{"as_of":"2026-08-09T11:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:45cc66a38a373e2f44f343d9eb9d0c4ee63abb2576b7dcbab913ff8832a55e6f","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":24,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":24,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":24,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":24,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T11:26:29.624226Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T16:59:58.664339Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":"2207.05844","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-07-04T16:59:58.664339Z","title":"Wayformer: Motion forecasting via simple & efﬁcient attention networks","venue":null,"work_id":"6b0b694a-dc18-4985-aafd-6e711933493f","year":2022},"citing_paper":{"arxiv_id":"2304.10891","last_updated":"2026-06-03T12:29:34Z","snapshot_observed_at":"2026-08-02T06:51:34.925550Z","submitted_at":"2023-04-21T11:15:31Z","title":"Transformer-Based Autonomous Driving Models and Deployment-Oriented Compression: A Survey","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-24T09:34:38.680374Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2304.10891"},"observation_digest":"sha256:514ee2a6a3b445aff971ecb6fb7d7614bc5d3edf01bdbb5b6a235eb86428f5d0","observation_id":"080223d5-7748-4caf-8a24-d48a45a0b68c","resolution":{"observed_at":"2026-05-24T09:36:07.048306Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-08-07T11:26:29.624226Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.02571","last_updated":"2025-06-03T07:53:04Z","snapshot_observed_at":"2026-08-07T19:27:41.362362Z","submitted_at":"2025-06-03T07:53:04Z","title":"Contrast & Compress: Learning Lightweight Embeddings for Short Trajectories","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T11:26:29.624226Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2506.02571"},"observation_digest":"sha256:fcd3da797d3434eb058ce4e4911aac76d96936cf5d815211d6bb4f749fadcbc4","observation_id":"6be6aaef-e9cd-48c9-a555-a7480040567d","resolution":{"observed_at":"2026-08-07T11:26:29.624226Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-08-06T22:40:12.398917Z","title":"S., and Sapp, B","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.21121","last_updated":"2025-06-26T09:46:53Z","snapshot_observed_at":"2026-08-09T07:33:33.315628Z","submitted_at":"2025-06-26T09:46:53Z","title":"GoIRL: Graph-Oriented Inverse Reinforcement Learning for Multimodal Trajectory Prediction","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-06T22:40:12.398917Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2506.21121"},"observation_digest":"sha256:b99496185e6d2300bd47ffe90ee37eb38cf8c0a089d2bfbae6296e2eac193472","observation_id":"85cd4b5f-4ba1-4358-963b-97421214a8fb","resolution":{"observed_at":"2026-08-06T22:40:12.398917Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-08-06T14:13:04.027569Z","title":"Nayakanti, R","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.19701","last_updated":"2025-07-25T22:45:42Z","snapshot_observed_at":"2026-08-08T06:22:03.555253Z","submitted_at":"2025-07-25T22:45:42Z","title":"PhysVarMix: Physics-Informed Variational Mixture Model for Multi-Modal Trajectory Prediction","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T14:13:04.027569Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2507.19701"},"observation_digest":"sha256:96de13db1f36529a96a94b8bae9276913c5bc35464eebc8a42d4dc6980b83428","observation_id":"5903acdf-5975-4350-b1cd-1a47146bba63","resolution":{"observed_at":"2026-08-06T14:13:04.027569Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-08-06T11:58:18.312921Z","title":"S.; and Sapp, B","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.22282","last_updated":"2025-07-29T23:16:04Z","snapshot_observed_at":"2026-08-06T11:58:15.865618Z","submitted_at":"2025-07-29T23:16:04Z","title":"Multi-Agent Path Finding Among Dynamic Uncontrollable Agents with Statistical Safety Guarantees","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-06T11:58:18.312921Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2507.22282"},"observation_digest":"sha256:1027ab016fd49ebac5a718ceef9e7555ea892411c36628969d8d6350636c0c81","observation_id":"498b171c-476d-4f18-be10-88840f82039a","resolution":{"observed_at":"2026-08-06T11:58:18.312921Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":"2207.05844","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-07-04T16:59:58.664339Z","title":"Wayformer: Motion forecasting via simple & efﬁcient attention networks","venue":null,"work_id":"6b0b694a-dc18-4985-aafd-6e711933493f","year":2022},"citing_paper":{"arxiv_id":"2510.04978","last_updated":"2026-04-30T05:27:37Z","snapshot_observed_at":"2026-08-03T00:20:05.639737Z","submitted_at":"2025-10-06T16:16:03Z","title":"Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI","version":5},"reference_index":298,"source":"pdf_text","source_observed_at":"2026-05-18T09:56:36.716680Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2510.04978"},"observation_digest":"sha256:dd49e327975ed95c102ec9208ad5e8e403412ca44e6c433afc815502c1a0f77b","observation_id":"e6d8c554-0d3c-4c46-8e10-34accde96a1e","resolution":{"observed_at":"2026-05-18T10:01:13.971300Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-07-15T00:16:35.376647Z","title":"Wayformer: Motion forecasting via simple & efficient attention networks.arXiv preprint arXiv:2207.05844, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.09420","last_updated":"2026-06-18T12:01:08Z","snapshot_observed_at":"2026-08-03T23:56:59.501054Z","submitted_at":"2026-03-10T09:35:08Z","title":"Class-Incremental Motion Forecasting","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-15T00:16:35.376647Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2603.09420"},"observation_digest":"sha256:f11fec2d5822254f29175563960ccd2e22445f5aca601cdc01a5bb21c65706a4","observation_id":"ccf2fc91-ccf9-4eca-8331-f788d92e42d4","resolution":{"observed_at":"2026-07-15T00:16:35.376647Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":"2207.05844","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-07-04T16:59:58.664339Z","title":"Wayformer: Motion forecasting via simple & efﬁcient attention networks","venue":null,"work_id":"6b0b694a-dc18-4985-aafd-6e711933493f","year":2022},"citing_paper":{"arxiv_id":"2604.11734","last_updated":"2026-05-11T16:12:05Z","snapshot_observed_at":"2026-08-02T23:33:25.516455Z","submitted_at":"2026-04-13T17:13:46Z","title":"SCORP: Scene-Consistent Multi-agent Diffusion Planning with Stable Online Reinforcement Post-Training for Cooperative Driving","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-10T14:59:21.264306Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2604.11734"},"observation_digest":"sha256:58f8a66c0215a0615267e611f9d809ff1836ee94988192dd026dd4c90280104a","observation_id":"f3a8e80e-fbf4-4de7-9f4c-a0d164878bcb","resolution":{"observed_at":"2026-05-11T11:21:04.631742Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":"2207.05844","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-07-04T16:59:58.664339Z","title":"Wayformer: Motion forecasting via simple & efﬁcient attention networks","venue":null,"work_id":"6b0b694a-dc18-4985-aafd-6e711933493f","year":2022},"citing_paper":{"arxiv_id":"2604.11734","last_updated":"2026-05-11T16:12:05Z","snapshot_observed_at":"2026-08-02T23:33:25.516455Z","submitted_at":"2026-04-13T17:13:46Z","title":"SCORP: Scene-Consistent Multi-agent Diffusion Planning with Stable Online Reinforcement Post-Training for Cooperative Driving","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-12T04:23:02.394477Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2604.11734"},"observation_digest":"sha256:257469714dde509069d63a2ed122ddb2b3b503ce787404d9b6db18ab0ef96b3f","observation_id":"374311fb-844b-4983-a7f2-8191afcb9160","resolution":{"observed_at":"2026-05-12T06:21:25.548306Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":"2207.05844","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-07-04T16:59:58.664339Z","title":"Wayformer: Motion forecasting via simple & efﬁcient attention networks","venue":null,"work_id":"6b0b694a-dc18-4985-aafd-6e711933493f","year":2022},"citing_paper":{"arxiv_id":"2604.16783","last_updated":"2026-04-18T02:13:02Z","snapshot_observed_at":"2026-07-06T23:04:01.558812Z","submitted_at":"2026-04-18T02:13:02Z","title":"EdgeVTP: Exploration of Latency-efficient Trajectory Prediction for Edge-based Embedded Vision