{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MBYR3U6QAEMQMNUFKVD4V6CAJE","short_pith_number":"pith:MBYR3U6Q","schema_version":"1.0","canonical_sha256":"60711dd3d001190636855547caf84049228b459832bb0e16f970803ab771b210","source":{"kind":"arxiv","id":"2408.13890","version":1},"attestation_state":"computed","paper":{"title":"Making Large Language Models Better Planners with Reasoning-Decision Alignment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guangrun Wang, Lin Ma, Shaoxiang Chen, Sihao Lin, Tao Tang, Xiaodan Liang, Zequn Jie, Zhijian Huang","submitted_at":"2024-08-25T16:43:47Z","abstract_excerpt":"Data-driven approaches for autonomous driving (AD) have been widely adopted in the past decade but are confronted with dataset bias and uninterpretability. Inspired by the knowledge-driven nature of human driving, recent approaches explore the potential of large language models (LLMs) to improve understanding and decision-making in traffic scenarios. They find that the pretrain-finetune paradigm of LLMs on downstream data with the Chain-of-Thought (CoT) reasoning process can enhance explainability and scene understanding. However, such a popular strategy proves to suffer from the notorious pro"},"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":"2408.13890","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-08-25T16:43:47Z","cross_cats_sorted":[],"title_canon_sha256":"a20674e18e9c09d13070d26392aa69e78739167c9891dd0f628715f05bd486ca","abstract_canon_sha256":"cc97d22b7cf9831726486bab75322719a0ce02ce507eeac58510b8637b504f12"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:59:13.458996Z","signature_b64":"yLdBcHR9MvkjvgIVsyN1FyscU04SfhUUtxyMto0Wdfl9VIle9cdIZG6/2ohG/JzQenxGV39X4AgMCWzB7ZjRCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"60711dd3d001190636855547caf84049228b459832bb0e16f970803ab771b210","last_reissued_at":"2026-07-05T08:59:13.458567Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:59:13.458567Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Making Large Language Models Better Planners with Reasoning-Decision Alignment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guangrun Wang, Lin Ma, Shaoxiang Chen, Sihao Lin, Tao Tang, Xiaodan Liang, Zequn Jie, Zhijian Huang","submitted_at":"2024-08-25T16:43:47Z","abstract_excerpt":"Data-driven approaches for autonomous driving (AD) have been widely adopted in the past decade but are confronted with dataset bias and uninterpretability. Inspired by the knowledge-driven nature of human driving, recent approaches explore the potential of large language models (LLMs) to improve understanding and decision-making in traffic scenarios. They find that the pretrain-finetune paradigm of LLMs on downstream data with the Chain-of-Thought (CoT) reasoning process can enhance explainability and scene understanding. However, such a popular strategy proves to suffer from the notorious pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.13890","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/2408.13890/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":"2408.13890","created_at":"2026-07-05T08:59:13.458626+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.13890v1","created_at":"2026-07-05T08:59:13.458626+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.13890","created_at":"2026-07-05T08:59:13.458626+00:00"},{"alias_kind":"pith_short_12","alias_value":"MBYR3U6QAEMQ","created_at":"2026-07-05T08:59:13.458626+00:00"},{"alias_kind":"pith_short_16","alias_value":"MBYR3U6QAEMQMNUF","created_at":"2026-07-05T08:59:13.458626+00:00"},{"alias_kind":"pith_short_8","alias_value":"MBYR3U6Q","created_at":"2026-07-05T08:59:13.458626+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.19318","citing_title":"MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MBYR3U6QAEMQMNUFKVD4V6CAJE","json":"https://pith.science/pith/MBYR3U6QAEMQMNUFKVD4V6CAJE.json","graph_json":"https://pith.science/api/pith-number/MBYR3U6QAEMQMNUFKVD4V6CAJE/graph.json","events_json":"https://pith.science/api/pith-number/MBYR3U6QAEMQMNUFKVD4V6CAJE/events.json","paper":"https://pith.science/paper/MBYR3U6Q"},"agent_actions":{"view_html":"https://pith.science/pith/MBYR3U6QAEMQMNUFKVD4V6CAJE","download_json":"https://pith.science/pith/MBYR3U6QAEMQMNUFKVD4V6CAJE.json","view_paper":"https://pith.science/paper/MBYR3U6Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.13890&json=true","fetch_graph":"https://pith.science/api/pith-number/MBYR3U6QAEMQMNUFKVD4V6CAJE/graph.json","fetch_events":"https://pith.science/api/pith-number/MBYR3U6QAEMQMNUFKVD4V6CAJE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MBYR3U6QAEMQMNUFKVD4V6CAJE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MBYR3U6QAEMQMNUFKVD4V6CAJE/action/storage_attestation","attest_author":"https://pith.science/pith/MBYR3U6QAEMQMNUFKVD4V6CAJE/action/author_attestation","sign_citation":"https://pith.science/pith/MBYR3U6QAEMQMNUFKVD4V6CAJE/action/citation_signature","submit_replication":"https://pith.science/pith/MBYR3U6QAEMQMNUFKVD4V6CAJE/action/replication_record"}},"created_at":"2026-07-05T08:59:13.458626+00:00","updated_at":"2026-07-05T08:59:13.458626+00:00"}