026-08-03T20:05:05.458402Z","title":"Point cloud forecasting as a proxy for 4d occupancy forecasting","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.458402Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:88463de878769614ddf3158a86213c0c8a7220ecfe13377f81b84347ec92afb0","observation_id":"f65387fd-2b68-4231-b150-c88ae01eafc3","resolution":{"observed_at":"2026-08-03T20:05:05.458402Z","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":"2026-08-03T20:05:05.460692Z","title":"Variational diffusion models.Advances in neural infor- mation processing systems, 34:21696–21707, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.460692Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:aa62e9fc29e8ce10014bbda93a015c5d32b26e1346d68140248f643636d27f2e","observation_id":"4a7823f4-f4f8-420a-bfcb-19ea75bdf567","resolution":{"observed_at":"2026-08-03T20:05:05.460692Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-08-14T23:50:45.029465Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-03T20:05:05.462997Z","title":"Auto-encoding varia- tional bayes.arXiv preprint arXiv:1312.6114, 2013","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.462997Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:f863656120f84d92562e30e9ebfb278d3991527147c0c3d5f83f922bade207ef","observation_id":"4229a6a1-3ba0-4846-b0c6-a97d8a9342cc","resolution":{"observed_at":"2026-08-03T20:05:05.462997Z","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":"2026-08-03T20:05:05.465638Z","title":"Lwsis: Lidar-guided weakly supervised instance segmentation for autonomous driving","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.465638Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:a69130c4d1053501ee27b586e6036254d7f2b97775623445f0f2d2a508ee15ab","observation_id":"92628a53-7719-429f-b730-f46c586c6f54","resolution":{"observed_at":"2026-08-03T20:05:05.465638Z","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":"2026-08-03T20:05:05.468147Z","title":"Di-v2x: Learning domain- invariant representation for vehicle-infrastructure collabora- tive 3d object detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.468147Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:f76ffa91b51bcf14bca5da6db46315333dd8b7ac05e4193a36d58db5be867406","observation_id":"45b69bdc-e087-4477-b6e1-99cf534a1880","resolution":{"observed_at":"2026-08-03T20:05:05.468147Z","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":"2026-08-03T20:05:05.470407Z","title":"Bevdepth: Acquisition of reliable depth for multi-view 3d object detec- tion","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.470407Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:dea62785a424f9a0ae5812072e94aebdd998cbdf724f6c0b687da76d4814da4b","observation_id":"551fe86a-9e23-4824-b685-46f452e01918","resolution":{"observed_at":"2026-08-03T20:05:05.470407Z","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":"2026-08-03T20:05:05.472851Z","title":"End-to-end 3d tracking with decoupled queries","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.472851Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:0ea6cd059bcaeac96c938be5e6750f183cc4f75a84e22b1e8b75ed701ddde746","observation_id":"4bddfeed-446f-463b-aef8-612ae5982342","resolution":{"observed_at":"2026-08-03T20:05:05.472851Z","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":"2026-08-03T20:05:05.475211Z","title":"Bevformer: learning bird’s-eye-view representation from lidar-camera via spatiotemporal transformers.IEEE Transactions on Pat- tern Analysis and Machine Intelligence, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.475211Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:e8b77c33f35a1f69a7f9e255362f5f58d568668286f83956e384363de462dc26","observation_id":"d13eeffb-5918-4c37-a081-b892c4876e96","resolution":{"observed_at":"2026-08-03T20:05:05.475211Z","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":"2026-08-03T20:05:05.477448Z","title":"Pnpnet: End-to-end per- ception and prediction with tracking in the loop","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.477448Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:b7e456c9bc36f406702f64973a2de513b5c544580e44746dc9897a415a18153f","observation_id":"20a80a07-bc14-458d-914d-83735b26b86a","resolution":{"observed_at":"2026-08-03T20:05:05.477448Z","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":"2026-08-03T20:05:05.479655Z","title":"Flownet3d: Learning scene flow in 3d point clouds","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.479655Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:270de7bc177f53200ffd102ed59bf67bccc27d9976c1496bf683735073ac907e","observation_id":"8df3fa78-9e15-4ece-856f-992e90853f47","resolution":{"observed_at":"2026-08-03T20:05:05.479655Z","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":"2026-08-03T20:05:05.481956Z","title":"Pcpnet: An efficient and semantic-enhanced transformer net- work for point cloud prediction.IEEE Robotics and Automa- tion Letters, 