{"as_of":"2026-08-11T21:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:da30dce0e10b2c91d935f22efc6f12cc7e1cc5fb5afd1856a981c7147b8c6cc8","coverage":[{"denominator":68,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":68,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T19:07:19.355271Z","state":"measured"},{"denominator":68,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":68,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+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/2507.06531/citation-record","integrity":"/paper/2507.06531/integrity","json":"/paper/2507.06531/citation-record.json","paper":"/paper/2507.06531"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:07:20.863499Z","title":"Learning lane graph representations for motion forecasting,","venue":null,"work_id":"6b186c97-84e3-400b-9a45-db0b4f0c255b","year":2020},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:14.965746Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:5dbd606ae5698893bb540cec4aac769f156e81c92888974327e54733ae98158e","observation_id":"34d3e888-442c-48b9-8277-156860298eff","resolution":{"observed_at":"2026-08-06T19:07:20.868339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.846215Z","title":"Deep learning-based vehicle behavior prediction for autonomous driving applications: A review,","venue":null,"work_id":"9fd3b9ce-6d28-41a5-bc4c-5d8403653af3","year":2020},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:15.070013Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:91c79685b06466bbfef4f5da1aa3365c03c2ec7427eba2d714e55c9d61e3eebf","observation_id":"97e0aa51-a639-4627-9325-bf13807249c8","resolution":{"observed_at":"2026-08-06T19:07:20.851889Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.826706Z","title":"Social predictive intelligent driver model for autonomous driving simulation,","venue":null,"work_id":"1c6c5ba9-1766-468f-8c1d-3907dbd30fbf","year":2025},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:15.194250Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:dade7caef79ed33a871d503a8eb167fca253f55cb40e3166b18ec7027238e04d","observation_id":"e03fcf0b-0582-4278-ad88-2e3c8fc1c93a","resolution":{"observed_at":"2026-08-06T19:07:20.833022Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.808380Z","title":"Covernet: Multimodal behavior prediction using trajectory sets,","venue":null,"work_id":"e7f2f454-35f7-4643-a32a-c1a760a2654e","year":2020},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:15.288032Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:96981c48d103b60a8118c985ebe5ff63de22e1b4a8eab389cb7c6f42c527cffb","observation_id":"33be869b-360b-4bcf-bef0-266d0ab131de","resolution":{"observed_at":"2026-08-06T19:07:20.814043Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.05449","last_updated":"2019-10-12T00:34:37Z","snapshot_observed_at":"2026-08-10T19:51:16.852667Z","submitted_at":"2019-10-12T00:34:37Z","title":"MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.05449","snapshot_observed_at":"2026-08-06T19:07:15.397231Z","title":"Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction,","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:15.397231Z"},"links":{"cited_paper":"/paper/1910.05449","citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:30b8aebfb908fe169ea987ef98d7336c68bb87ffb511ad80449a366d0c28bc35","observation_id":"17254ada-b175-411d-9e47-4e8ad90e3fc5","resolution":{"observed_at":"2026-08-06T19:07:15.397231Z","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":"2026-08-06T19:07:20.790845Z","title":"Rules of the road: Predicting driving behavior with a convolutional model of semantic interactions,","venue":null,"work_id":"3bf95e4b-5979-4cf0-a547-a600ea4dd8d8","year":2019},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:15.508360Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:68489c2a665b3363268f982d6671cad12d4ab3485f47f8f0001843eab7ab8afb","observation_id":"9a28e309-6215-4dc4-9a65-acd002fba895","resolution":{"observed_at":"2026-08-06T19:07:20.795757Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.772298Z","title":"Ganet: Goal area network for motion forecasting,","venue":null,"work_id":"5406d3ce-198a-4c48-a300-639f2be05190","year":2023},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:15.602883Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:596471dcd076e94f1f65955ca4354964017af597eeb83b215b57f7a290dd8fab","observation_id":"13b3c7bf-63f0-49bc-beb9-1adb7d33254b","resolution":{"observed_at":"2026-08-06T19:07:20.777395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.753328Z","title":"Lanercnn: Distributed representations for graph-centric motion