{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:CCFTK2HXTPH2VNMLAXMWIXLOV2","short_pith_number":"pith:CCFTK2HX","canonical_record":{"source":{"id":"2504.05422","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-07T18:45:49Z","cross_cats_sorted":["cs.LG","cs.RO"],"title_canon_sha256":"b06e37b9f501c607e688f6a322379a7e4bcd4c97388c48c228e8c1384e1220c7","abstract_canon_sha256":"abb8b985c796b84e0f7edbbf535e75d1477bf0587dfa54bf2a994096fb9f17f5"},"schema_version":"1.0"},"canonical_sha256":"108b3568f79bcfaab58b05d9645d6eaea53d2f9de4934b4256117258984aca6b","source":{"kind":"arxiv","id":"2504.05422","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.05422","created_at":"2026-07-05T11:45:57Z"},{"alias_kind":"arxiv_version","alias_value":"2504.05422v3","created_at":"2026-07-05T11:45:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.05422","created_at":"2026-07-05T11:45:57Z"},{"alias_kind":"pith_short_12","alias_value":"CCFTK2HXTPH2","created_at":"2026-07-05T11:45:57Z"},{"alias_kind":"pith_short_16","alias_value":"CCFTK2HXTPH2VNML","created_at":"2026-07-05T11:45:57Z"},{"alias_kind":"pith_short_8","alias_value":"CCFTK2HX","created_at":"2026-07-05T11:45:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:CCFTK2HXTPH2VNMLAXMWIXLOV2","target":"record","payload":{"canonical_record":{"source":{"id":"2504.05422","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-07T18:45:49Z","cross_cats_sorted":["cs.LG","cs.RO"],"title_canon_sha256":"b06e37b9f501c607e688f6a322379a7e4bcd4c97388c48c228e8c1384e1220c7","abstract_canon_sha256":"abb8b985c796b84e0f7edbbf535e75d1477bf0587dfa54bf2a994096fb9f17f5"},"schema_version":"1.0"},"canonical_sha256":"108b3568f79bcfaab58b05d9645d6eaea53d2f9de4934b4256117258984aca6b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:45:57.169371Z","signature_b64":"mNrt/x1hOpd3zn8I8+G5KC06BKhRZsCDGbvupbhtK+z93O0u/0G8u1kVUPMWYDZKCyFIzXqLT5s6MSw8W6sQCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"108b3568f79bcfaab58b05d9645d6eaea53d2f9de4934b4256117258984aca6b","last_reissued_at":"2026-07-05T11:45:57.168845Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:45:57.168845Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.05422","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:45:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EkjfSobDi08jrDZXGdSxxFAyRPE6TuHONJRs2WxXWX18Sgxx3un2MxclYzYpho21EiZVR8WPs2B62D0zYXPvCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T03:44:00.508200Z"},"content_sha256":"4334f1e9a57f3783364f441470a58e078a98776986cac17a913f45c9aca12c70","schema_version":"1.0","event_id":"sha256:4334f1e9a57f3783364f441470a58e078a98776986cac17a913f45c9aca12c70"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:CCFTK2HXTPH2VNMLAXMWIXLOV2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"EP-Diffuser: An Efficient Diffusion Model for Traffic Scene Generation and Prediction via Polynomial Representations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Daniel Goehring, Joerg Reichardt, Mohamed-Khalil Bouzidi, Yue Yao","submitted_at":"2025-04-07T18:45:49Z","abstract_excerpt":"As the prediction horizon increases, predicting the future evolution of traffic scenes becomes increasingly difficult due to the multi-modal nature of agent motion. Most state-of-the-art (SotA) prediction models primarily focus on forecasting the most likely future. However, for the safe operation of autonomous vehicles, it is equally important to cover the distribution for plausible motion alternatives. To address this, we introduce EP-Diffuser, a novel parameter-efficient diffusion-based generative model designed to capture the distribution of possible traffic scene evolutions. Conditioned o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.05422","kind":"arxiv","version":3},"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/2504.05422/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:45:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Kjf0umvLEfPx3E5SYzDKSd3S20FMyGMtloYklbXAiYJduDmjpHkTmRHXDVqv/d2AiBeqqGejQMRrXbNRoj+7AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T03:44:00.508695Z"},"content_sha256":"a1f2b848ca998ae037c5919204e8b02486eceade99fa14eaa64156db88c97910","schema_version":"1.0","event_id":"sha256:a1f2b848ca998ae037c5919204e8b02486eceade99fa14eaa64156db88c97910"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CCFTK2HXTPH2VNMLAXMWIXLOV2/bundle.json","state_url":"https://pith.science/pith/CCFTK2HXTPH2VNMLAXMWIXLOV2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CCFTK2HXTPH2VNMLAXMWIXLOV2/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-22T03:44:00Z","links":{"resolver":"https://pith.science/pith/CCFTK2HXTPH2VNMLAXMWIXLOV2","bundle":"https://pith.science/pith/CCFTK2HXTPH2VNMLAXMWIXLOV2/bundle.json","state":"https://pith.science/pith/CCFTK2HXTPH2VNMLAXMWIXLOV2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CCFTK2HXTPH2VNMLAXMWIXLOV2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:CCFTK2HXTPH2VNMLAXMWIXLOV2","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"abb8b985c796b84e0f7edbbf535e75d1477bf0587dfa54bf2a994096fb9f17f5","cross_cats_sorted":["cs.LG","cs.RO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-07T18:45:49Z","title_canon_sha256":"b06e37b9f501c607e688f6a322379a7e4bcd4c97388c48c228e8c1384e1220c7"},"schema_version":"1.0","source":{"id":"2504.05422","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.05422","created_at":"2026-07-05T11:45:57Z"},{"alias_kind":"arxiv_version","alias_value":"2504.05422v3","created_at":"2026-07-05T11:45:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.05422","created_at":"2026-07-05T11:45:57Z"},{"alias_kind":"pith_short_12","alias_value":"CCFTK2HXTPH2","created_at":"2026-07-05T11:45:57Z"},{"alias_kind":"pith_short_16","alias_value":"CCFTK2HXTPH2VNML","created_at":"2026-07-05T11:45:57Z"},{"alias_kind":"pith_short_8","alias_value":"CCFTK2HX","created_at":"2026-07-05T11:45:57Z"}],"graph_snapshots":[{"event_id":"sha256:a1f2b848ca998ae037c5919204e8b02486eceade99fa14eaa64156db88c97910","target":"graph","created_at":"2026-07-05T11:45:57Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2504.05422/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"As the prediction horizon increases, predicting the future evolution of traffic scenes becomes increasingly difficult due to the multi-modal nature of agent motion. Most state-of-the-art (SotA) prediction models primarily focus on forecasting the most likely future. However, for the safe operation of autonomous vehicles, it is equally important to cover the distribution for plausible motion alternatives. To address this, we introduce EP-Diffuser, a novel parameter-efficient diffusion-based generative model designed to capture the distribution of possible traffic scene evolutions. Conditioned o","authors_text":"Daniel Goehring, Joerg Reichardt, Mohamed-Khalil Bouzidi, Yue Yao","cross_cats":["cs.LG","cs.RO"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-07T18:45:49Z","title":"EP-Diffuser: An Efficient Diffusion Model for Traffic Scene Generation and Prediction via Polynomial Representations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.05422","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4334f1e9a57f3783364f441470a58e078a98776986cac17a913f45c9aca12c70","target":"record","created_at":"2026-07-05T11:45:57Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"abb8b985c796b84e0f7edbbf535e75d1477bf0587dfa54bf2a994096fb9f17f5","cross_cats_sorted":["cs.LG","cs.RO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-07T18:45:49Z","title_canon_sha256":"b06e37b9f501c607e688f6a322379a7e4bcd4c97388c48c228e8c1384e1220c7"},"schema_version":"1.0","source":{"id":"2504.05422","kind":"arxiv","version":3}},"canonical_sha256":"108b3568f79bcfaab58b05d9645d6eaea53d2f9de4934b4256117258984aca6b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"108b3568f79bcfaab58b05d9645d6eaea53d2f9de4934b4256117258984aca6b","first_computed_at":"2026-07-05T11:45:57.168845Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:45:57.168845Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mNrt/x1hOpd3zn8I8+G5KC06BKhRZsCDGbvupbhtK+z93O0u/0G8u1kVUPMWYDZKCyFIzXqLT5s6MSw8W6sQCg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:45:57.169371Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.05422","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4334f1e9a57f3783364f441470a58e078a98776986cac17a913f45c9aca12c70","sha256:a1f2b848ca998ae037c5919204e8b02486eceade99fa14eaa64156db88c97910"],"state_sha256":"0cb6b436ef87b87c90d8232782a74743e846c9d00eb2d1a9820ebd576ec4bf16"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nhmglTQsMv+eJ4iijXIPwaUEyRB25X2kT7egrk7wNoPjzLlSJV+v7/tIIMHsx+WAMjtfQekQbivdLLVGKB7WAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T03:44:00.512528Z","bundle_sha256":"f17f07eca6d0f33b64ba4a8556dad70ae02235d75aa1c67a7e0c61c298237f66"}}