{"as_of":"2026-08-09T04:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9059a8351c2d15a97faf551cbb72cef1565fb14ae9d0d8c1bd87f0547abee5d2","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T13:40:35.533993Z","state":"measured"},{"denominator":49,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":49,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T15:59:54.224722Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T15:59:55.367226Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"cited_work":{"arxiv_id":"2502.07178","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.07178","snapshot_observed_at":"2026-08-06T15:59:55.367226Z","title":"Online Aggregation of Trajectory Predictors","venue":"cs.RO","work_id":"e30df9c0-d50f-4710-81f2-68224eeec40b","year":2025},"citing_paper":{"arxiv_id":"2507.14456","last_updated":"2025-09-11T16:24:09Z","snapshot_observed_at":"2026-08-08T03:32:30.940222Z","submitted_at":"2025-07-19T03:04:28Z","title":"GEMINUS: Dual-aware Global and Scene-Adaptive Mixture-of-Experts for End-to-End Autonomous Driving","version":4},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T15:59:54.224722Z"},"links":{"cited_paper":"/paper/2502.07178","citing_paper":"/paper/2507.14456"},"observation_digest":"sha256:b75ca7047f1760203f43aba4dbb4756786c45dc3ae5e507809291cf10b40ec05","observation_id":"cbe8eceb-ab24-4b4c-a0c9-73fae59a1d91","resolution":{"observed_at":"2026-08-06T15:59:55.418060Z","resolver_source":"local_arxiv","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"}},{"citation":{"cited_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.07178","snapshot_observed_at":"2026-08-01T17:24:28.566159Z","title":"arXiv preprint arXiv:2502.07178 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26370","last_updated":"2026-07-29T01:07:28Z","snapshot_observed_at":"2026-08-07T06:07:13.986442Z","submitted_at":"2026-07-29T01:07:28Z","title":"Self-Adaptive Learning and Model Predictive Control for Tracking Unknown Dynamics with No Regret","version":1},"reference_index":208,"source":"arxiv_source","source_observed_at":"2026-08-01T17:24:28.566159Z"},"links":{"cited_paper":"/paper/2502.07178","citing_paper":"/paper/2607.26370"},"observation_digest":"sha256:c28ef2fe24ef8033939f3aff9471e8210481cf9109ef55913dd328fcac752884","observation_id":"f5d00ea1-fc7a-426d-8e95-40bc954773d4","resolution":{"observed_at":"2026-08-01T17:24:28.566159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2502.07178/citation-record","integrity":"/paper/2502.07178/integrity","json":"/paper/2502.07178/citation-record.json","paper":"/paper/2502.07178"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T13:40:36.057825Z","title":"Liability, ethics, and culture-aware behavior specification using rulebooks,","venue":null,"work_id":"5960141f-b3f9-4dfa-95c1-bfd530af93cf","year":2019},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.392753Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:ae104efa5397f11570cfc0995e8f124c05fb4d42c31649d0b54662cb7a0a5285","observation_id":"ae83ef2e-c771-433c-9d06-79bd2e1e6e04","resolution":{"observed_at":"2026-08-08T13:40:36.061157Z","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":"2026-08-08T13:40:36.047500Z","title":"Receding horizon planning with rule hierarchies for autonomous vehicles,","venue":null,"work_id":"34b1f90d-9ce2-46bb-b3e9-1108e2181c3b","year":2023},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.396731Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:26b5a8c76027ab7201cd5bb1674394b478eed7886a3b74f5ce9bc429017f6070","observation_id":"94f53e0b-c0a2-43cf-953b-8d3df296a824","resolution":{"observed_at":"2026-08-08T13:40:36.050919Z","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":"2026-08-08T13:40:36.036662Z","title":"The reasonable crowd: Towards evidence-based and interpretable models of driving behavior,","venue":null,"work_id":"df6ef514-b1b0-4b7b-b585-050654baffab","year":2021},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.400175Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:90a037b8b27ae2673170f7c90b5f17adf1eb56d866765f2b27b3c732dd359154","observation_id":"97e5c23c-a58d-4ab5-9f1b-918529399bb9","resolution":{"observed_at":"2026-08-08T13:40:36.040354Z","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":"2026-08-08T13:40:36.025475Z","title":"Tra- jectron++: Dynamically-feasible trajectory forecasting with hetero- geneous