forecasting,","venue":null,"work_id":"bf1c8786-42fd-488d-a4ad-f2b340d3dd7a","year":2021},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:15.700651Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:6353cb69c8dbbbd3fb4b7bfa4fe711a25f29d1840ce1664ea07c5608e3075edf","observation_id":"6d1515be-cfcd-4f29-95f0-9eb6201d40c7","resolution":{"observed_at":"2026-08-06T19:07:20.759794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.733191Z","title":"Vectornet: Encoding hd maps and agent dynamics from vectorized representation,","venue":null,"work_id":"1059bdd8-1124-494e-bcba-9ae9b86aa3f5","year":2020},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:15.795101Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:8bfdd49c4c6d58367d0462c103e2bf7819bf2fd2e6d9afbb3ede5fadf4f4e146","observation_id":"8a90eaca-2e1d-4f55-9f4c-1e6fb4e3561f","resolution":{"observed_at":"2026-08-06T19:07:20.738497Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.712213Z","title":"Tpcn: Temporal point cloud networks for motion forecasting,","venue":null,"work_id":"70eb2535-0dc0-42a2-81ee-0a08406de446","year":2021},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:15.859460Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:13fc70cc79b3a13082bf7fb708113f47228da429dec85588272a8c875f9e23dd","observation_id":"e82652f6-a020-4336-a9f3-2539e767cb57","resolution":{"observed_at":"2026-08-06T19:07:20.718198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.691104Z","title":"Convolutional neural network for tra- jectory prediction,","venue":null,"work_id":"b204e06e-0c50-42a4-9c7d-81701a2dd819","year":2018},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:15.912984Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:04b37d9c8e77ba023c2b280d0e0134f1e33467e0cd7cf1bd21bd40f4f392bf8a","observation_id":"fdb00c0a-410a-4d1a-8bdc-ec7274526a00","resolution":{"observed_at":"2026-08-06T19:07:20.696686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.668967Z","title":"Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction,","venue":null,"work_id":"91ceb416-8791-4c2f-a5eb-28c505e2828d","year":2020},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:16.005580Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:e3fd36ac463c7d6097e44b1a494f5dcb2b772363013757804eaf26d809f6dc1a","observation_id":"6f76f01f-6995-4928-be2c-b1e638bd1a6c","resolution":{"observed_at":"2026-08-06T19:07:20.676658Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.640274Z","title":"SR-LSTM: State refinement for LSTM towards pedestrian trajectory prediction,","venue":null,"work_id":"e7efb174-b8bc-4e49-8318-c7c67a9dc646","year":2019},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:16.061773Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:2dcc375c688f2435ea19a2963866ec96e4b8b5fed3a3815020b4038a7a02127c","observation_id":"30997b0c-bd25-4be0-bc79-a9041e714eb4","resolution":{"observed_at":"2026-08-06T19:07:20.649854Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.620019Z","title":"Spatio-temporal graph trans- former networks for pedestrian trajectory prediction,","venue":null,"work_id":"1e5fe7a0-1731-402b-b083-6c21b2154012","year":2020},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:16.158441Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:3dbba2bf85d28086a20ff7dbd24f1e051d27731a9648f27888cc6caf89c4c2d8","observation_id":"72550da8-ac4d-485d-a875-eb2b11bdc9ae","resolution":{"observed_at":"2026-08-06T19:07:20.625846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.595024Z","title":"TNT: Target-driven trajectory prediction,","venue":null,"work_id":"2d90f83d-bc3b-451e-b9df-343d8c7355c0","year":2021},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:16.252555Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:21bf2ed2ffb71fb4dc8f376c650f5ad047ac6c304a643d87c71d9b819c29f79f","observation_id":"c2f148e1-7fdf-4498-8c95-c56eb20e3c8a","resolution":{"observed_at":"2026-08-06T19:07:20.600973Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.572284Z","title":"Densetnt: End-to-end trajectory prediction from dense goal sets,","venue":null,"work_id":"5a112559-952d-4507-8a05-0ceb62cbf276","year":2021},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:16.305869Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:36db8d327a20fde41de59141b760bb5d34f25bc75fb99d18ccf8aaf11644d8ba","observation_id":"ab7a8e2d-d8fc-4f88-bc3a-cdc440da5ed9","resolution":{"observed_at":"2026-08-06T19:07:20.578578Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.551910Z","title":"GoHome: Graph-oriented heatmap output for future motion estimation,","venue":null,"work_id":"d5623d5c-988f-4e3d-873a-029b6091859c","year":2022},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:16.377449Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:a792000b902b5627b0b97636a4141d4740dd2c1e6ad3902acdafed4ec30b5332","observation_id":"a1ee4b71-b352-4522-9f11-adb09a972a7e","resolution":{"observed_at":"2026-08-06T19:07:20.557768Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.523254Z","title":"Wayformer: Motion forecasting via simple & efficient attention networks,","venue":null,"work_id":"57c2512f-1148-406d-95a0-6bd5d660528e","year":2023},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:16.447477Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:d5ea0087a5865c226454f4af092b721cce20f3c1e85e58f550bddea075f11553","observation_id":"2d5f2618-6e86-4abb-b94c-b587790a6e4a","resolution":{"observed_at":"2026-08-06T19:07:20.530625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.498620Z","title":"A hierarchical hybrid learning framework for multi-agent trajectory prediction,","venue":null,"work_id":"f3d159a5-425b-4c9e-9f04-53aba289d7cf","year":2024},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:16.526589Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:887386778e9673278f25ab6ec8f4fb62c86d3b6aa124372b75306634c18d5be0","observation_id":"87fa79dc-48c9-4f61-bfd7-58070a38679a","resolution":{"observed_at":"2026-08-06T19:07:20.503654Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:16.613470Z","title":"Scene trans- former: A unified architecture for predicting multiple agent trajectories,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:16.613470Z"},"links":{"cited_paper":"/paper/2106.08417","citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:3d78a41c7fb737b36eff90720333a44d8feb6f4fb7a4ee86a8ef4c7ae8893250","observation_id":"7e8e3934-febb-484f-b170-f83dc79d8dba","resolution":{"observed_at":"2026-08-06T19:07:16.613470Z","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":"2026-08-06T19:07:20.477371Z","title":"Query-centric trajectory prediction,","venue":null,"work_id":"2e0f64be-1e27-4553-8441-834884b20abf","year":2023},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:16.695156Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:b95700ca6123183d71716a64f09c5a40c9cf26a0989e4c3631a9c9ee7a5b97b0","observation_id":"f08a4667-31e9-453d-bb92-b21cbd7f1b9f","resolution":{"observed_at":"2026-08-06T19:07:20.485296Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.458188Z","title":"HPNet: Dynamic trajectory forecasting with historical prediction attention,","venue":null,"work_id":"ea5baa58-0038-4cea-af16-bf0ea890e237","year":2024},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:16.773552Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:de81d0accc616eefc0d4143f0680904267da40cd383135699648d6e2e878a9a3","observation_id":"c598b3fb-7bd2-49eb-86f2-cec7a64faae4","resolution":{"observed_at":"2026-08-06T19:07:20.464586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.10508","last_updated":"2023-06-18T09:40:40Z","snapshot_observed_at":"2026-08-11T06:59:42.690762Z","submitted_at":"2023-06-18T09:40:40Z","title":"QCNeXt: A Next-Generation Framework For Joint Multi-Agent Trajectory Prediction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.10508","snapshot_observed_at":"2026-08-06T19:07:16.865039Z","title":"QCNext: A next- generation framework for joint multi-agent trajectory prediction,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:16.865039Z"},"links":{"cited_paper":"/paper/2306.10508","citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:c352335524baeae0a2eb43693b131e00bb948cdb43ea95f7877b5c786f0aabb5","observation_id":"6a63491e-278c-4a52-97a6-f7158c89d7c6","resolution":{"observed_at":"2026-08-06T19:07:16.865039Z","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":"2026-08-06T19:07:20.432228Z","title":"DiffusionDrive: Truncated diffusion model for end-to-end autonomous driving,","venue":null,"work_id":"73eb9edd-d723-4e60-8d9e-86b5414da0d1","year":2025},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:16.953090Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:9c35f2bddf58028516347cfbc33304bb16a6be12ca86ebf3f30f7a8ebf905197","observation_id":"abe33971-0d05-4749-82e2-1d4c8929ff8c","resolution":{"observed_at":"2026-08-06T19:07:20.441348Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.406745Z","title":"Eliminating uncertainty of driver’s social preferences for lane change decision-making in realistic simulation environment,","venue":null,"work_id":"27c77d26-5544-464f-a149-fadcedf90680","year":2024},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:17.034511Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:1503226092c49973917cf02d2024dfb9dc490d1e6690812c7d7e26c0bda2fc4f","observation_id":"941f3b41-389e-4c0b-8bbc-30900c420b8e","resolution":{"observed_at":"2026-08-06T19:07:20.412548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.386582Z","title":"Hyper-relational Interaction Modeling in Multi-modal Trajectory Prediction for Intelligent Connected Vehicles in Smart