{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:E6YJ66DA5YUACN2QJMDUK6Q7KG","short_pith_number":"pith:E6YJ66DA","canonical_record":{"source":{"id":"2501.13072","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-01-22T18:34:51Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"88881b3a4dab920345b7499ca8514d7ae488411462bacb37198ac3419b9cdf37","abstract_canon_sha256":"bd39a0002ed5ed5242107f390f8acaf6a278c76ad4c5328376f10e469e535e5a"},"schema_version":"1.0"},"canonical_sha256":"27b09f7860ee280137504b07457a1f51a7a505c7e3b1e845bc594ee1ce16df7a","source":{"kind":"arxiv","id":"2501.13072","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.13072","created_at":"2026-07-05T10:04:23Z"},{"alias_kind":"arxiv_version","alias_value":"2501.13072v2","created_at":"2026-07-05T10:04:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.13072","created_at":"2026-07-05T10:04:23Z"},{"alias_kind":"pith_short_12","alias_value":"E6YJ66DA5YUA","created_at":"2026-07-05T10:04:23Z"},{"alias_kind":"pith_short_16","alias_value":"E6YJ66DA5YUACN2Q","created_at":"2026-07-05T10:04:23Z"},{"alias_kind":"pith_short_8","alias_value":"E6YJ66DA","created_at":"2026-07-05T10:04:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:E6YJ66DA5YUACN2QJMDUK6Q7KG","target":"record","payload":{"canonical_record":{"source":{"id":"2501.13072","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-01-22T18:34:51Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"88881b3a4dab920345b7499ca8514d7ae488411462bacb37198ac3419b9cdf37","abstract_canon_sha256":"bd39a0002ed5ed5242107f390f8acaf6a278c76ad4c5328376f10e469e535e5a"},"schema_version":"1.0"},"canonical_sha256":"27b09f7860ee280137504b07457a1f51a7a505c7e3b1e845bc594ee1ce16df7a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:04:23.373830Z","signature_b64":"j8gafDEVfKmQze2BwH4roGkSBs9OLq8PN6M8eDJfgJIeemEqw7rzaQWFhygMN+Y3E+Ic5KMsUMrbqGiYxJ2vAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"27b09f7860ee280137504b07457a1f51a7a505c7e3b1e845bc594ee1ce16df7a","last_reissued_at":"2026-07-05T10:04:23.373215Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:04:23.373215Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.13072","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:04:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7o72ABbp7g6kBcyMS1m+1fhn5DQYLmeHHJ6Azz0yr1SlYZtA2c/m1XvaUkzY6b6s8UY8yR+g3Uw9i8D2H9dkCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T21:25:02.328532Z"},"content_sha256":"bed3433cd60409aec55e26fde842041f62e79098bcc256bf00824bcbb3bb3f65","schema_version":"1.0","event_id":"sha256:bed3433cd60409aec55e26fde842041f62e79098bcc256bf00824bcbb3bb3f65"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:E6YJ66DA5YUACN2QJMDUK6Q7KG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AdaWM: Adaptive World Model based Planning for Autonomous Driving","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Abhirup Mallik, Burhaneddin Yaman, Chenbin Pan, Feng Tao, Hang Wang, Junshan Zhang, Liu Ren, Xin Ye","submitted_at":"2025-01-22T18:34:51Z","abstract_excerpt":"World model based reinforcement learning (RL) has emerged as a promising approach for autonomous driving, which learns a latent dynamics model and uses it to train a planning policy. To speed up the learning process, the pretrain-finetune paradigm is often used, where online RL is initialized by a pretrained model and a policy learned offline. However, naively performing such initialization in RL may result in dramatic performance degradation during the online interactions in the new task. To tackle this challenge, we first analyze the performance degradation and identify two primary root caus"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.13072","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/2501.13072/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":"2026-07-05T10:04:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wxW+o/6jyzhImYoDXdUoTAOYq7s86+LTSCTC/csfu8X845uC68lIl57JxZrQpv3s8NQGbp9DoaeQ6rzXXl00Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T21:25:02.329477Z"},"content_sha256":"886453bad479ad8f5f393cad1915d71e1ba870abc6192e71db4d469d8adc7a13","schema_version":"1.0","event_id":"sha256:886453bad479ad8f5f393cad1915d71e1ba870abc6192e71db4d469d8adc7a13"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/E6YJ66DA5YUACN2QJMDUK6Q7KG/bundle.json","state_url":"https://pith.science/pith/E6