8(7):4267–4274, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.481956Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:5244fa737095501e17a5cdcd3d4ff2aca9ec176db328ad606a0b77fc399e109c","observation_id":"a13e327f-d431-4679-953d-a316b1c33649","resolution":{"observed_at":"2026-08-03T20:05:05.481956Z","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":"2026-08-03T20:05:05.484156Z","title":"Lidar- only based navigation algorithm for an autonomous agricul- tural robot.Computers and electronics in agriculture, 154: 71–79, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.484156Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:66a0a52827e82940be8c373624b40249324859c696d10920e8f24ce5f2fefd06","observation_id":"f7094db7-84e9-43c3-ba42-f0232f394960","resolution":{"observed_at":"2026-08-03T20:05:05.484156Z","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":"2026-08-03T20:05:05.486523Z","title":"Weakly supervised 3d object detection from lidar point cloud","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.486523Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:8d95b26468ec298c82a06afa5d8e33301cf18be511d2d3c09c21652288b523bd","observation_id":"1c1f69f0-ef86-41a8-9a09-ce6295681714","resolution":{"observed_at":"2026-08-03T20:05:05.486523Z","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":"2026-08-03T20:05:05.488712Z","title":"Self-supervised point cloud prediction using 3d spatio-temporal convolutional networks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.488712Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:5b6e2b6d56ea5d1dae922d3d041aa8ee319eced5d6188a6caa67279d02a59518","observation_id":"e473f216-eb9e-4837-8c2d-82023f7d784d","resolution":{"observed_at":"2026-08-03T20:05:05.488712Z","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":"2026-08-03T20:05:05.491130Z","title":"Lidar data synthe- sis with denoising diffusion probabilistic models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.491130Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:1de9f2c7fde6837bf0141c5eb26ca2408a4dc347d34c2e3ae4ef99b71f6628bb","observation_id":"6e35a260-c0e1-4a78-878c-892a39a0fcaf","resolution":{"observed_at":"2026-08-03T20:05:05.491130Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.10741","last_updated":"2022-03-08T18:18:49Z","snapshot_observed_at":"2026-08-07T12:21:17.790675Z","submitted_at":"2021-12-20T18:42:55Z","title":"GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.10741","snapshot_observed_at":"2026-08-03T20:05:05.493442Z","title":"Glide: Towards photorealistic image generation and editing with text-guided diffusion models.arXiv preprint arXiv:2112.10741, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.493442Z"},"links":{"cited_paper":"/paper/2112.10741","citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:7e10675a0be252c2870c684de885fdcc86d8cd5f305fd53809e58477695673c2","observation_id":"31698e23-df14-4f8b-a9c9-790b932e02f7","resolution":{"observed_at":"2026-08-03T20:05:05.493442Z","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":"2026-08-03T20:05:05.496014Z","title":"Improved denoising diffusion probabilistic models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.496014Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:d2c3f38d638b8a73ef6ff60daaf806cb9e8b48e6a9aab57af3d6afb91d2c12eb","observation_id":"b316ba97-857d-4861-9a01-72cc32ff2ea5","resolution":{"observed_at":"2026-08-03T20:05:05.496014Z","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":"2026-08-03T20:05:05.498464Z","title":"Atppnet: Attention based temporal point cloud prediction network","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.498464Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:7740426591714c336fabcb3597feec2d47081950a9b0e879a9acd0631b3870ad","observation_id":"97bfb14f-9476-474c-b792-3a0e887c8348","resolution":{"observed_at":"2026-08-03T20:05:05.498464Z","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":"2026-08-03T20:05:05.500661Z","title":"Simpletrack: Understanding and rethinking 3d multi-object tracking","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.500661Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:ef722f26b5a7e49fe5b4978cfdae9470f374159f5a9d66c3652ce20639b1cdbc","observation_id":"a3409a76-b771-4dcf-a242-3b738a177420","resolution":{"observed_at":"2026-08-03T20:05:05.500661Z","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":"2026-08-03T20:05:05.502932Z","title":"Multi- modal fusion transformer for end-to-end autonomous driv- ing","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.502932Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:7a8eb0f204e3bae65838f50916d1cdb46a984d5c8e60816704738d204e68b085","observation_id":"c7d7a056-6404-436d-95c7-6039d9c79c8a","resolution":{"observed_at":"2026-08-03T20:05:05.502932Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.06125","last_updated":"2022-04-13T01:10:33Z","snapshot_observed_at":"2026-08-15T12:50:58.405488Z","submitted_at":"2022-04-13T01:10:33Z","title":"Hierarchical Text-Conditional Image Generation with CLIP Latents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.06125","snapshot_observed_at":"2026-08-03T20:05:05.505523Z","title":"Hierarchical text-conditional image gener- ation with clip latents.arXiv preprint arXiv:2204.06125, 1 (2):3, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.505523Z"},"links":{"cited_paper":"/paper/2204.06125","citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:6a1f0b3a8c399b72be4be48c6ab94d083164651f654e89f49cfd7d681f753466","observation_id":"c1f68c90-41e1-4115-b939-b2f6b3b4cdc8","resolution":{"observed_at":"2026-08-03T20:05:05.505523Z","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":"2026-08-03T20:05:05.508141Z","title":"Towards realistic scene generation with lidar diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.508141Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:219082a132827cf85c0e722a815bc8522fd6a9ea8c241530b0f6ef0b781afe96","observation_id":"472595c4-72f9-4535-9326-e22b7a1a34bf","resolution":{"observed_at":"2026-08-03T20:05:05.508141Z","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":"2026-08-03T20:05:05.510566Z","title":"Drone laser scanning for modeling riverscape topography and vegetation: Comparison with traditional aerial lidar","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.510566Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:ac7e193e3dabd55bb03f8111f6e04744bf92d71985068844f15495ceef2de36c","observation_id":"264028b2-bd95-426b-a358-40af6f4dfc16","resolution":{"observed_at":"2026-08-03T20:05:05.510566Z","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":"2026-08-03T20:05:05.512845Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.512845Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:33a354b113cc0368fab2dd77d8fa103541674a515a168a8fdccf7999c6bcd544","observation_id":"ff44b4b3-c9b3-48d6-adaa-eb0602b4fe37","resolution":{"observed_at":"2026-08-03T20:05:05.512845Z","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":"2026-08-03T20:05:05.515269Z","title":"Photorealistic text-to-image diffusion models with deep language understanding.Advances in neural information processing systems, 35:36479–36494, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.515269Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:a30d46f1456eb4159be2dd2aea9fbadb56a1dcee2b89340a7f4750033488dec4","observation_id":"38fa28aa-cea8-4994-b6fe-d54a7f54e078","resolution":{"observed_at":"2026-08-03T20:05:05.515269Z","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":"2026-08-03T20:05:05.517537Z","title":"Pointr- cnn: 3d object proposal generation and detection from point cloud","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.517537Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:12bbf392d36cb1c6b44625847b2844dd541aec38c4eabd8c862b394e2ceabc2a","observation_id":"8fa969a3-4738-4447-ab59-0cc001f0fc01","resolution":{"observed_at":"2026-08-03T20:05:05.517537Z","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":"2026-08-03T20:05:05.519811Z","title":"Pv-rcnn: Point- voxel feature set abstraction for 3d object detection","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.519811Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:5dccf9103c280abc3886849d9cf7c1025e238dec5379756ce5ba6c3dde7ede83","observation_id":"f1f791fb-1fc2-46a6-b9e9-3c4dcaed0f27","resolution":{"observed_at":"2026-08-03T20:05:05.519811Z","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":"2026-08-03T20:05:05.522057Z","title":"Deep unsupervised learning using nonequilibrium thermodynamics","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.522057Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:74d7f986daedbc072f41885a673d138adb3ebf1b5d007cfd6e257983ff74c7fc","observation_id":"aff0b195-cc57-4fc1-8fa8-d3729b3afdb0","resolution":{"observed_at":"2026-08-03T20:05:05.522057Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-08-11T15:38:14.931716Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-08-03T20:05:05.524308Z","title":"Denoising diffusion implicit models.arXiv preprint arXiv:2010.02502, 2020","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.524308Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:975ff647225ab5c008a03febd00c43a29f74f06779186740097f70d76242049b","observation_id":"1f2688d0-a671-4625-ad6f-10dbe579d47f","resolution":{"observed_at":"2026-08-03T20:05:05.524308Z","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":"2026-08-03T20:05:05.526836Z","title":"Generative modeling by esti- mating gradients of the data distribution.Advances in neural information processing systems, 32, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.526836Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:9f8a922b1b9df02a0e0ba3803b38b60627981e3fdfc411a26360fe2e8cea6619","observation_id":"3aee4330-2a10-4b6e-96f6-788cbb1c9307","resolution":{"observed_at":"2026-08-03T20:05:05.526836Z","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":"2026-08-03T20:05:05.529263Z","title":"Improved techniques for training score-based generative models.Advances in neural information processing systems, 33:12438–12448, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.529263Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:165f9acfcaaca5a8d1858bba05275bd39c5eac97b7c3510b4f007cd2813e8e05","observation_id":"36785e0a-76f8-497a-9c7c-7bac6f1ab4d8","resolution":{"observed_at":"2026-08-03T20:05:05.529263Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.13456","last_updated":"2021-02-10T18:17:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-11-26T19:39:10Z","title":"Score-Based