Cities,","venue":null,"work_id":"d5c34db9-fb08-4d06-b795-1953cea90cac","year":2025},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:17.105962Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:25f1e19ca90cc814e331374198dfc392baef8315c3094fb97b7368777a67da80","observation_id":"250a4913-eeca-432f-a485-a4b09a4d95c5","resolution":{"observed_at":"2026-08-06T19:07:20.393872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.366220Z","title":"Probabilistic prediction of inter- active driving behavior via hierarchical inverse reinforcement learning,","venue":null,"work_id":"5aab74f5-ee10-4775-b2ca-342f817688cf","year":2018},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:17.199200Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:79da9391fdcc6b2149c86da1e44482cd0af600667f6be97665aa930d881080f8","observation_id":"899678d1-e492-49be-8842-9b648d392c89","resolution":{"observed_at":"2026-08-06T19:07:20.373257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.342101Z","title":"MTR++: Multi-agent motion prediction with symmetric scene modeling and guided intention query- ing,","venue":null,"work_id":"64d11652-f0e5-4754-a85e-87f17e0fdfd2","year":2024},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:17.306156Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:d052012823b19f773568b496c9b511c795cb729e3508813c87bc9f240a1a51aa","observation_id":"b07bf0d0-e2d8-4a3d-a899-1c5eb0248882","resolution":{"observed_at":"2026-08-06T19:07:20.347615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.04862","last_updated":"2025-04-07T09:19:20Z","snapshot_observed_at":"2026-08-07T16:08:03.615068Z","submitted_at":"2025-04-07T09:19:20Z","title":"GAMDTP: Dynamic Trajectory Prediction with Graph Attention Mamba Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.04862","snapshot_observed_at":"2026-08-06T19:07:17.368584Z","title":"GAMDTP: Dynamic trajectory prediction with graph attention Mamba network,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:17.368584Z"},"links":{"cited_paper":"/paper/2504.04862","citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:cecbcc3ffd328a942b3d55ceb63797b87e7553369e231e8bcd580a03136a89bb","observation_id":"8fad52e1-6f10-4d14-a6fa-d12fa4cb9f04","resolution":{"observed_at":"2026-08-06T19:07:17.368584Z","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":"2026-08-06T19:07:20.321117Z","title":"ProphNet: Efficient agent-centric motion forecasting with anchor-informed proposals,","venue":null,"work_id":"695f1555-64b0-4cc0-bb97-37a13ee19ffa","year":2023},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:17.484812Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:bd8357519e3d3a14afa4e4e18d3a5c6fa3bd7c1105707773a43363047d6aa8ec","observation_id":"62b9ec15-5f87-4b6a-8f5c-15f556c1c216","resolution":{"observed_at":"2026-08-06T19:07:20.329447Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.300253Z","title":"SmartRefine: A scenario-adaptive refinement framework for efficient motion prediction,","venue":null,"work_id":"9112a5a3-78e3-42cd-bb2e-2df0df7e14c9","year":2024},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:17.577488Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:10a45e0003f6a34bbef89f964617f328d18ec5e2639870a0bd446c88848f0643","observation_id":"ec849f60-f830-4fe6-9590-f9cefbe4538d","resolution":{"observed_at":"2026-08-06T19:07:20.305973Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.279763Z","title":"Criteria: A new bench- marking paradigm for evaluating trajectory prediction models for au- tonomous driving,","venue":null,"work_id":"141abf2c-9a85-4852-a733-7a1c6054b1b4","year":2024},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:17.680192Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:b7465f3e48165208f2b2ac311a4c87d332f0cde2287435ba2e907ce08aa281aa","observation_id":"b525f2f6-c8fb-407b-988d-cd9fbcc2c547","resolution":{"observed_at":"2026-08-06T19:07:20.286486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.259354Z","title":"Diverse and admissible trajectory forecasting through multimodal context understanding,","venue":null,"work_id":"7e042cbf-cf14-4c15-992b-9d3b156213b1","year":2020},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:17.829064Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:409afbb43cd9d277737ceaad6a50b69d0457ec00aae3d47fd0b11e6a47f51382","observation_id":"62a4bcaa-5fff-4a48-b9aa-48b2b5709d6c","resolution":{"observed_at":"2026-08-06T19:07:20.266468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.240221Z","title":"Reliable trajectory prediction in scene fusion based on spatio-temporal structure causal model,","venue":null,"work_id":"462af53d-c06c-4014-bee3-9270e8e6ad2c","year":2024},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:17.930145Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:36fcd949d81047298f4be34a64a45c552fcf13d5db7febad72cde5455fe1985e","observation_id":"889b42b7-f299-45b3-abcf-06f9c9fbc673","resolution":{"observed_at":"2026-08-06T19:07:20.246189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.221380Z","title":"Trajectory