data,","venue":null,"work_id":"5e13e788-82da-4089-9bb2-61a92b799cda","year":2020},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.403707Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:722974d3782930fc4aaebe30ab5b94adbd6f99e00dbd72e0887258677936ad07","observation_id":"c05cfc94-a582-4b54-8e24-9a7c3798a066","resolution":{"observed_at":"2026-08-08T13:40:36.028978Z","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":"2026-08-08T13:40:36.015078Z","title":"Scept: Scene-consistent, policy- based trajectory predictions for planning,","venue":null,"work_id":"74a38230-921e-4c79-b01f-2d970604e886","year":2022},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.406921Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:e347a2f483a0e524c5fe41a2bac51e55ea77e099f003bf17fe10204c1229c54d","observation_id":"37257434-40c6-437b-a28e-67e31af55f4f","resolution":{"observed_at":"2026-08-08T13:40:36.018538Z","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":"2026-08-08T13:40:36.004045Z","title":"Agentformer: Agent- aware transformers for socio-temporal multi-agent forecasting,","venue":null,"work_id":"f1fa4385-a1f2-484a-b009-a77c942da6da","year":2021},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.410143Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:470b1ac25f5c7d692705822f7b39891d8c1c5cafc3ab56783afab47007c7444e","observation_id":"832cf53e-808f-475b-9da4-8058f9a08294","resolution":{"observed_at":"2026-08-08T13:40:36.007968Z","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":"2108.07258","last_updated":"2022-07-12T23:45:14Z","snapshot_observed_at":"2026-08-02T09:20:40.804790Z","submitted_at":"2021-08-16T17:50:08Z","title":"On the Opportunities and Risks of Foundation Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.07258","snapshot_observed_at":"2026-08-08T13:40:35.413576Z","title":"On the opportunities and risks of foundation models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.413576Z"},"links":{"cited_paper":"/paper/2108.07258","citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:9e64a4a5add681606383d6636042bc64b703236e79f215d7c94ac206ca875fde","observation_id":"a14a4a1a-7e0f-4905-8f2f-8515a5f50e67","resolution":{"observed_at":"2026-08-08T13:40:35.413576Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11698","last_updated":"2024-02-26T20:41:01Z","snapshot_observed_at":"2026-08-07T02:34:39.980213Z","submitted_at":"2023-06-20T17:24:23Z","title":"DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11698","snapshot_observed_at":"2026-08-08T13:40:35.417043Z","title":"Decodingtrust: A compre- hensive assessment of trustworthiness in gpt models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.417043Z"},"links":{"cited_paper":"/paper/2306.11698","citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:6bf7f6da9bcbb029aa00a30255c3faf96335e61f7f055520cdcc21bfc4d43d14","observation_id":"d7cc4c40-8f05-42eb-87e2-f8bab05a5a8d","resolution":{"observed_at":"2026-08-08T13:40:35.417043Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.11096","last_updated":"2023-11-18T14:52:10Z","snapshot_observed_at":"2026-08-02T15:07:26.038740Z","submitted_at":"2023-11-18T14:52:10Z","title":"On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":"2311.11096","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.11096","snapshot_observed_at":"2026-08-08T13:40:35.666622Z","title":"On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation","venue":"eess.IV","work_id":"fc92392e-3124-4eb7-85aa-f1440729491f","year":2023},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.420572Z"},"links":{"cited_paper":"/paper/2311.11096","citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:b51be75a398f3641cf65f52d8e16f0d425f3494ed12492d5bbbdbc723bf100ac","observation_id":"fa64e1fd-3cd2-4ad5-8105-b1c86b9b06d3","resolution":{"observed_at":"2026-08-08T13:40:35.670639Z","resolver_source":"local_arxiv","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"}},{"citation":{"cited_paper":{"arxiv_id":"2402.00253","last_updated":"2024-05-06T01:10:01Z","snapshot_observed_at":"2026-07-06T17:23:26.913214Z","submitted_at":"2024-02-01T00:33:21Z","title":"A Survey on Hallucination in Large Vision-Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.00253","snapshot_observed_at":"2026-08-08T13:40:35.423937Z","title":"A survey on hallucination in large vision-language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.423937Z"},"links":{"cited_paper":"/paper/2402.00253","citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:99e2ac7a80e39e955b0dbade8a81675ecd8067d21e529323011874b459f72b5f","observation_id":"db2fbcbe-f7e7-43a9-96cf-3a055330a8e1","resolution":{"observed_at":"2026-08-08T13:40:35.423937Z","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":"2026-08-08T13:40:35.993150Z","title":"A