{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5LJABTGJ7K3BFTPVTGXJZACZCJ","short_pith_number":"pith:5LJABTGJ","schema_version":"1.0","canonical_sha256":"ead200ccc9fab612cdf599ae9c8059126dfb74b1d9eb8331e18424a25620f90e","source":{"kind":"arxiv","id":"2503.22020","version":1},"attestation_state":"computed","paper":{"title":"CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Adding explicit visual chain-of-thought by predicting future image frames before actions improves vision-language-action model performance on complex robot tasks.","cross_cats":["cs.AI","cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Ankur Handa, Chelsea Finn, Donglai Xiang, Gordon Wetzstein, Ming-Yu Liu, Moo Jin Kim, Qianli Ma, Qingqing Zhao, Song Han, Tsung-Yi Lin, Yao Lu, Yecheng Wu, Zhaoshuo Li, Zhuoyang Zhang, Zipeng Fu","submitted_at":"2025-03-27T22:23:04Z","abstract_excerpt":"Vision-language-action models (VLAs) have shown potential in leveraging pretrained vision-language models and diverse robot demonstrations for learning generalizable sensorimotor control. While this paradigm effectively utilizes large-scale data from both robotic and non-robotic sources, current VLAs primarily focus on direct input--output mappings, lacking the intermediate reasoning steps crucial for complex manipulation tasks. As a result, existing VLAs lack temporal planning or reasoning capabilities. In this paper, we introduce a method that incorporates explicit visual chain-of-thought (C"},"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":true,"formal_links_present":true},"canonical_record":{"source":{"id":"2503.22020","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-27T22:23:04Z","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"title_canon_sha256":"dd802464cc0fdd008ca9fd5ca53edf693e63979868d8545f80321fe3dc1c1b6c","abstract_canon_sha256":"7a75950fedc7665a116cbc335d63c8dd7bb2ed11a6f217ef1d118cc4831d38cd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:48.986650Z","signature_b64":"Vrty8zIArtoQ96oDqqb4XYdQuNZtMNrXuPv+FeyuaFnQ5qPcaJWZktfikN+CmKnpwHGjQ3iy20q4RFWPbbwUDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ead200ccc9fab612cdf599ae9c8059126dfb74b1d9eb8331e18424a25620f90e","last_reissued_at":"2026-05-17T23:38:48.986037Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:48.986037Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Adding explicit visual chain-of-thought by predicting future image frames before actions improves vision-language-action model performance on complex robot tasks.","cross_cats":["cs.AI","cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Ankur Handa, Chelsea Finn, Donglai Xiang, Gordon Wetzstein, Ming-Yu Liu, Moo Jin Kim, Qianli Ma, Qingqing Zhao, Song Han, Tsung-Yi Lin, Yao Lu, Yecheng Wu, Zhaoshuo Li, Zhuoyang Zhang, Zipeng Fu","submitted_at":"2025-03-27T22:23:04Z","abstract_excerpt":"Vision-language-action models (VLAs) have shown potential in leveraging pretrained vision-language models and diverse robot demonstrations for learning generalizable sensorimotor control. While this paradigm effectively utilizes large-scale data from both robotic and non-robotic sources, current VLAs primarily focus on direct input--output mappings, lacking the intermediate reasoning steps crucial for complex manipulation tasks. As a result, existing VLAs lack temporal planning or reasoning capabilities. In this paper, we introduce a method that incorporates explicit visual chain-of-thought (C"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our experimental results demonstrate that CoT-VLA achieves strong performance, outperforming the state-of-the-art VLA model by 17% in real-world manipulation tasks and 6% in simulation benchmarks.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That autoregressive prediction of future image frames produces reliable visual goals that meaningfully improve downstream action generation for complex manipulation tasks.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"CoT-VLA is a 7B VLA that generates future visual frames autoregressively as planning goals before actions, outperforming prior VLAs by 17% on real-world tasks and 6% in simulation.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Adding explicit visual chain-of-thought by predicting future image frames before actions improves vision-language-action model performance on complex robot tasks.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"3b52b88d2af5ea114c21b3b378c53ee08b402920c61bbf8d8ff99ba3ab2c10d3"},"source":{"id":"2503.22020","kind":"arxiv","version":1},"verdict":{"id":"5fa2ec6f-345f-40fb-a915-12c6d595b5c6","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T05:17:41.158602Z","strongest_claim":"Our experimental results demonstrate that CoT-VLA achieves strong performance, outperforming the state-of-the-art VLA model by 17% in real-world manipulation tasks and 6% in simulation benchmarks.","one_line_summary":"CoT-VLA is a 7B VLA that generates future visual frames autoregressively as planning goals before actions, outperforming prior VLAs by 17% on real-world tasks and 6% in simulation.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That autoregressive prediction of future image frames produces reliable visual goals that meaningfully improve downstream action generation for complex manipulation tasks.","pith_extraction_headline":"Adding explicit visual chain-of-thought by predicting future image frames before actions improves vision-language-action model performance on complex robot tasks."