Applications","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-10T07:53:01.735375Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2604.16783"},"observation_digest":"sha256:3d95214a73d18f60710d76df6341d9724d214bd5b7d21ef2b09dbafc8d7f11e5","observation_id":"213d9502-d1ef-4142-8ab0-bec43093022d","resolution":{"observed_at":"2026-05-10T09:18:32.361672Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":"2207.05844","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-07-04T16:59:58.664339Z","title":"Wayformer: Motion forecasting via simple & efﬁcient attention networks","venue":null,"work_id":"6b0b694a-dc18-4985-aafd-6e711933493f","year":2022},"citing_paper":{"arxiv_id":"2604.26065","last_updated":"2026-04-28T19:06:06Z","snapshot_observed_at":"2026-08-07T23:27:37.745583Z","submitted_at":"2026-04-28T19:06:06Z","title":"FlowS: One-Step Motion Prediction via Local Transport Conditioning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-07T15:29:43.164528Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2604.26065"},"observation_digest":"sha256:1e73f03c2854c4a36c971d5a2de7917aec15ea1d9671e85a40e1ef38bed709b3","observation_id":"82ce84b0-6055-42ef-beab-18f6810e391e","resolution":{"observed_at":"2026-05-12T00:21:21.275339Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":"2207.05844","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-07-04T16:59:58.664339Z","title":"Wayformer: Motion forecasting via simple & efﬁcient attention networks","venue":null,"work_id":"6b0b694a-dc18-4985-aafd-6e711933493f","year":2022},"citing_paper":{"arxiv_id":"2605.00050","last_updated":"2026-04-29T14:33:27Z","snapshot_observed_at":"2026-07-06T23:13:33.799847Z","submitted_at":"2026-04-29T14:33:27Z","title":"Learning physically grounded traffic accident reconstruction from public accident reports","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-09T19:59:27.929899Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2605.00050"},"observation_digest":"sha256:2240ef5ab5fd583e7933745c7011d44bb2db3cdda0f6d66bf8c2c8571f75f8bf","observation_id":"a00add68-a0b8-4aff-9b1b-27fc7d409598","resolution":{"observed_at":"2026-05-11T15:26:10.116456Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":"2207.05844","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-07-04T16:59:58.664339Z","title":"Wayformer: Motion forecasting via simple & efﬁcient attention networks","venue":null,"work_id":"6b0b694a-dc18-4985-aafd-6e711933493f","year":2022},"citing_paper":{"arxiv_id":"2605.01222","last_updated":"2026-05-02T03:32:23Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T03:32:23Z","title":"Zero-Shot Signal Temporal Logic Planning with Disjunctive Branch Selection in Dynamic Semantic Maps","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-09T15:08:00.493447Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2605.01222"},"observation_digest":"sha256:cc9c16d22fb5e80072c7530ddf67ceeaf62a2421a56ed7cf357d4918b3209a73","observation_id":"36c1f502-dbe6-4ca2-ad88-78cd066fd3b9","resolution":{"observed_at":"2026-05-11T16:46:07.080229Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":"2207.05844","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-07-04T16:59:58.664339Z","title":"Wayformer: Motion forecasting via simple & efﬁcient attention networks","venue":null,"work_id":"6b0b694a-dc18-4985-aafd-6e711933493f","year":2022},"citing_paper":{"arxiv_id":"2605.01393","last_updated":"2026-05-02T11:31:26Z","snapshot_observed_at":"2026-08-02T16:40:42.981378Z","submitted_at":"2026-05-02T11:31:26Z","title":"Recall to Predict: Grounding Motion Forecasting in Interpretable Motion Bank","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-09T15:12:20.245550Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2605.01393"},"observation_digest":"sha256:2d4adac9e374ed380bbb5223f5f54f8b6423c04ae3db1293526903d6c0dd3f42","observation_id":"685c3f12-629d-4a1b-95bd-f4d353ff4db5","resolution":{"observed_at":"2026-05-11T16:46:05.594245Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":"2207.05844","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-07-04T16:59:58.664339Z","title":"Wayformer: Motion forecasting via simple & efﬁcient attention networks","venue":null,"work_id":"6b0b694a-dc18-4985-aafd-6e711933493f","year":2022},"citing_paper":{"arxiv_id":"2605.09171","last_updated":"2026-05-12T03:46:03Z","snapshot_observed_at":"2026-07-06T23:21:16.454273Z","submitted_at":"2026-05-09T21:15:28Z","title":"SHIELD: Scalable Optimal Control with Certification using Duality and