brief introduction to boosting,","venue":null,"work_id":"515d6a4a-b94d-42f5-aa4d-80f22680ba20","year":1999},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.427158Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:7da5138064b265c244cb498c91c555139441dcca2360b4f51e78a6d4e38d4ef3","observation_id":"0ce9a9dd-315d-4982-81be-aa2818eaf642","resolution":{"observed_at":"2026-08-08T13:40:35.996285Z","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":"2026-08-08T13:40:35.982531Z","title":"An introduction to boosting and leveraging,","venue":null,"work_id":"aca1c316-7e45-4072-a159-398d9564ab3d","year":2002},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.430359Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:0e2131f2ce91960582aba9f5c1d5e5779f7ee4acf69fd05de3aa293eb25aa9b3","observation_id":"088a4f35-ce66-4f92-a9c4-abe8220f32b2","resolution":{"observed_at":"2026-08-08T13:40:35.985985Z","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":"1912.13213","last_updated":"2026-06-21T08:13:27Z","snapshot_observed_at":"2026-07-06T08:47:45.187408Z","submitted_at":"2019-12-31T08:16:31Z","title":"Online Learning: A Modern Introduction Using Convex Optimization","version":10},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.13213","snapshot_observed_at":"2026-08-08T13:40:35.433369Z","title":"A modern introduction to online learning,","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.433369Z"},"links":{"cited_paper":"/paper/1912.13213","citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:1704f41f61a4727c925b3d99fdf5489a9531f4081d7fb3404f4a3688bbee1e11","observation_id":"b37f2820-1697-4c4f-ae1d-3d279953f1c1","resolution":{"observed_at":"2026-08-08T13:40:35.433369Z","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":"2026-08-08T13:40:35.971010Z","title":"Improving adaptive online learning using refined discretization,","venue":null,"work_id":"3cee02f7-9c9a-4a77-8346-584839fb7551","year":2024},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.436689Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:4604dff4096756a98751a5abaa14437a10f73672e6ea2a4db150ee288786fde5","observation_id":"e65b1143-2e3e-42f9-ba4c-156b33540ab3","resolution":{"observed_at":"2026-08-08T13:40:35.974909Z","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":"2026-08-08T13:40:35.959676Z","title":"Second-order quantile methods for experts and combinatorial games,","venue":null,"work_id":"cdba5e9a-1820-4470-b0f1-03c1b38afab6","year":2015},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.439575Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:1cca8e64277f537d10ed666bf66b47dd843ff7b2991e24bb6d80b2ba4b635271","observation_id":"3915bafc-c10b-48f6-9624-a474e977fe43","resolution":{"observed_at":"2026-08-08T13:40:35.963611Z","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":"1902.09013","last_updated":"2019-02-24T20:30:59Z","snapshot_observed_at":"2026-07-06T07:35:02.046018Z","submitted_at":"2019-02-24T20:30:59Z","title":"Artificial Constraints and Lipschitz Hints for Unconstrained Online Learning","version":1},"cited_work":{"arxiv_id":"1902.09013","doi":null,"metadata_source":"pith","pith_arxiv_id":"1902.09013","snapshot_observed_at":"2026-08-08T13:40:35.632938Z","title":"Artificial Constraints and Lipschitz Hints for Unconstrained Online Learning","venue":"stat.ML","work_id":"9eeda91a-82be-4f95-8a7d-16bffa052baa","year":2019},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.442529Z"},"links":{"cited_paper":"/paper/1902.09013","citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:23de303c85fb7660d322453a0d4cd0c56712239b5ac0171851a4dda9f6f29b50","observation_id":"37cdd8c8-e2c3-4e00-a6fb-c0c5015b0774","resolution":{"observed_at":"2026-08-08T13:40:35.636280Z","resolver_source":"local_arxiv","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"}},{"citation":{"cited_paper":{"arxiv_id":"1903.08850","last_updated":"2019-04-29T07:56:18Z","snapshot_observed_at":"2026-07-06T07:40:42.809424Z","submitted_at":"2019-03-21T07:05:44Z","title":"Stochastic Optimization of Sorting Networks via Continuous Relaxations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.08850","snapshot_observed_at":"2026-08-08T13:40:35.445934Z","title":"Stochastic optimiza- tion of sorting networks via continuous