YJ66DA5YUACN2QJMDUK6Q7KG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/E6YJ66DA5YUACN2QJMDUK6Q7KG/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":"2026-08-09T21:25:02Z","links":{"resolver":"https://pith.science/pith/E6YJ66DA5YUACN2QJMDUK6Q7KG","bundle":"https://pith.science/pith/E6YJ66DA5YUACN2QJMDUK6Q7KG/bundle.json","state":"https://pith.science/pith/E6YJ66DA5YUACN2QJMDUK6Q7KG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/E6YJ66DA5YUACN2QJMDUK6Q7KG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:E6YJ66DA5YUACN2QJMDUK6Q7KG","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":"bd39a0002ed5ed5242107f390f8acaf6a278c76ad4c5328376f10e469e535e5a","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-01-22T18:34:51Z","title_canon_sha256":"88881b3a4dab920345b7499ca8514d7ae488411462bacb37198ac3419b9cdf37"},"schema_version":"1.0","source":{"id":"2501.13072","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.13072","created_at":"2026-07-05T10:04:23Z"},{"alias_kind":"arxiv_version","alias_value":"2501.13072v2","created_at":"2026-07-05T10:04:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.13072","created_at":"2026-07-05T10:04:23Z"},{"alias_kind":"pith_short_12","alias_value":"E6YJ66DA5YUA","created_at":"2026-07-05T10:04:23Z"},{"alias_kind":"pith_short_16","alias_value":"E6YJ66DA5YUACN2Q","created_at":"2026-07-05T10:04:23Z"},{"alias_kind":"pith_short_8","alias_value":"E6YJ66DA","created_at":"2026-07-05T10:04:23Z"}],"graph_snapshots":[{"event_id":"sha256:886453bad479ad8f5f393cad1915d71e1ba870abc6192e71db4d469d8adc7a13","target":"graph","created_at":"2026-07-05T10:04:23Z","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/2501.13072/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"World model based reinforcement learning (RL) has emerged as a promising approach for autonomous driving, which learns a latent dynamics model and uses it to train a planning policy. To speed up the learning process, the pretrain-finetune paradigm is often used, where online RL is initialized by a pretrained model and a policy learned offline. However, naively performing such initialization in RL may result in dramatic performance degradation during the online interactions in the new task. To tackle this challenge, we first analyze the performance degradation and identify two primary root caus","authors_text":"Abhirup Mallik, Burhaneddin Yaman, Chenbin Pan, Feng Tao, Hang Wang, Junshan Zhang, Liu Ren, Xin Ye","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-01-22T18:34:51Z","title":"AdaWM: Adaptive World Model based Planning for Autonomous Driving"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.13072","kind":"arxiv","version":2},"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:bed3433cd60409aec55e26fde842041f62e79098bcc256bf00824bcbb3bb3f65","target":"record","created_at":"2026-07-05T10:04:23Z","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":"bd39a0002ed5ed5242107f390f8acaf6a278c76ad4c5328376f10e469e535e5a","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-01-22T18:34:51Z","title_canon_sha256":"88881b3a4dab920345b7499ca8514d7ae488411462bacb37198ac3419b9cdf37"},"schema_version":"1.0","source":{"id":"2501.13072","kind":"arxiv","version":2}},"canonical_sha256":"27b09f7860ee280137504b07457a1f51a7a505c7e3b1e845bc594ee1ce16df7a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"27b09f7860ee280137504b07457a1f51a7a505c7e3b1e845bc594ee1ce16df7a","first_computed_at":"2026-07-05T10:04:23.373215Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:04:23.373215Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"j8gafDEVfKmQze2BwH4roGkSBs9OLq8PN6M8eDJfgJIeemEqw7rzaQWFhygMN+Y3E+Ic5KMsUMrbqGiYxJ2vAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:04:23.373830Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.13072","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bed3433cd60409aec55e26fde842041f62e79098bcc256bf00824bcbb3bb3f65","sha256:886453bad479ad8f5f393cad1915d71e1ba870abc6192e71db4d469d8adc7a13"],"state_sha256":"a9d462af4a20ca70389298550a778f20bcf5938f35a68e18666328670ee6755b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GhEGxfpaQne2BL8H6zEhhTWsE+58+SKBRkSXlf5VSs/bmLfCPvi8EU+X6FYP91fTPoF3DGv0X0JTvy3vh1mUCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T21:25:02.337023Z","bundle_sha256":"fae3fe450e1e3180d5b3fbd2a5d864023c32a82e8c35f700bd5ad095c9d03cd5"}}