Convexity","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-12T02:06:53.248218Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2605.09171"},"observation_digest":"sha256:4d30d96cac114de4bd59b3260ad5253dfad2df78e0abea62c540ef99ed0c2fe2","observation_id":"686b560b-3c8b-4387-8890-fa6b0558ec99","resolution":{"observed_at":"2026-05-12T02:11:16.286321Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":"2207.05844","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-07-04T16:59:58.664339Z","title":"Wayformer: Motion forecasting via simple & efﬁcient attention networks","venue":null,"work_id":"6b0b694a-dc18-4985-aafd-6e711933493f","year":2022},"citing_paper":{"arxiv_id":"2605.09171","last_updated":"2026-05-12T03:46:03Z","snapshot_observed_at":"2026-07-06T23:21:16.454273Z","submitted_at":"2026-05-09T21:15:28Z","title":"SHIELD: Scalable Optimal Control with Certification using Duality and Convexity","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-13T06:26:20.914150Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2605.09171"},"observation_digest":"sha256:caf930f67cc0e52824ab0fe1ee48294b3c01467f72931d6381a378348c00fe81","observation_id":"3bef6565-790b-4e37-83bb-15b53541f0a5","resolution":{"observed_at":"2026-05-13T06:27:24.378249Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":"2207.05844","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-07-04T16:59:58.664339Z","title":"Wayformer: Motion forecasting via simple & efﬁcient attention networks","venue":null,"work_id":"6b0b694a-dc18-4985-aafd-6e711933493f","year":2022},"citing_paper":{"arxiv_id":"2605.13646","last_updated":"2026-05-19T11:58:26Z","snapshot_observed_at":"2026-08-02T22:03:11.881725Z","submitted_at":"2026-05-13T15:06:22Z","title":"Causality-Aware End-to-End Autonomous Driving via Ego-Centric Joint Scene Modeling","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-14T18:26:18.738944Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2605.13646"},"observation_digest":"sha256:677f8ed0fc6af501f956de2fdd508eea62f1d19dab94e1abb73971d1c6134955","observation_id":"4074b55d-9eb9-472f-b276-eb47ce81d096","resolution":{"observed_at":"2026-05-14T18:27:35.445435Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":"2207.05844","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-07-04T16:59:58.664339Z","title":"Wayformer: Motion forecasting via simple & efﬁcient attention networks","venue":null,"work_id":"6b0b694a-dc18-4985-aafd-6e711933493f","year":2022},"citing_paper":{"arxiv_id":"2605.13646","last_updated":"2026-05-19T11:58:26Z","snapshot_observed_at":"2026-08-02T22:03:11.881725Z","submitted_at":"2026-05-13T15:06:22Z","title":"Causality-Aware End-to-End Autonomous Driving via Ego-Centric Joint Scene Modeling","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-20T20:55:36.793147Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2605.13646"},"observation_digest":"sha256:ed42b8840d1e8c127d164cbd256c115d14717d98b7c61f6fc0685997830540a3","observation_id":"470909ca-9179-44a9-bba3-f9c5c510e2ac","resolution":{"observed_at":"2026-05-20T20:59:01.796493Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":"2207.05844","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-07-04T16:59:58.664339Z","title":"Wayformer: Motion forecasting via simple & efﬁcient attention networks","venue":null,"work_id":"6b0b694a-dc18-4985-aafd-6e711933493f","year":2022},"citing_paper":{"arxiv_id":"2605.22189","last_updated":"2026-05-21T08:56:38Z","snapshot_observed_at":"2026-08-02T17:25:02.859347Z","submitted_at":"2026-05-21T08:56:38Z","title":"Learning A Unified Risk Map for Autonomous Driving in Partially Observable Environments","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-22T06:07:18.257587Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2605.22189"},"observation_digest":"sha256:484d4747259c3c07b00d07509b7f4e8e4ba9554c09cb2588ccac9753cedd2ebb","observation_id":"56d0776a-a2aa-4ab8-82c2-b874e8001f3d","resolution":{"observed_at":"2026-05-22T06:11:09.274026Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":"2207.05844","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-07-04T16:59:58.664339Z","title":"Wayformer: Motion forecasting via simple & efﬁcient attention networks","venue":null,"work_id":"6b0b694a-dc18-4985-aafd-6e711933493f","year":2022},"citing_paper":{"arxiv_id":"2606.25002","last_updated":"2026-06-23T16:28:09Z","snapshot_observed_at":"2026-08-07T13:28:18.417013Z","submitted_at":"2026-06-23T16:28:09Z","title":"TRACER: Training-Free Closed-Loop Structured Inference for Traffic Accident Reconstruction","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-26T00:02:49.064677Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2606.25002"},"observation_digest":"sha256:6c5c8e6d96d8bf905ca8dfbaceb0093709e810e2f42e5b6294db9ec5b99c19c3","observation_id":"ceb47754-6c9b-4ea7-a68f-18c4425bc12b","resolution":{"observed_at":"2026-07-04T16:59:58.666510Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":"2207.05844","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-07-04T16:59:58.664339Z","title":"Wayformer: Motion forecasting via simple & efﬁcient attention networks","venue":null,"work_id":"6b0b694a-dc18-4985-aafd-6e711933493f","year":2022},"citing_paper":{"arxiv_id":"2606.26424","last_updated":"2026-06-24T22:26:43Z","snapshot_observed_at":"2026-08-07T22:43:50.121138Z","submitted_at":"2026-06-24T22:26:43Z","title":"Rethinking Training & Inference for Forecasting: Linking Winner-Take-All back to GMMs","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-26T01:16:40.007718Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2606.26424"},"observation_digest":"sha256:4e4ec23bf7946eb26d4fff32ac927895c36e3b4dadd0309207292c54c8143a5f","observation_id":"195807fa-0246-4085-a5f4-cbc4977cb06c","resolution":{"observed_at":"2026-07-04T15:49:58.044664Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-07-14T16:59:14.895571Z","title":"Wayformer: Motion forecasting via simple and efficient attention networks,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.09725","last_updated":"2026-06-29T11:54:04Z","snapshot_observed_at":"2026-08-07T21:51:11.920686Z","submitted_at":"2026-06-29T11:54:04Z","title":"Learning High-Level Decision Making with an Interaction-Aware Attention-Based Network in Autonomous Driving","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-14T16:59:14.895571Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2607.09725"},"observation_digest":"sha256:b5f35539f87b1942f2b1613e902deda81a65e80a07ffe62f372acafd01c9baf7","observation_id":"34e2b0ea-7dda-455c-9ade-2a4d9f28a7cf","resolution":{"observed_at":"2026-07-14T16:59:14.895571Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-07-14T16:37:01.846968Z","title":"arXiv preprint arXiv:2207.05844 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09740","last_updated":"2026-07-02T23:55:53Z","snapshot_observed_at":"2026-08-07T12:52:02.711048Z","submitted_at":"2026-07-02T23:55:53Z","title":"A Dynamic Scene Interaction Reasoning Framework for Scene-level Lane-Change Intention and Trajectory Prediction of Multiple Interacting Vehicles","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-07-14T16:37:01.846968Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2607.09740"},"observation_digest":"sha256:88a4b55b03f2cce03b84846d5b3373f61b45deadd1589166e6494ea4109f08ec","observation_id":"34156b7c-5af8-478d-b34d-351ee1f129aa","resolution":{"observed_at":"2026-07-14T16:37:01.846968Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.05844","snapshot_observed_at":"2026-08-05T20:54:17.912634Z","title":"Way- former: Motion forecasting via simple & efficient attention networks,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.03330","last_updated":"2026-08-04T08:39:16Z","snapshot_observed_at":"2026-08-08T18:46:39.576493Z","submitted_at":"2026-08-04T08:39:16Z","title":"Long-term Traffic Scene Prediction via Polynomial Representations in Autonomous Driving","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-05T20:54:17.912634Z"},"links":{"cited_paper":"/paper/2207.05844","citing_paper":"/paper/2608.03330"},"observation_digest":"sha256:6cda496d434c9105ef76c8188996742fb0e412a4c574099ff139844ff278399d","observation_id":"da5b36cc-1f10-4d86-b0bc-3332d4846953","resolution":{"observed_at":"2026-08-05T20:54:17.912634Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2207.05844/citation-record","integrity":"/paper/2207.05844/integrity","json":"/paper/2207.05844/citation-record.json","paper":"/paper/2207.05844"},"outbound":[],"paper":{"arxiv_id":"2207.05844","last_updated":"2022-07-12T21:19:04Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-01T11:42:19.399562Z","submitted_at":"2022-07-12T21:19:04Z","title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 24 inbound Pith citation observations for arXiv:2207.05844."}