relaxations,","venue":null,"work_id":null,"year":1903},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.445934Z"},"links":{"cited_paper":"/paper/1903.08850","citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:a9dcf2cfbae5bed2fbea838ed1b403b4efca7db31edc8f2bb562ab9852be9211","observation_id":"8438e24d-9345-4b59-b4f1-2aff8627836b","resolution":{"observed_at":"2026-08-08T13:40:35.445934Z","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":"2026-08-08T13:40:35.949009Z","title":"Discounted adaptive online learning: Towards better regularization,","venue":null,"work_id":"da95b330-c880-40df-8a62-a3a28c823823","year":2024},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.449084Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:30e1658834e5041a10cde89beb6ea2d07b3ddee567b6a6f6ddab8628b508532a","observation_id":"1142cc37-b1c2-4738-aef8-80c0d6fbc66b","resolution":{"observed_at":"2026-08-08T13:40:35.952468Z","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":"2026-08-08T13:40:35.451859Z","title":"nuscenes: A multimodal dataset for autonomous driving,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.451859Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:0f769d217147bf750f5e0898efed3ecac6ae860a1a0c6eed30182917e26646fa","observation_id":"3a1da4b1-edf5-47d7-8a83-01622d3c0904","resolution":{"observed_at":"2026-08-08T13:40:35.451859Z","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":"2026-08-08T13:40:35.930017Z","title":"Multi-predictor fusion: Combin- ing learning-based and rule-based trajectory predictors,","venue":null,"work_id":"ec847006-0be6-4450-b832-029a2fea5ffe","year":2023},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.454579Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:c974e40b74ab8b3729134bd81d3ceaff077522544dd797ccf68e02166316d5b0","observation_id":"ad2f14d0-6ddc-417b-b9aa-c408adf987e6","resolution":{"observed_at":"2026-08-08T13:40:35.933703Z","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":"2026-08-08T13:40:35.457539Z","title":"One thousand and one hours: Self-driving motion prediction dataset,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.457539Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:8f50377a0aacb6cd1571a181f8b19d0cc39d9a62d955d76e2626b12a12d1a163","observation_id":"649be568-1ec5-4b05-9b8b-0ef503cb21ba","resolution":{"observed_at":"2026-08-08T13:40:35.457539Z","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":"2026-08-08T13:40:35.913139Z","title":"Vehicle trajec- tory prediction based on motion model and maneuver recognition,","venue":null,"work_id":"0ed918ce-063f-43af-94f5-a49e17835d34","year":2013},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.460276Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:56ac10ce1700d46dce95338f2c4630cf4f8d748a4a17b7defa7f7ae7609b2284","observation_id":"a07ce929-1edf-4d3e-8bf6-44f5a3f09cf4","resolution":{"observed_at":"2026-08-08T13:40:35.916398Z","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":"2026-08-08T13:40:35.903148Z","title":"Social lstm: Human trajectory prediction in crowded spaces,","venue":null,"work_id":"bb3f7429-7359-4690-aa9b-94c222de93b6","year":2016},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.462913Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:61c701ced3941225494446e456486d4ee24e27f543acca2cc87d182b6053eb10","observation_id":"3029b934-822c-4026-a9f7-4714b43d4fc4","resolution":{"observed_at":"2026-08-08T13:40:35.906501Z","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":"2026-08-08T13:40:35.892932Z","title":"Pre- dictionnet: Real-time joint probabilistic traffic prediction for planning, control, and simulation,","venue":null,"work_id":"35dff052-ee03-4aaf-b0f2-639d56eeb3fb","year":2022},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.465423Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:60eb721ae768bede2ceef80aff72952cbe457121e3d99b7a3784174880f9b6f6","observation_id":"9d22c316-76f4-46be-ae23-a2dc9575b608","resolution":{"observed_at":"2026-08-08T13:40:35.896677Z","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":"2026-08-08T13:40:35.883387Z","title":"Social GAN: Socially acceptable trajectories with generative adversarial