Generative Modeling through Stochastic Differential Equations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.13456","snapshot_observed_at":"2026-08-03T20:05:05.531513Z","title":"Score-based generative modeling through stochastic differential equa- tions.arXiv preprint arXiv:2011.13456, 2020","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.531513Z"},"links":{"cited_paper":"/paper/2011.13456","citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:c14a9ec931b7f5816f87ac43c2c9532344505094f5bb3bcc0ef303c968b8e09b","observation_id":"10ddf343-e414-4e9b-ada0-f1377dba9211","resolution":{"observed_at":"2026-08-03T20:05:05.531513Z","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":"2026-08-03T20:05:05.534120Z","title":"Scene as occupancy","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.534120Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:b69fe67f0e5df946cd48a5ca513b38fdf56f74e25fdf8115a343b2e07dfd5b2c","observation_id":"923e1245-5fdb-4647-b381-639c1b513cd5","resolution":{"observed_at":"2026-08-03T20:05:05.534120Z","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":"2026-08-03T20:05:05.536310Z","title":"Plant detection and mapping for agricultural robots using a 3d lidar sensor.Robotics and autonomous systems, 59(5):265–273, 2011","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.536310Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:96437e5305a9eae25f4a6d6a1b0138f09b1d407775227667893a44f2a5a308ad","observation_id":"26802a6b-cd11-4d0f-9f4a-1ad149751dac","resolution":{"observed_at":"2026-08-03T20:05:05.536310Z","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":"2026-08-03T20:05:05.538643Z","title":"Inverting the pose forecasting pipeline with spf2: Sequential pointcloud forecasting for se- quential pose forecasting","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.538643Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:bb625d6b0309721d436a17cc02d962aab2ded8239b0f1175a49012f472082549","observation_id":"0809d8e2-bc31-47a2-8c73-ce82ea7eb2f7","resolution":{"observed_at":"2026-08-03T20:05:05.538643Z","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":"2026-08-03T20:05:05.540953Z","title":"S2net: Stochastic sequential pointcloud forecasting","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.540953Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:8da5425721fe3d11e8dae17847faa543a5c991e510947ef149fb847423a93896","observation_id":"9797f69d-7780-46fb-a23f-8f93eda9bf5f","resolution":{"observed_at":"2026-08-03T20:05:05.540953Z","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":"2026-08-03T20:05:05.543244Z","title":"Para-drive: Parallelized architecture for real- time autonomous driving","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.543244Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:90ec2589e1b25f036686315f8abfc4b714e0f169f90349ec9152907207f4c1c3","observation_id":"7e6174d8-e7c4-49cd-8e1a-859e4dd3aba4","resolution":{"observed_at":"2026-08-03T20:05:05.543244Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.00493","last_updated":"2023-01-02T00:36:22Z","snapshot_observed_at":"2026-08-12T21:55:36.747659Z","submitted_at":"2023-01-02T00:36:22Z","title":"Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.00493","snapshot_observed_at":"2026-08-03T20:05:05.545532Z","title":"Argoverse 2: Next generation datasets for self-driving perception and forecasting.arXiv preprint arXiv:2301.00493, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.545532Z"},"links":{"cited_paper":"/paper/2301.00493","citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:0d7f3df22c42de5dff415bd51d884a8668563cd99db6485278b054e60beee1a8","observation_id":"5d25f9ad-1189-41a3-a381-bee385e72d4b","resolution":{"observed_at":"2026-08-03T20:05:05.545532Z","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":"2026-08-03T20:05:05.548240Z","title":"Deep 3d object detection networks using lidar data: A review.IEEE Sensors Journal, 21(2):1152–1171, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.548240Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:037add786308a8ebccaef834518e2f10ae576aac6e1cf4aa49f1663ee96bf2b5","observation_id":"608c51c0-ecb5-4add-b1ae-39d579ad6c6e","resolution":{"observed_at":"2026-08-03T20:05:05.548240Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.01448","last_updated":"2023-11-02T17:57:03Z","snapshot_observed_at":"2026-08-20T01:22:39.551002Z","submitted_at":"2023-11-02T17:57:03Z","title":"UltraLiDAR: Learning Compact Representations for LiDAR Completion and Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.01448","snapshot_observed_at":"2026-08-03T20:05:05.550571Z","title":"Ultralidar: Learning compact representations for lidar completion and generation.arXiv preprint