{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:AETP2NB3OTI5OQSUNQNU6NZTMF","short_pith_number":"pith:AETP2NB3","schema_version":"1.0","canonical_sha256":"0126fd343b74d1d742546c1b4f373361575030033d23e0a4d66f5bae69b0a064","source":{"kind":"arxiv","id":"2606.30777","version":1},"attestation_state":"computed","paper":{"title":"Unveiling Transferability in Trajectory Prediction via Latent Scene Embeddings","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bj\\\"orn Olofsson, David Axelsson, Erik Frisk, Theodor Westny","submitted_at":"2026-06-29T18:07:34Z","abstract_excerpt":"The growing availability of trajectory datasets has fueled major advances in data-driven motion prediction. Yet, models trained on one dataset often fail to generalize beyond their training domain as a result of differences in scene layouts, agent behaviors, and sensing conditions. A framework that learns latent representations of datasets and quantifies their similarity using distributional metrics is presented. This large-scale study covers 24 major datasets, including the most widely used motion-prediction benchmarks, and shows that the resulting transferability scores strongly correlate wi"},"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":"2606.30777","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-29T18:07:34Z","cross_cats_sorted":[],"title_canon_sha256":"475bcf0cac92100459fdeccfa3c1b9df2db6f4e3669130187a4afc78016ef847","abstract_canon_sha256":"d86c9a8c8bb1e4c66b7b9ec5fc2f85c4859454803e0b86d4918c7134a1725f6d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-01T00:17:16.650943Z","signature_b64":"siMk8JXdT5d5q4B1eh3hCKOV/T/N4k4x2vxpIjFzdx5azsR8KAC0aiYow0XBAC/9x97s0jZjQ4M5r8qP6nylAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0126fd343b74d1d742546c1b4f373361575030033d23e0a4d66f5bae69b0a064","last_reissued_at":"2026-07-01T00:17:16.650405Z","signature_status":"signed_v1","first_computed_at":"2026-07-01T00:17:16.650405Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unveiling Transferability in Trajectory Prediction via Latent Scene Embeddings","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bj\\\"orn Olofsson, David Axelsson, Erik Frisk, Theodor Westny","submitted_at":"2026-06-29T18:07:34Z","abstract_excerpt":"The growing availability of trajectory datasets has fueled major advances in data-driven motion prediction. Yet, models trained on one dataset often fail to generalize beyond their training domain as a result of differences in scene layouts, agent behaviors, and sensing conditions. A framework that learns latent representations of datasets and quantifies their similarity using distributional metrics is presented. This large-scale study covers 24 major datasets, including the most widely used motion-prediction benchmarks, and shows that the resulting transferability scores strongly correlate wi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.30777","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/2606.30777/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":"2606.30777","created_at":"2026-07-01T00:17:16.650481+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.30777v1","created_at":"2026-07-01T00:17:16.650481+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.30777","created_at":"2026-07-01T00:17:16.650481+00:00"},{"alias_kind":"pith_short_12","alias_value":"AETP2NB3OTI5","created_at":"2026-07-01T00:17:16.650481+00:00"},{"alias_kind":"pith_short_16","alias_value":"AETP2NB3OTI5OQSU","created_at":"2026-07-01T00:17:16.650481+00:00"},{"alias_kind":"pith_short_8","alias_value":"AETP2NB3","created_at":"2026-07-01T00:17:16.650481+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/AETP2NB3OTI5OQSUNQNU6NZTMF","json":"https://pith.science/pith/AETP2NB3OTI5OQSUNQNU6NZTMF.json","graph_json":"https://pith.science/api/pith-number/AETP2NB3OTI5OQSUNQNU6NZTMF/graph.json","events_json":"https://pith.science/api/pith-number/AETP2NB3OTI5OQSUNQNU6NZTMF/events.json","paper":"https://pith.science/paper/AETP2NB3"},"agent_actions":{"view_html":"https://pith.science/pith/AETP2NB3OTI5OQSUNQNU6NZTMF","download_json":"https://pith.science/pith/AETP2NB3OTI5OQSUNQNU6NZTMF.json","view_paper":"https://pith.science/paper/AETP2NB3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.30777&json=true","fetch_graph":"https://pith.science/api/pith-number/AETP2NB3OTI5OQSUNQNU6NZTMF/graph.json","fetch_events":"https://pith.science/api/pith-number/AETP2NB3OTI5OQSUNQNU6NZTMF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AETP2NB3OTI5OQSUNQNU6NZTMF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AETP2NB3OTI5OQSUNQNU6NZTMF/action/storage_attestation","attest_author":"https://pith.science/pith/AETP2NB3OTI5OQSUNQNU6NZTMF/action/author_attestation","sign_citation":"https://pith.science/pith/AETP2NB3OTI5OQSUNQNU6NZTMF/action/citation_signature","submit_replication":"https://pith.science/pith/AETP2NB3OTI5OQSUNQNU6NZTMF/action/replication_record"}},"created_at":"2026-07-01T00:17:16.650481+00:00","updated_at":"2026-07-01T00:17:16.650481+00:00"}