networks,","venue":null,"work_id":"2e95c463-94ee-4062-ab60-9dad2658253c","year":2018},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.468396Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:b5570cd326a780d2432a374e534987b451b38dab01d6bc3b463203577209fb8b","observation_id":"f77f1de0-6ad6-4ba6-9e47-372db162f819","resolution":{"observed_at":"2026-08-08T13:40:35.886779Z","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":"2403.18447","last_updated":"2024-03-27T11:06:44Z","snapshot_observed_at":"2026-07-06T17:51:52.326981Z","submitted_at":"2024-03-27T11:06:44Z","title":"Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction","version":1},"cited_work":{"arxiv_id":"2403.18447","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.18447","snapshot_observed_at":"2026-08-08T13:40:35.605410Z","title":"Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction","venue":"cs.CL","work_id":"e8f4d5c8-99fd-43fb-ad17-36486aaecfa9","year":2024},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.471330Z"},"links":{"cited_paper":"/paper/2403.18447","citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:b5432d2eb6f91ad0de61324e70a7aeaf1fc23d3d44cfbffb7ac502f0f70eed6d","observation_id":"be272df7-620c-43f1-aa61-9307a5e7f5b6","resolution":{"observed_at":"2026-08-08T13:40:35.610933Z","resolver_source":"local_arxiv","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T13:40:35.872834Z","title":"Learning to predict vehicle trajectories with model-based planning,","venue":null,"work_id":"84ff899b-02eb-4f1f-8ab8-aebc1362fcc1","year":2021},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.474544Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:6d92fea720e30642bd1847bc44fac8b0a87256b373bc9f095d904d6f63a6d35a","observation_id":"2ec05a19-2f47-4e98-9c5d-7842ee239c2b","resolution":{"observed_at":"2026-08-08T13:40:35.876187Z","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":"2026-08-08T13:40:35.862604Z","title":"Planning-inspired hierarchical trajectory prediction for autonomous driving,","venue":null,"work_id":"ce910f6e-8ed3-4322-85aa-4a5b7f3920f6","year":2023},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.477813Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:94187283116f729052148adc1ddffb938ac88bed93976be246183474a2479071","observation_id":"9aad4368-d3ef-4f6f-9296-50cf7d43d731","resolution":{"observed_at":"2026-08-08T13:40:35.866248Z","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":"2026-08-08T13:40:35.853349Z","title":"Differentiable logic layer for rule guided trajectory prediction,","venue":null,"work_id":"8df9def5-d303-4f42-b5cf-6e8172cab3fe","year":2020},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.480951Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:9be2a6553dead68fbae29fa54edf8279972b04a41a0ed9f51f86d6b896c78fb7","observation_id":"cba33283-ef6e-43b0-93ca-3721a6d7e778","resolution":{"observed_at":"2026-08-08T13:40:35.856583Z","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":"2026-08-08T13:40:35.843641Z","title":"On complementing end-to-end human behavior predictors with planning,","venue":null,"work_id":"dcf30ba6-a176-43b0-a1ca-9c7e3c2baebd","year":2021},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.484063Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:f4755e2983fc0fcbb54324270a31d083acccef1ab2ca4427e5dfe83a12fa5a37","observation_id":"45352c7a-b3b1-4153-b015-786701b2e401","resolution":{"observed_at":"2026-08-08T13:40:35.846768Z","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":"2405.11139","last_updated":"2024-09-16T18:44:47Z","snapshot_observed_at":"2026-08-09T01:21:00.782263Z","submitted_at":"2024-05-18T01:49:16Z","title":"RuleFuser: An Evidential Bayes Approach for Rule Injection in Imitation Learned Planners and Predictors for Robustness under Distribution Shifts","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.11139","snapshot_observed_at":"2026-08-08T13:40:35.487058Z","title":"Rulefuser: Injecting rules in evidential networks for robust out-of-distribution trajectory prediction,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.487058Z"},"links":{"cited_paper":"/paper/2405.11139","citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:0b5630b75be469ed483afc6fd6f2e42507a9da8d956f8ff0cab27c1cd274d5cf","observation_id":"593537b1-da55-4b09-85d2-5fa6e1627540","resolution":{"observed_at":"2026-08-08T13:40:35.487058Z","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":"2026-08-08T13:40:35.833539Z","title":"Online control with adversarial disturbances,","venue":null,"work_id":"a9a0eed4-4894-4a7b-b632-7c527c8eaa8e","year":2019},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.490140Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:c235d186b49b0e226f0651eec85a6b2f535a38d373ea842864922b8aceeb8f70","observation_id":"6278fdf3-8bcb-4f2a-a69e-b1383d4f4bb2","resolution":{"observed_at":"2026-08-08T13:40:35.836787Z","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":"2211.09619","last_updated":"2026-04-27T00:54:26Z","snapshot_observed_at":"2026-08-01T01:49:20.538953Z","submitted_at":"2022-11-17T16:12:45Z","title":"Introduction to