prediction for safety critical maneuvers in automated highway driving,","venue":null,"work_id":"df3498ee-9591-4b15-b374-c501910616c0","year":2018},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:18.127320Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:1f755094718394c85a2d03e6953271e91170940061fb6dec0a5646c9b3c2760c","observation_id":"44e06cc7-d636-447d-9250-0886125e25ea","resolution":{"observed_at":"2026-08-06T19:07:20.227010Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.200446Z","title":"When will it change the lane? A probabilistic regression approach for rarely occurring events,","venue":null,"work_id":"7006d765-67f7-456c-94f0-aa0ea12e5300","year":2015},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:18.245897Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:3eff4c04dfe78dd3e53726d5e48e41fe3065f33ef1a90e201cfa030e0e55c885","observation_id":"610ebb9b-27b1-4f6c-8a24-da91e43f02f0","resolution":{"observed_at":"2026-08-06T19:07:20.206825Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.06241","last_updated":"2020-02-14T20:11:13Z","snapshot_observed_at":"2026-07-06T08:57:26.370386Z","submitted_at":"2020-02-14T20:11:13Z","title":"Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network","version":1},"cited_work":{"arxiv_id":"2002.06241","doi":null,"metadata_source":"pith","pith_arxiv_id":"2002.06241","snapshot_observed_at":"2026-08-06T19:07:19.548380Z","title":"Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network","venue":"cs.CV","work_id":"8f29326d-033e-44fe-8caf-e99a5e4ab86b","year":2020},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:18.382123Z"},"links":{"cited_paper":"/paper/2002.06241","citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:d9c893cb192e8fdb0951c09ac7e2628bb5ee9317285cbb68396358d14a4e4e4b","observation_id":"473c5d79-721d-4409-9320-c68fc93eade9","resolution":{"observed_at":"2026-08-06T19:07:19.559348Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.182939Z","title":"DifTraj: Diffusion Inspired by Intrinsic Intention and Extrinsic Interaction for Multi-Modal Trajectory Prediction,","venue":null,"work_id":"1e78eeb8-8ef4-4fdd-9745-7c8579a9ce8c","year":2024},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:18.483703Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:68f9e548c54d4a7d868c4a863fd44f48950028147a2359051f78cc948c16785b","observation_id":"cc3b214a-05a6-40f2-9b95-b22b6da2d8ed","resolution":{"observed_at":"2026-08-06T19:07:20.188320Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.163977Z","title":"Social LSTM: Human trajectory prediction in crowded spaces,","venue":null,"work_id":"736ef364-c5f6-417d-910f-787d8e6cab7e","year":2016},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:18.599015Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:0a7ded6401ddd0e57a69343b496e4c100beb5e01ef553b61e557b05c728704b4","observation_id":"c9bc0eac-3958-4efa-b67e-2a385a2d33cc","resolution":{"observed_at":"2026-08-06T19:07:20.169446Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.146058Z","title":"Multimodal motion prediction with stacked transformers,","venue":null,"work_id":"0a30b38e-8655-4722-a695-528f1ba60bd5","year":2021},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:18.703648Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:4a4710acf5e6962154ebd696a434c61be131ac67fea8ea1c4c917721e3b1c81f","observation_id":"15627280-3019-4944-9778-fe411b6408a0","resolution":{"observed_at":"2026-08-06T19:07:20.151827Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.128808Z","title":"Scenario-transferable semantic graph reasoning for interaction-aware probabilistic prediction,","venue":null,"work_id":"fb5dad95-d0fb-41f9-941b-574028837b10","year":2022},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:18.776914Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:9c3c343dc559649caed7a948e2596f46526937dffbc851c8a06e5b7b64be7ca3","observation_id":"7ac6d282-4637-4508-9307-03c0bb1d4ea3","resolution":{"observed_at":"2026-08-06T19:07:20.134318Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.106809Z","title":"Intention- aware vehicle trajectory prediction based on spatial-temporal dynamic attention network for internet of vehicles,","venue":null,"work_id":"743268f4-e017-456e-9aa3-6624f57a60a5","year":2022},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:18.881841Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:50ef4841e9d839e73f26c0190951badf583f239a52e97332f19542fe279481f2","observation_id":"76ebd1dd-0401-41ae-9874-449098f74eb1","resolution":{"observed_at":"2026-08-06T19:07:20.113142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.086764Z","title":"GRIN: Generative relation and