{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:3JPOEUXQVUJEUYICFUPGUNC2EN","short_pith_number":"pith:3JPOEUXQ","schema_version":"1.0","canonical_sha256":"da5ee252f0ad124a61022d1e6a345a2351e69853cfea8b851d14cbebc2142cd0","source":{"kind":"arxiv","id":"2506.17213","version":2},"attestation_state":"computed","paper":{"title":"Long-term Traffic Simulation with Interleaved Autoregressive Motion and Scenario Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.RO"],"primary_cat":"cs.CV","authors_text":"Philipp Kr\\\"ahenb\\\"uhl, Shuhan Tan, Xiuyu Yang","submitted_at":"2025-06-20T17:59:21Z","abstract_excerpt":"An ideal traffic simulator replicates the realistic long-term point-to-point trip that a self-driving system experiences during deployment. Prior models and benchmarks focus on closed-loop motion simulation for initial agents in a scene. This is problematic for long-term simulation. Agents enter and exit the scene as the ego vehicle enters new regions. We propose InfGen, a unified next-token prediction model that performs interleaved closed-loop motion simulation and scene generation. InfGen automatically switches between closed-loop motion simulation and scene generation mode. It enables stab"},"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":"2506.17213","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-20T17:59:21Z","cross_cats_sorted":["cs.AI","cs.RO"],"title_canon_sha256":"bb7dd660903c825d816de1329e7526c0bbc4eb3903c2a9437060947d518bab62","abstract_canon_sha256":"20deccb2abcb38200cf8d8c5bac5437792bc4179bec3a4195fc8f0063df948c8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:48:26.871864Z","signature_b64":"fGtVtOCDLJcudOp4txZpMnB6FMvCol/YSC45qAISrL+jgKerb+cYnWgXZtsvMPMZpgj7zRfOP7WgKk4UG+BlBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"da5ee252f0ad124a61022d1e6a345a2351e69853cfea8b851d14cbebc2142cd0","last_reissued_at":"2026-07-05T11:48:26.871274Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:48:26.871274Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Long-term Traffic Simulation with Interleaved Autoregressive Motion and Scenario Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.RO"],"primary_cat":"cs.CV","authors_text":"Philipp Kr\\\"ahenb\\\"uhl, Shuhan Tan, Xiuyu Yang","submitted_at":"2025-06-20T17:59:21Z","abstract_excerpt":"An ideal traffic simulator replicates the realistic long-term point-to-point trip that a self-driving system experiences during deployment. Prior models and benchmarks focus on closed-loop motion simulation for initial agents in a scene. This is problematic for long-term simulation. Agents enter and exit the scene as the ego vehicle enters new regions. We propose InfGen, a unified next-token prediction model that performs interleaved closed-loop motion simulation and scene generation. InfGen automatically switches between closed-loop motion simulation and scene generation mode. It enables stab"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.17213","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/2506.17213/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":"2506.17213","created_at":"2026-07-05T11:48:26.871348+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.17213v2","created_at":"2026-07-05T11:48:26.871348+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.17213","created_at":"2026-07-05T11:48:26.871348+00:00"},{"alias_kind":"pith_short_12","alias_value":"3JPOEUXQVUJE","created_at":"2026-07-05T11:48:26.871348+00:00"},{"alias_kind":"pith_short_16","alias_value":"3JPOEUXQVUJEUYIC","created_at":"2026-07-05T11:48:26.871348+00:00"},{"alias_kind":"pith_short_8","alias_value":"3JPOEUXQ","created_at":"2026-07-05T11:48:26.871348+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/3JPOEUXQVUJEUYICFUPGUNC2EN","json":"https://pith.science/pith/3JPOEUXQVUJEUYICFUPGUNC2EN.json","graph_json":"https://pith.science/api/pith-number/3JPOEUXQVUJEUYICFUPGUNC2EN/graph.json","events_json":"https://pith.science/api/pith-number/3JPOEUXQVUJEUYICFUPGUNC2EN/events.json","paper":"https://pith.science/paper/3JPOEUXQ"},"agent_actions":{"view_html":"https://pith.science/pith/3JPOEUXQVUJEUYICFUPGUNC2EN","download_json":"https://pith.science/pith/3JPOEUXQVUJEUYICFUPGUNC2EN.json","view_paper":"https://pith.science/paper/3JPOEUXQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.17213&json=true","fetch_graph":"https://pith.science/api/pith-number/3JPOEUXQVUJEUYICFUPGUNC2EN/graph.json","fetch_events":"https://pith.science/api/pith-number/3JPOEUXQVUJEUYICFUPGUNC2EN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3JPOEUXQVUJEUYICFUPGUNC2EN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3JPOEUXQVUJEUYICFUPGUNC2EN/action/storage_attestation","attest_author":"https://pith.science/pith/3JPOEUXQVUJEUYICFUPGUNC2EN/action/author_attestation","sign_citation":"https://pith.science/pith/3JPOEUXQVUJEUYICFUPGUNC2EN/action/citation_signature","submit_replication":"https://pith.science/pith/3JPOEUXQVUJEUYICFUPGUNC2EN/action/replication_record"}},"created_at":"2026-07-05T11:48:26.871348+00:00","updated_at":"2026-07-05T11:48:26.871348+00:00"}