{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:IXU5YT27RPXDM4XGVYSNKYKQLE","short_pith_number":"pith:IXU5YT27","schema_version":"1.0","canonical_sha256":"45e9dc4f5f8bee3672e6ae24d5615059376a5cf08b9e86eebb849dfa389555ec","source":{"kind":"arxiv","id":"2305.06292","version":2},"attestation_state":"computed","paper":{"title":"Joint Metrics Matter: A Better Standard for Trajectory Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Deva Ramanan, Erica Weng, Hana Hoshino, Kris Kitani","submitted_at":"2023-05-10T16:27:55Z","abstract_excerpt":"Multi-modal trajectory forecasting methods commonly evaluate using single-agent metrics (marginal metrics), such as minimum Average Displacement Error (ADE) and Final Displacement Error (FDE), which fail to capture joint performance of multiple interacting agents. Only focusing on marginal metrics can lead to unnatural predictions, such as colliding trajectories or diverging trajectories for people who are clearly walking together as a group. Consequently, methods optimized for marginal metrics lead to overly-optimistic estimations of performance, which is detrimental to progress in trajectory"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2305.06292","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2023-05-10T16:27:55Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"852b48b5328e3c0f05f0d97f83fd88e21f96fdb8a0ec0a91f5a55435a48221fc","abstract_canon_sha256":"7ba088d0af383e39ac67f28eb7775ef629a102d66cf4407924187247c1d2abae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:59:52.125175Z","signature_b64":"VixRBE2aIeyQYgY0AAITaTPQrJ8T+ufCaYEReQ6GmQApT9D2K6LeNZzJy6l3DhPqkGpMm84F6QnYgZSTBEwcBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45e9dc4f5f8bee3672e6ae24d5615059376a5cf08b9e86eebb849dfa389555ec","last_reissued_at":"2026-07-05T06:59:52.124829Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:59:52.124829Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Joint Metrics Matter: A Better Standard for Trajectory Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Deva Ramanan, Erica Weng, Hana Hoshino, Kris Kitani","submitted_at":"2023-05-10T16:27:55Z","abstract_excerpt":"Multi-modal trajectory forecasting methods commonly evaluate using single-agent metrics (marginal metrics), such as minimum Average Displacement Error (ADE) and Final Displacement Error (FDE), which fail to capture joint performance of multiple interacting agents. Only focusing on marginal metrics can lead to unnatural predictions, such as colliding trajectories or diverging trajectories for people who are clearly walking together as a group. Consequently, methods optimized for marginal metrics lead to overly-optimistic estimations of performance, which is detrimental to progress in trajectory"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.06292","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2305.06292/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2305.06292","created_at":"2026-07-05T06:59:52.124889+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.06292v2","created_at":"2026-07-05T06:59:52.124889+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.06292","created_at":"2026-07-05T06:59:52.124889+00:00"},{"alias_kind":"pith_short_12","alias_value":"IXU5YT27RPXD","created_at":"2026-07-05T06:59:52.124889+00:00"},{"alias_kind":"pith_short_16","alias_value":"IXU5YT27RPXDM4XG","created_at":"2026-07-05T06:59:52.124889+00:00"},{"alias_kind":"pith_short_8","alias_value":"IXU5YT27","created_at":"2026-07-05T06:59:52.124889+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IXU5YT27RPXDM4XGVYSNKYKQLE","json":"https://pith.science/pith/IXU5YT27RPXDM4XGVYSNKYKQLE.json","graph_json":"https://pith.science/api/pith-number/IXU5YT27RPXDM4XGVYSNKYKQLE/graph.json","events_json":"https://pith.science/api/pith-number/IXU5YT27RPXDM4XGVYSNKYKQLE/events.json","paper":"https://pith.science/paper/IXU5YT27"},"agent_actions":{"view_html":"https://pith.science/pith/IXU5YT27RPXDM4XGVYSNKYKQLE","download_json":"https://pith.science/pith/IXU5YT27RPXDM4XGVYSNKYKQLE.json","view_paper":"https://pith.science/paper/IXU5YT27","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.06292&json=true","fetch_graph":"https://pith.science/api/pith-number/IXU5YT27RPXDM4XGVYSNKYKQLE/graph.json","fetch_events":"https://pith.science/api/pith-number/IXU5YT27RPXDM4XGVYSNKYKQLE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IXU5YT27RPXDM4XGVYSNKYKQLE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IXU5YT27RPXDM4XGVYSNKYKQLE/action/storage_attestation","attest_author":"https://pith.science/pith/IXU5YT27RPXDM4XGVYSNKYKQLE/action/author_attestation","sign_citation":"https://pith.science/pith/IXU5YT27RPXDM4XGVYSNKYKQLE/action/citation_signature","submit_replication":"https://pith.science/pith/IXU5YT27RPXDM4XGVYSNKYKQLE/action/replication_record"}},"created_at":"2026-07-05T06:59:52.124889+00:00","updated_at":"2026-07-05T06:59:52.124889+00:00"}