intention network for multi-agent trajectory prediction,","venue":null,"work_id":"8b3a9038-b4b3-40b4-bb6b-bbaae1c033a6","year":2021},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:18.977034Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:0d15d1dfacdb0dad565a7aeedffd509ec88fafa0cb053229008b802c3e850d60","observation_id":"970e0afd-b24b-4382-aa08-29699641371f","resolution":{"observed_at":"2026-08-06T19:07:20.091596Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.067142Z","title":"Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network,","venue":null,"work_id":"7c6574e1-917a-4318-8325-37e91ea92492","year":2022},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.038875Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:7587e8abc683d2a5e5aaca1f46f2a20452ff60f1c85ee8bcd942979f65a88a8d","observation_id":"72efd989-eb1e-42b5-a009-aa12207296aa","resolution":{"observed_at":"2026-08-06T19:07:20.073094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.050685Z","title":"Evolvegraph: Multi-agent trajectory prediction with dynamic relational reasoning,","venue":null,"work_id":"ec1b121d-0959-41b5-8f14-01799d32acf5","year":2020},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.132809Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:a7f1f6c5d7ce0820ddc6e95be62d84b26f5cdd6ca77b40e6c678e8e6aa5abf91","observation_id":"8d63fa2a-df14-4f70-aca8-e7ce99b92cd1","resolution":{"observed_at":"2026-08-06T19:07:20.055181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.032478Z","title":"Multi-agent trajectory prediction with difficulty-guided feature enhancement network,","venue":null,"work_id":"604e6512-f9fc-4fd9-a68d-c1c085cd694a","year":2025},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.197073Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:3b84612f36f846b2873cfe60b2907d2eec5cb688e057aa2469acda436820012c","observation_id":"9394310f-1808-4a93-adc6-415568514198","resolution":{"observed_at":"2026-08-06T19:07:20.038603Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:20.013380Z","title":"Laformer: Trajectory prediction for autonomous driving 13 with lane-aware scene constraints,","venue":null,"work_id":"f57210bb-a2e6-4f4b-aeed-8ce20c127bc6","year":2024},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.240558Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:7af0ca50e49f024f6185fc33d22ae8820ac393e67ef05aab69d341d360ff885e","observation_id":"c34c6c5a-50b4-46b3-98e2-2e04de74fd03","resolution":{"observed_at":"2026-08-06T19:07:20.019241Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:19.993960Z","title":"GoIRL: Graph-Oriented Inverse Reinforcement Learning for Multimodal Trajectory Prediction,","venue":null,"work_id":"9d9f8c28-7793-4fa6-a0dd-41394a3adc48","year":2025},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.245661Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:ed653cd2ab591f027a21d05c7c718182a6c70582b370aacb0edef88d6542ab45","observation_id":"6e8457fd-b3f7-49ca-96b8-1b2fb9a29eb1","resolution":{"observed_at":"2026-08-06T19:07:19.998926Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:19.971643Z","title":"Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data,","venue":null,"work_id":"1ae565c1-6ba0-4d37-8f30-84654d5931b0","year":2020},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.251221Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:4270e83f47417f037b9421136f912324c1f07704ba58367a123133554e96f14c","observation_id":"ce7d0d1e-619c-43cd-ab7e-38fbca4d2051","resolution":{"observed_at":"2026-08-06T19:07:19.976842Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:19.955300Z","title":"Agentformer: Agent- aware transformers for socio-temporal multi-agent forecasting,","venue":null,"work_id":"3cc4077a-ad8e-4a10-827e-8b58c8681b12","year":2021},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.255861Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:d9b017666c5e0931135a34c70411b969deb3c23ce8f25326b091d214d41166d4","observation_id":"38460f14-9eee-4daa-8d2e-49d697764dae","resolution":{"observed_at":"2026-08-06T19:07:19.960411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:19.938328Z","title":"HIVT: Hierarchical vector transformer for multi-agent motion prediction,","venue":null,"work_id":"397ed471-e9d4-401a-87cf-95662f19625b","year":2022},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.260607Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:c5e353334bad8b1582e02b4bba46eede9d556170aa7a64fa15a0291e1332e5f9","observation_id":"2df4d6b6-4874-407c-95ad-5b9f97663d1c","resolution":{"observed_at":"2026-08-06T19:07:19.943402Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2025.35297","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:07:19.506288Z","title":"Bidirectional agent-map interaction feature learning leveraged by map-related