{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:QDNR5Q65FSU7USKVKEIITU5QIM","short_pith_number":"pith:QDNR5Q65","canonical_record":{"source":{"id":"2502.19908","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-02-27T09:26:22Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"8a334e9ec31345d437840ad6bfbac5e334ce30d7bab37428361ec253327cc131","abstract_canon_sha256":"1dec9c47e10499a4992cefec66a202fe56d0a2f448b41b2a865cbfb947e550d8"},"schema_version":"1.0"},"canonical_sha256":"80db1ec3dd2ca9fa4955511089d3b043216546bbd508f9a340a54d785858fbe9","source":{"kind":"arxiv","id":"2502.19908","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.19908","created_at":"2026-07-05T10:38:23Z"},{"alias_kind":"arxiv_version","alias_value":"2502.19908v3","created_at":"2026-07-05T10:38:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.19908","created_at":"2026-07-05T10:38:23Z"},{"alias_kind":"pith_short_12","alias_value":"QDNR5Q65FSU7","created_at":"2026-07-05T10:38:23Z"},{"alias_kind":"pith_short_16","alias_value":"QDNR5Q65FSU7USKV","created_at":"2026-07-05T10:38:23Z"},{"alias_kind":"pith_short_8","alias_value":"QDNR5Q65","created_at":"2026-07-05T10:38:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:QDNR5Q65FSU7USKVKEIITU5QIM","target":"record","payload":{"canonical_record":{"source":{"id":"2502.19908","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-02-27T09:26:22Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"8a334e9ec31345d437840ad6bfbac5e334ce30d7bab37428361ec253327cc131","abstract_canon_sha256":"1dec9c47e10499a4992cefec66a202fe56d0a2f448b41b2a865cbfb947e550d8"},"schema_version":"1.0"},"canonical_sha256":"80db1ec3dd2ca9fa4955511089d3b043216546bbd508f9a340a54d785858fbe9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:38:23.062246Z","signature_b64":"+Fk4m8KzoNhPgiXISepGRUaPCM/AkzijaHtzOme+J+HlHI6t3fZaj3r4mzLRL8SjulcA2pKGydZMseufFaJzAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"80db1ec3dd2ca9fa4955511089d3b043216546bbd508f9a340a54d785858fbe9","last_reissued_at":"2026-07-05T10:38:23.061681Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:38:23.061681Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.19908","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":"2026-07-05T10:38:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Us19SxGvZ87gzXGxID+TxROU66PrQAo+SSxRiZxIgtbJOA6V22FzTC66nUku6aCsYs7xOOB8fYiZOrbubx3+Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T01:21:58.925074Z"},"content_sha256":"ea2e99f77b010260b81a6df8acefe79d3136244e53575f4caa76dfa6fc89ed7e","schema_version":"1.0","event_id":"sha256:ea2e99f77b010260b81a6df8acefe79d3136244e53575f4caa76dfa6fc89ed7e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:QDNR5Q65FSU7USKVKEIITU5QIM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CarPlanner: Consistent Auto-regressive Trajectory Planning for Large-scale Reinforcement Learning in Autonomous Driving","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Dongkun Zhang, Jiaming Liang, Ke Guo, Qi Wang, Rong Xiong, Sha Lu, Yue Wang, Zhenwei Miao","submitted_at":"2025-02-27T09:26:22Z","abstract_excerpt":"Trajectory planning is vital for autonomous driving, ensuring safe and efficient navigation in complex environments. While recent learning-based methods, particularly reinforcement learning (RL), have shown promise in specific scenarios, RL planners struggle with training inefficiencies and managing large-scale, real-world driving scenarios. In this paper, we introduce \\textbf{CarPlanner}, a \\textbf{C}onsistent \\textbf{a}uto-\\textbf{r}egressive \\textbf{Planner} that uses RL to generate multi-modal trajectories. The auto-regressive structure enables efficient large-scale RL training, while the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.19908","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/2502.19908/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":"2026-07-05T10:38:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6NcRAs9fn/3PU3Qf2B7mSPsh4F7DrVixzho6lePKF7ZkpJm/wGIebqxftlwHxIVaDh6w2R0IRgAx5On1UfnkCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T01:21:58.925784Z"},"content_sha256":"6fc85998da6e409dbf0012d847999b19bf3e3a21bacf62045e3ef8c61ed86829","schema_version":"1.0","event_id":"sha256:6fc85998da6e409dbf0012d847999b19bf3e3a21bacf62045e3ef8c61ed86829"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QDNR5Q65FSU7USKVKEIITU5QIM/bundle.json","state_url":"https://