Online Control","version":8},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.09619","snapshot_observed_at":"2026-08-08T13:40:35.493399Z","title":"Introduction to online nonstochastic control,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.493399Z"},"links":{"cited_paper":"/paper/2211.09619","citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:fb878334cc5ba2a4d775a7290b51f84c5f1ac42ab3444cfff84393ec59b42707","observation_id":"e4e41a03-7025-4169-b856-cf0c1d93523d","resolution":{"observed_at":"2026-08-08T13:40:35.493399Z","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":"2026-08-08T13:40:35.822973Z","title":"Fast trac: A parameter-free optimizer for lifelong reinforcement learning,","venue":null,"work_id":"8da005a4-e263-401f-a35c-4b6f33ab10ba","year":2024},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.496553Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:0f92fc3bdffd07afa037b84b3029311249626f8a6bee76dfe49eae05313bf142","observation_id":"81461d23-0ebf-483e-949a-e5ea020cd51a","resolution":{"observed_at":"2026-08-08T13:40:35.826374Z","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":"2026-08-08T13:40:35.812080Z","title":"Adaptive conformal inference under dis- tribution shift,","venue":null,"work_id":"eb772dd8-1bb6-4552-b135-baebe7c5b46b","year":2021},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.499476Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:034e15f8a324c3d4156e694a17a4a41d0a6eae3d4567d10acc2c7f022b6fbaf5","observation_id":"6098543b-2e10-41e4-a433-47d375c0ce70","resolution":{"observed_at":"2026-08-08T13:40:35.815924Z","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":"2026-08-08T13:40:35.800087Z","title":"Improved online conformal prediction via strongly adaptive online learning,","venue":null,"work_id":"2d5e7eb9-18bb-481a-ba36-5e1cdfd3149e","year":2023},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.502661Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:368b2d0672ff28cc0d51fcff5e76f4d5847fefee82294d532bb4774175c58127","observation_id":"11207458-e706-4c87-bf3e-7e7a88e75fe2","resolution":{"observed_at":"2026-08-08T13:40:35.804419Z","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":"2026-08-08T13:40:35.787597Z","title":"Wayformer: Motion forecasting via simple & efficient atten- tion networks,","venue":null,"work_id":"5e959f1a-d995-4909-891a-60301ac8035b","year":2023},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.505549Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:97f93a433ef78989fd1a59bf0a7ac0c3704f711716ca406c9ad2da688810edff","observation_id":"4355114f-6d0e-4343-a1e8-8ec199d76137","resolution":{"observed_at":"2026-08-08T13:40:35.791281Z","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":"2026-08-08T13:40:35.775714Z","title":"Mtr++: Multi-agent motion prediction with symmetric scene modeling and guided intention query- ing,","venue":null,"work_id":"4cdd89f0-0fbf-487e-95fd-b8547ebf35e9","year":2024},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.508245Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:3003ab29f02bb6a696728eebeddd4016732b195469ef95cdaed863f85a51078d","observation_id":"914b0561-f912-4093-b3a1-193b6c13d2ef","resolution":{"observed_at":"2026-08-08T13:40:35.779667Z","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":"2026-08-08T13:40:35.763402Z","title":"A decision-theoretic generalization of on-line learning and an application to boosting,","venue":null,"work_id":"08a835a1-7aea-421d-93af-56b331697cba","year":1997},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.511153Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:1cf095fe2a8de284898c00a0e9bd3ec8179f4a5324e0c3b781e7b36796c310a5","observation_id":"6d5f56f0-5b40-4f7c-bed0-4b3fefce3860","resolution":{"observed_at":"2026-08-08T13:40:35.767532Z","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":"1211.5063","last_updated":"2013-02-16T00:35:48Z","snapshot_observed_at":"2026-08-07T12:06:37.739332Z","submitted_at":"2012-11-21T15:40:11Z","title":"On the difficulty of training Recurrent Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1211.5063","snapshot_observed_at":"2026-08-08T13:40:35.514090Z","title":"Understanding the exploding gradient problem,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.514090Z"},"links":{"cited_paper":"/paper/1211.5063","citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:265a19657b364c34795275404e0521034dc65e788e114e6d7b458a37716d2ac3","observation_id":"c03e8bba-27ff-4a3d-8696-b7edd5ed74a5","resolution":{"observed_at":"2026-08-08T13:40:35.514090Z","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":"2026-08-08T13:40:35.752143Z","title":"Stochastic