tasks for trajectory prediction in autonomous driving,","venue":null,"work_id":"6e4692cc-bb0c-47d2-96c9-ea885a22dfc6","year":2025},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.266061Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:459ab4e5cd0ba192c02da87982b84b3e50d3c7d9058cff7fea38e8ea914fd7de","observation_id":"fbb0d664-8958-4143-aac2-f8caa8c8a93e","resolution":{"observed_at":"2026-08-06T19:07:19.518297Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:19.920958Z","title":"SOPHIE: An attentive GAN for predicting paths compliant to social and physical constraints,","venue":null,"work_id":"11b3e0d6-6d37-4c91-8911-19edab7ef552","year":2019},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.270848Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:afb018887384d20f835da9ce8ef1715ff68009a2a71af8704306e3b89f79c51f","observation_id":"a36fb29e-3cd9-4855-86b0-138890556a5e","resolution":{"observed_at":"2026-08-06T19:07:19.925968Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:19.901977Z","title":"Multipath++: Efficient information fusion and trajectory aggregation for behavior prediction,","venue":null,"work_id":"42e22a24-66d7-4fa7-a60e-2b174e497a82","year":2022},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.276237Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:ec5131855a5fae48c542ea98b781816c92df0327945b73f778c911ea2c0243cd","observation_id":"5c361917-529c-4533-a319-29ff89384cc4","resolution":{"observed_at":"2026-08-06T19:07:19.907797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:19.884981Z","title":"Trajectory prediction with graph-based dual-scale context fusion,","venue":null,"work_id":"4ceb8311-814a-4d61-ad43-c303991347ef","year":2022},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.283249Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:61df4896d18186f56613d510fca54283402e44a363b2346224acf1518b7fccb7","observation_id":"619dfaf0-7d46-48c8-866c-13217e5c3a9d","resolution":{"observed_at":"2026-08-06T19:07:19.890112Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:19.867966Z","title":"R-pred: Two-stage motion prediction via tube-query attention-based trajectory refinement,","venue":null,"work_id":"6d145f54-f4b3-4a2c-a258-768430c7f08f","year":2023},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.288318Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:5483ace25555849d3e4a8f0e2ffadb6bf65b98e93dd7d2a71965a6b71aa2c1e9","observation_id":"0ffa68e4-c0a9-43db-90a4-89b7363c5468","resolution":{"observed_at":"2026-08-06T19:07:19.873309Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:19.848583Z","title":"Motion transformer with global intention localization and local movement refinement,","venue":null,"work_id":"529c09ee-1413-4855-9410-c6c97c3d098f","year":2022},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.294274Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:c349acde591d585fbaee4dcdd76bc5e1de8f4d0f2b042e53688390b6ab056e1a","observation_id":"efbc6491-8ff8-4acd-996e-5a1d6276f385","resolution":{"observed_at":"2026-08-06T19:07:19.854111Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:19.832158Z","title":"Bootstrap motion forecasting with self-consistent constraints,","venue":null,"work_id":"de8103ef-7173-42c3-b0d4-adf7c0903b1d","year":2023},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.300403Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:0ed30ade992b93891e031045f15d9a7015d69295649334244df4393760f36283","observation_id":"6ffcdbd3-39cc-434d-8dbf-282ca22dab22","resolution":{"observed_at":"2026-08-06T19:07:19.837178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:19.813670Z","title":"Macformer: Map-agent coupled transformer for real-time and robust trajectory prediction,","venue":null,"work_id":"02a40ced-d4ac-416d-b2af-6ec3cf4d427e","year":2023},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.305659Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:c790dc975cf6fdd4ba6288b5ab48dedea7aeeb657497cd1967134598e79249e3","observation_id":"45c8ddeb-bdee-466f-8f36-39918a48600e","resolution":{"observed_at":"2026-08-06T19:07:19.818695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:19.796002Z","title":"SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving,","venue":null,"work_id":"97220cb9-8518-4098-bfd4-c847d8715c93","year":2024},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.312986Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:d63e4b8e119e79fb06780b1f24510fe8d44ea20e51559fd0aa8b3652fdee76ed","observation_id":"31d3e14d-6cfc-4814-85c8-fa8ac154acb5","resolution":{"observed_at":"2026-08-06T19:07:19.801347Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:19.778644Z","title":"FJMP: Fac- torized joint multi-agent motion prediction over learned directed acyclic interaction