arXiv:2311.01448, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.550571Z"},"links":{"cited_paper":"/paper/2311.01448","citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:59a02ad704cd351e171f345269c14fbded264d6d711225fe71ea4de37dc2741b","observation_id":"698a5f9c-32b1-4834-8119-8e8595f8d915","resolution":{"observed_at":"2026-08-03T20:05:05.550571Z","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":"2026-08-03T20:05:05.553062Z","title":"Second: Sparsely embed- ded convolutional detection.Sensors, 18(10), 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.553062Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:7bdf7fdff2f6f69a2d9a9b71700eea9a9c3d015891939cc122657ebc520bdbda","observation_id":"eaf80d1c-abf5-4f48-92f5-41f3815f0aea","resolution":{"observed_at":"2026-08-03T20:05:05.553062Z","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":"2026-08-03T20:05:05.555311Z","title":"Visual point cloud forecasting enables scalable autonomous driving","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.555311Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:005bdbaebd3e75be2be5c951f338d93402f786ecb75a6c33b1a3512ce7e93090","observation_id":"b0e7abed-5921-494c-a6f4-9da62e77847f","resolution":{"observed_at":"2026-08-03T20:05:05.555311Z","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":"2026-08-03T20:05:05.557732Z","title":"Is-fusion: Instance-scene collaborative fusion for multimodal 3d ob- ject detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.557732Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:8bcabb2bd3b92609d16d5a54460781bf4a85d631711821e6e8135df6896af7af","observation_id":"2903afc2-82fc-4133-9c82-cf9304c702cc","resolution":{"observed_at":"2026-08-03T20:05:05.557732Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.04627","last_updated":"2022-06-05T01:57:58Z","snapshot_observed_at":"2026-08-21T12:42:02.651994Z","submitted_at":"2021-10-09T18:36:00Z","title":"Vector-quantized Image Modeling with Improved VQGAN","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.04627","snapshot_observed_at":"2026-08-03T20:05:05.560196Z","title":"Vector-quantized image modeling with improved vqgan.arXiv preprint arXiv:2110.04627, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.560196Z"},"links":{"cited_paper":"/paper/2110.04627","citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:4dcadd5f514b9f7f0ef93c73c6fb0cf620dfc6f5aa5a9eaffb305f002d011f0f","observation_id":"d923e1a5-d440-4a00-ae5d-d8ccd3b2d969","resolution":{"observed_at":"2026-08-03T20:05:05.560196Z","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":"2026-08-03T20:05:05.562749Z","title":"Optical flow and scene flow estimation: A survey","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.562749Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:e0d0fd716d1e87bedfb64b0e9f5f4c7ec531734a5b715918334fcdc597287a80","observation_id":"d5efaa2d-431f-4c91-8ec6-521938d5ec83","resolution":{"observed_at":"2026-08-03T20:05:05.562749Z","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":"2026-08-03T20:05:05.565053Z","title":"Loam: Lidar odometry and mapping in real-time","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.565053Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:c053cec7300ea6717c3660311b571a23fa3dd6b65654e8432b41cac9b3cb5013","observation_id":"acf9e411-9811-408a-bc26-61ae7c4c38f8","resolution":{"observed_at":"2026-08-03T20:05:05.565053Z","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":"2026-08-03T20:05:05.567527Z","title":"Adding conditional control to text-to-image diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.567527Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:ead50a4248e2b7d3fbd4e12e6700913b86ac73b96e682ce6580a428978b9a65d","observation_id":"e52089a9-0e13-4554-9fab-2de1426c4b52","resolution":{"observed_at":"2026-08-03T20:05:05.567527Z","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":"2026-08-03T20:05:05.569763Z","title":"Mutr3d: A multi-camera tracking frame- work via 3d-to-2d queries","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.569763Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:1e24a2bb88fd811871742370dcf9389150ad4566adc25913de3e20eaacecc0aa","observation_id":"f7404c42-f49b-48f3-b0dc-1ae2f9709f03","resolution":{"observed_at":"2026-08-03T20:05:05.569763Z","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":"2026-08-03T20:05:05.572103Z","title":"Learning to generate realistic lidar point clouds","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-03T20:05:05.572103Z"},"links":{"citing_paper":"/paper/2511.21256"},"observation_digest":"sha256:c2901ebb15f0ef6bccea855d8a5e59c58ad09e821771531f83d39c1a3a7a4f29","observation_id":"d1e5716a-5569-4279-9682-6320ff0fa00c","resolution":{"observed_at":"2026-08-03T20:05:05.572103Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2511.21256","last_updated":"2026-06-27T08:44:30Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-15T23:23:59.636700Z","submitted_at":"2025-11-26T10:39:16Z","title":"LaGen: Towards Autoregressive LiDAR Scene Generation"},"reference_resolution":{"displayed":70,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":70,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":70},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 0 inbound Pith citation observations for arXiv:2511.21256."}