optimization with heavy-tailed noise via accelerated gradient clipping,","venue":null,"work_id":"93308b00-2045-4059-9d4c-c3215256b359","year":2020},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.517063Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:225644c9b4974bc2215d7535a6c20ca27f6538df7e195e3f4768a2da41bd7ff1","observation_id":"68265bd9-dbe8-49f6-a47d-8af5e59b37ea","resolution":{"observed_at":"2026-08-08T13:40:35.755727Z","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":"2026-08-08T13:40:35.741069Z","title":"Why are adaptive methods good for attention models?","venue":null,"work_id":"513b109a-6e6a-40f6-b60f-9ac23e7d29bd","year":2020},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.519867Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:9eafb44896a9a0456b4ee7a834b4408b367c4e2548321c51f3b9ff4796db680f","observation_id":"37f3b2aa-280c-41ff-8852-dfb1ca21017c","resolution":{"observed_at":"2026-08-08T13:40:35.744850Z","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":"2026-08-08T13:40:35.522465Z","title":null,"venue":null,"work_id":null,"year":1970},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.522465Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:a414570effd3990ca7782f66c9c050d9f4da8b5d7316c4b5c3d3ee4d5d08ee1c","observation_id":"17fa6e44-fd0e-459b-b4de-db202432f178","resolution":{"observed_at":"2026-08-08T13:40:35.522465Z","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":"2026-08-08T13:40:35.723118Z","title":"Softsort: A continuous relaxation for the argsort operator,","venue":null,"work_id":"6a31d085-0574-44c5-8af0-0114fd6f2719","year":2020},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.525385Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:d061e58b049d946d30632fb899f5b5f4e8fb113d9bf77882c4200d04dda38bd7","observation_id":"15e5e832-1b74-48d5-98d3-ff7c8f77af5c","resolution":{"observed_at":"2026-08-08T13:40:35.726586Z","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":"2026-08-08T13:40:35.712835Z","title":"Nuplan: A closed-loop ml- based planning benchmark for autonomous vehicles,","venue":null,"work_id":"dac695e6-9686-42da-9590-e89a1011dc1f","year":2022},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.528044Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:bb7148c6a7cf0ab641c105c4c0cde109b6d024f6a924090af8b16aed7d777bfc","observation_id":"27ecf670-571b-46aa-9dfe-c31af3c2e7b2","resolution":{"observed_at":"2026-08-08T13:40:35.716245Z","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":"2106.08417","last_updated":"2022-03-04T20:25:25Z","snapshot_observed_at":"2026-07-06T11:19:38.321360Z","submitted_at":"2021-06-15T20:20:44Z","title":"Scene Transformer: A unified architecture for predicting multiple agent trajectories","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.08417","snapshot_observed_at":"2026-08-08T13:40:35.530857Z","title":"Scene transformer: A unified architecture for predicting multiple agent tra- jectories,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.530857Z"},"links":{"cited_paper":"/paper/2106.08417","citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:f2b79054a6607d3c74a9d77eeff240a0f6ee52d9270577afdbb1c31ce7fb9b4b","observation_id":"b84459c9-ef79-4d5e-8191-f46a877b9ff4","resolution":{"observed_at":"2026-08-08T13:40:35.530857Z","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":"2026-08-08T13:40:35.702515Z","title":"Lecture 10: Exponentiated gradient descent,","venue":null,"work_id":"eecce08c-f23e-4a3b-8779-ad54ce31a324","year":2012},"citing_paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-08T13:40:35.533993Z"},"links":{"citing_paper":"/paper/2502.07178"},"observation_digest":"sha256:bd14fc2403d7af6a2fd694557b2264babe782bd45f8791dea3689d338d1f693c","observation_id":"91cbff4b-af3b-4e73-8e77-7f05d5ace787","resolution":{"observed_at":"2026-08-08T13:40:35.705971Z","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"}}],"paper":{"arxiv_id":"2502.07178","last_updated":"2025-02-11T02:01:56Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-08T13:30:45.647383Z","submitted_at":"2025-02-11T02:01:56Z","title":"Online Aggregation of Trajectory Predictors"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":3,"verified_fuzzy":32},"total_outbound_references":47},"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 9 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 2 inbound Pith citation observations for arXiv:2502.07178."}