graphs,","venue":null,"work_id":"ab84ca96-8fb5-4e13-b964-a6918dafcf10","year":2023},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.319841Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:42ae597d2bdea81bc538e26b36be455637c8c90c1f8bd45255abd9e243d4d77d","observation_id":"2aaeee9e-13c1-4363-a7dc-ba7e4550666c","resolution":{"observed_at":"2026-08-06T19:07:19.784373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:19.760180Z","title":"Traj-MAE: Masked autoencoders for trajectory prediction,","venue":null,"work_id":"bbe4bd5b-4923-464a-996a-041d0538f898","year":2023},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.324904Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:aca9ee51d274639f96213b5b5ebf358a001363d59916598a200898b4540e4c7d","observation_id":"6f8e6a7c-6620-41c6-ac8c-340d29d8d6c9","resolution":{"observed_at":"2026-08-06T19:07:19.766208Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:19.743104Z","title":"HDGT: Heterogeneous driving graph transformer for multi-agent trajectory prediction via scene encoding,","venue":null,"work_id":"aac1fc4d-099b-43c9-bc9a-ff0412b882fc","year":2023},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.329153Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:2ca54d24bc67c090c600a08d6bea1ae8eb680f4173739efe0ce30ed15ab6b333","observation_id":"fbd62572-6a2f-41d6-a894-676ccdabe956","resolution":{"observed_at":"2026-08-06T19:07:19.747708Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:19.727179Z","title":"THOMAS: Trajectory heatmap output with learned multi-agent sam- pling,","venue":null,"work_id":"d8e10303-c650-43d3-bef0-cff2c20a10f2","year":2022},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.333923Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:f276ec426163aeedb13a6dde1d9bebca64377a4a011e6c13798d4d79bdbab9a3","observation_id":"fe242b41-92ba-4ec8-bf68-ec4aadbbe928","resolution":{"observed_at":"2026-08-06T19:07:19.731956Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:19.709108Z","title":"Latent variable sequential set transformers for joint multi-agent motion prediction,","venue":null,"work_id":"bd3dff4d-8b2d-49fb-b37e-fb7f89a5aef2","year":2022},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.338647Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:7b06bcc01bac6f0277b0bc8c2437ea57a5476d515954886a1f7749c21b7dd312","observation_id":"9c8dccf1-6321-4c37-87e0-1c19dd0db8b6","resolution":{"observed_at":"2026-08-06T19:07:19.714661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:19.688897Z","title":"Stochastic multiple choice learning for training diverse deep ensembles,","venue":null,"work_id":"b6feb356-6a8f-47e2-882f-0e98f7f572ae","year":2016},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.343239Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:d9704dddac02b3df2da0e2edb1b7bad997ee2b676c92168b675d5bf165944db9","observation_id":"f9419833-a8a2-49c7-9a6e-fcc6ca4c5e92","resolution":{"observed_at":"2026-08-06T19:07:19.695103Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-06T19:07:19.669518Z","title":"Argoverse: 3D tracking and forecasting with rich maps,","venue":null,"work_id":"3b8ee635-c1da-4b99-b4ba-94cf997223f9","year":2019},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.350098Z"},"links":{"citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:890297ba8816d4fb29688f92168e8e0f5c9651e654b51539d1c1d87f0f618fe4","observation_id":"199b058b-c2cd-41ee-90e5-945e5ae2e9aa","resolution":{"observed_at":"2026-08-06T19:07:19.674952Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.03088","last_updated":"2019-09-30T17:26:51Z","snapshot_observed_at":"2026-08-08T11:58:48.029939Z","submitted_at":"2019-09-30T17:26:51Z","title":"INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.03088","snapshot_observed_at":"2026-08-06T19:07:19.355271Z","title":"Interaction dataset: An international, adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps,","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-06T19:07:19.355271Z"},"links":{"cited_paper":"/paper/1910.03088","citing_paper":"/paper/2507.06531"},"observation_digest":"sha256:d253b34da8d4f821e8721a59f50188103aacba704d52059c1709c28dfca2d9f4","observation_id":"f3b8b482-de03-4f2b-ac21-f27c67153995","resolution":{"observed_at":"2026-08-06T19:07:19.355271Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.06531","last_updated":"2025-07-09T04:18:01Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-11T20:46:45.531267Z","submitted_at":"2025-07-09T04:18:01Z","title":"ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture"},"reference_resolution":{"displayed":68,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":5,"verified_exact":1,"verified_fuzzy":61},"total_outbound_references":68},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 0 inbound Pith citation observations for arXiv:2507.06531."}