pith.science/pith/QDNR5Q65FSU7USKVKEIITU5QIM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QDNR5Q65FSU7USKVKEIITU5QIM/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":"2026-08-06T01:21:58Z","links":{"resolver":"https://pith.science/pith/QDNR5Q65FSU7USKVKEIITU5QIM","bundle":"https://pith.science/pith/QDNR5Q65FSU7USKVKEIITU5QIM/bundle.json","state":"https://pith.science/pith/QDNR5Q65FSU7USKVKEIITU5QIM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QDNR5Q65FSU7USKVKEIITU5QIM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:QDNR5Q65FSU7USKVKEIITU5QIM","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":"1dec9c47e10499a4992cefec66a202fe56d0a2f448b41b2a865cbfb947e550d8","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-02-27T09:26:22Z","title_canon_sha256":"8a334e9ec31345d437840ad6bfbac5e334ce30d7bab37428361ec253327cc131"},"schema_version":"1.0","source":{"id":"2502.19908","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.19908","created_at":"2026-07-05T10:38:23Z"},{"alias_kind":"arxiv_version","alias_value":"2502.19908v3","created_at":"2026-07-05T10:38:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.19908","created_at":"2026-07-05T10:38:23Z"},{"alias_kind":"pith_short_12","alias_value":"QDNR5Q65FSU7","created_at":"2026-07-05T10:38:23Z"},{"alias_kind":"pith_short_16","alias_value":"QDNR5Q65FSU7USKV","created_at":"2026-07-05T10:38:23Z"},{"alias_kind":"pith_short_8","alias_value":"QDNR5Q65","created_at":"2026-07-05T10:38:23Z"}],"graph_snapshots":[{"event_id":"sha256:6fc85998da6e409dbf0012d847999b19bf3e3a21bacf62045e3ef8c61ed86829","target":"graph","created_at":"2026-07-05T10:38:23Z","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/2502.19908/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Trajectory planning is vital for autonomous driving, ensuring safe and efficient navigation in complex environments. While recent learning-based methods, particularly reinforcement learning (RL), have shown promise in specific scenarios, RL planners struggle with training inefficiencies and managing large-scale, real-world driving scenarios. In this paper, we introduce \\textbf{CarPlanner}, a \\textbf{C}onsistent \\textbf{a}uto-\\textbf{r}egressive \\textbf{Planner} that uses RL to generate multi-modal trajectories. The auto-regressive structure enables efficient large-scale RL training, while the ","authors_text":"Dongkun Zhang, Jiaming Liang, Ke Guo, Qi Wang, Rong Xiong, Sha Lu, Yue Wang, Zhenwei Miao","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-02-27T09:26:22Z","title":"CarPlanner: Consistent Auto-regressive Trajectory Planning for Large-scale Reinforcement Learning in Autonomous Driving"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.19908","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:ea2e99f77b010260b81a6df8acefe79d3136244e53575f4caa76dfa6fc89ed7e","target":"record","created_at":"2026-07-05T10:38:23Z","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":"1dec9c47e10499a4992cefec66a202fe56d0a2f448b41b2a865cbfb947e550d8","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-02-27T09:26:22Z","title_canon_sha256":"8a334e9ec31345d437840ad6bfbac5e334ce30d7bab37428361ec253327cc131"},"schema_version":"1.0","source":{"id":"2502.19908","kind":"arxiv","version":3}},"canonical_sha256":"80db1ec3dd2ca9fa4955511089d3b043216546bbd508f9a340a54d785858fbe9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"80db1ec3dd2ca9fa4955511089d3b043216546bbd508f9a340a54d785858fbe9","first_computed_at":"2026-07-05T10:38:23.061681Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:38:23.061681Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+Fk4m8KzoNhPgiXISepGRUaPCM/AkzijaHtzOme+J+HlHI6t3fZaj3r4mzLRL8SjulcA2pKGydZMseufFaJzAw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:38:23.062246Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.19908","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ea2e99f77b010260b81a6df8acefe79d3136244e53575f4caa76dfa6fc89ed7e","sha256:6fc85998da6e409dbf0012d847999b19bf3e3a21bacf62045e3ef8c61ed86829"],"state_sha256":"4512fa006d3fe9487241210cff5857cf2617ff783dcd24c5c69b19f905a71b4d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ejZc5VfP27HVRKrLHqZDNbMiPzMCDlQc6dPll3oM4FbVqNermutGsGY1vTlshKfirCcQBcdxOatnOXv1lBvmBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T01:21:58.931646Z","bundle_sha256":"c5ef2948056f8e3684c325d78f7a7c8702c58bace9871b6ae781b7d9e9c48d90"}}