{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ELEWZH5QYSONWPDCAILQU2FZ47","short_pith_number":"pith:ELEWZH5Q","schema_version":"1.0","canonical_sha256":"22c96c9fb0c49cdb3c6202170a68b9e7e6afa2e573f94e4c3380983dff952796","source":{"kind":"arxiv","id":"2403.18452","version":1},"attestation_state":"computed","paper":{"title":"SingularTrajectory: Universal Trajectory Predictor Using Diffusion Model","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Hae-Gon Jeon, Inhwan Bae, Young-Jae Park","submitted_at":"2024-03-27T11:11:08Z","abstract_excerpt":"There are five types of trajectory prediction tasks: deterministic, stochastic, domain adaptation, momentary observation, and few-shot. These associated tasks are defined by various factors, such as the length of input paths, data split and pre-processing methods. Interestingly, even though they commonly take sequential coordinates of observations as input and infer future paths in the same coordinates as output, designing specialized architectures for each task is still necessary. For the other task, generality issues can lead to sub-optimal performances. In this paper, we propose SingularTra"},"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":"2403.18452","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-27T11:11:08Z","cross_cats_sorted":["cs.LG","cs.RO"],"title_canon_sha256":"7e99b11f50b342f03300da0ea60194cb26507726977705b8797761b5eed3c6d2","abstract_canon_sha256":"43deb28e003bc10966a5003dbb5c18a95a9ee51783f8ef343a4c25dc2d37b18d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:01:22.883283Z","signature_b64":"rQn/lL4nuixoqeLqqRdJ807VIBkjX0FvjLuqAxstmZkDhZnL/Ar679T+wsDjGdMgTuHYawihmAl6jjV1LXp0Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"22c96c9fb0c49cdb3c6202170a68b9e7e6afa2e573f94e4c3380983dff952796","last_reissued_at":"2026-07-05T08:01:22.882918Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:01:22.882918Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SingularTrajectory: Universal Trajectory Predictor Using Diffusion Model","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Hae-Gon Jeon, Inhwan Bae, Young-Jae Park","submitted_at":"2024-03-27T11:11:08Z","abstract_excerpt":"There are five types of trajectory prediction tasks: deterministic, stochastic, domain adaptation, momentary observation, and few-shot. These associated tasks are defined by various factors, such as the length of input paths, data split and pre-processing methods. Interestingly, even though they commonly take sequential coordinates of observations as input and infer future paths in the same coordinates as output, designing specialized architectures for each task is still necessary. For the other task, generality issues can lead to sub-optimal performances. In this paper, we propose SingularTra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.18452","kind":"arxiv","version":1},"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/2403.18452/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":"2403.18452","created_at":"2026-07-05T08:01:22.882976+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.18452v1","created_at":"2026-07-05T08:01:22.882976+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.18452","created_at":"2026-07-05T08:01:22.882976+00:00"},{"alias_kind":"pith_short_12","alias_value":"ELEWZH5QYSON","created_at":"2026-07-05T08:01:22.882976+00:00"},{"alias_kind":"pith_short_16","alias_value":"ELEWZH5QYSONWPDC","created_at":"2026-07-05T08:01:22.882976+00:00"},{"alias_kind":"pith_short_8","alias_value":"ELEWZH5Q","created_at":"2026-07-05T08:01:22.882976+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.11463","citing_title":"Encore: Conditioning Trajectory Forecasting via Biased Ego Rehearsals","ref_index":19,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ELEWZH5QYSONWPDCAILQU2FZ47","json":"https://pith.science/pith/ELEWZH5QYSONWPDCAILQU2FZ47.json","graph_json":"https://pith.science/api/pith-number/ELEWZH5QYSONWPDCAILQU2FZ47/graph.json","events_json":"https://pith.science/api/pith-number/ELEWZH5QYSONWPDCAILQU2FZ47/events.json","paper":"https://pith.science/paper/ELEWZH5Q"},"agent_actions":{"view_html":"https://pith.science/pith/ELEWZH5QYSONWPDCAILQU2FZ47","download_json":"https://pith.science/pith/ELEWZH5QYSONWPDCAILQU2FZ47.json","view_paper":"https://pith.science/paper/ELEWZH5Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.18452&json=true","fetch_graph":"https://pith.science/api/pith-number/ELEWZH5QYSONWPDCAILQU2FZ47/graph.json","fetch_events":"https://pith.science/api/pith-number/ELEWZH5QYSONWPDCAILQU2FZ47/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ELEWZH5QYSONWPDCAILQU2FZ47/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ELEWZH5QYSONWPDCAILQU2FZ47/action/storage_attestation","attest_author":"https://pith.science/pith/ELEWZH5QYSONWPDCAILQU2FZ47/action/author_attestation","sign_citation":"https://pith.science/pith/ELEWZH5QYSONWPDCAILQU2FZ47/action/citation_signature","submit_replication":"https://pith.science/pith/ELEWZH5QYSONWPDCAILQU2FZ47/action/replication_record"}},"created_at":"2026-07-05T08:01:22.882976+00:00","updated_at":"2026-07-05T08:01:22.882976+00:00"}