},"references":{"count":85,"sample":[{"doi":"","year":2024,"title":"Gen2Act: Human Video Generation in Novel Scenarios enables Generalizable Robot Manipulation","work_id":"a3bde288-aace-40db-8067-3ae6656f9509","ref_index":1,"cited_arxiv_id":"2409.16283","is_internal_anchor":true},{"doi":"","year":2023,"title":"Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models","work_id":"954b4359-f4ed-4c73-ae5b-f75d486b6fc8","ref_index":2,"cited_arxiv_id":"2310.10639","is_internal_anchor":true},{"doi":"","year":2022,"title":"RT-1: Robotics Transformer for Real-World Control at Scale","work_id":"e11bda85-8531-46bc-a07f-d0ade3643ab1","ref_index":3,"cited_arxiv_id":"2212.06817","is_internal_anchor":true},{"doi":"","year":2021,"title":"Emerg- ing properties in self-supervised vision transformers","work_id":"d783e7b6-6d26-419d-a3e8-aae0948df097","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation","work_id":"843ab5eb-2815-4db8-b3bc-890b23fa5ffa","ref_index":5,"cited_arxiv_id":"2410.06158","is_internal_anchor":true}],"resolved_work":85,"snapshot_sha256":"ed33a648d307c2bf7df5fb3321c74515d25cf806f5865b17937f09810b96bb56","internal_anchors":28},"formal_canon":{"evidence_count":2,"snapshot_sha256":"38c0d167a3af73b21e802bd9b1a98b72fe48248e34edd20fb8a7e992da0f3826"},"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":"2503.22020","created_at":"2026-05-17T23:38:48.986149+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.22020v1","created_at":"2026-05-17T23:38:48.986149+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.22020","created_at":"2026-05-17T23:38:48.986149+00:00"},{"alias_kind":"pith_short_12","alias_value":"5LJABTGJ7K3B","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"5LJABTGJ7K3BFTPV","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"5LJABTGJ","created_at":"2026-05-18T12:33:37.589309+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":27,"internal_anchor_count":27,"sample":[{"citing_arxiv_id":"2605.23856","citing_title":"Point Tracking Improves World Action Models","ref_index":78,"is_internal_anchor":true},{"citing_arxiv_id":"2507.16815","citing_title":"ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning","ref_index":55,"is_internal_anchor":true},{"citing_arxiv_id":"2505.03233","citing_title":"GraspVLA: a Grasping Foundation Model Pre-trained on Billion-scale Synthetic Action Data","ref_index":34,"is_internal_anchor":true},{"citing_arxiv_id":"2505.15659","citing_title":"FLARE: Robot Learning with Implicit World Modeling","ref_index":5,"is_internal_anchor":true},{"citing_arxiv_id":"2507.01925","citing_title":"A Survey on Vision-Language-Action Models: An Action Tokenization Perspective","ref_index":223,"is_internal_anchor":true},{"citing_arxiv_id":"2504.19854","citing_title":"NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks","ref_index":15,"is_internal_anchor":true},{"citing_arxiv_id":"2507.04447","citing_title":"DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge","ref_index":58,"is_internal_anchor":true},{"citing_arxiv_id":"2503.15558","citing_title":"Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning","ref_index":59,"is_internal_anchor":true},{"citing_arxiv_id":"2509.06951","citing_title":"F1: A Vision-Language-Action Model Bridging Understanding and Generation to Actions","ref_index":31,"is_internal_anchor":true},{"citing_arxiv_id":"2505.12705","citing_title":"DreamGen: Unlocking Generalization in Robot Learning through Video World Models","ref_index":53,"is_internal_anchor":true},{"citing_arxiv_id":"2507.23682","citing_title":"villa-X: Enhancing Latent Action Modeling in Vision-Language-Action Models","ref_index":71,"is_internal_anchor":true},{"citing_arxiv_id":"2602.20231","citing_title":"UniLACT: Depth-Aware RGB Latent Action Learning for Vision-Language-Action Models","ref_index":22,"is_internal_anchor":true},{"citing_arxiv_id":"2512.15692","citing_title":"mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs","ref_index":60,"is_internal_anchor":true},{"citing_arxiv_id":"2506.13757","citing_title":"AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning","ref_index":31,"is_internal_anchor":true},{"citing_arxiv_id":"2502.05855","citing_title":"DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control","ref_index":17,"is_internal_anchor":true},{"citing_arxiv_id":"2605.13757","citing_title":"FrameSkip: Learning from Fewer but More Informative Frames in VLA Training","ref_index":21,"is_internal_anchor":true},{"citing_arxiv_id":"2603.16666","citing_title":"Fast-WAM: Do World Action Models Need Test-time Future Imagination?","ref_index":28,"is_internal_anchor":true},{"citing_arxiv_id":"2605.10564","citing_title":"DeepSight: Long-Horizon World Modeling via Latent States Prediction for End-to-End Autonomous Driving","ref_index":38,"is_internal_anchor":true},{"citing_arxiv_id":"2605.09693","citing_title":"Do multimodal models imagine electric sheep?","ref_index":30,"is_internal_anchor":true},{"citing_arxiv_id":"2605.06481","citing_title":"OA-WAM: Object-Addressable World Action Model for Robust Robot Manipulation","ref_index":95,"is_internal_anchor":true},{"citing_arxiv_id":"2605.06175","citing_title":"VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts","ref_index":29,"is_internal_anchor":true},{"citing_arxiv_id":"2604.22152","citing_title":"dWorldEval: Scalable Robotic Policy Evaluation via Discrete Diffusion World Model","ref_index":48,"is_internal_anchor":true},{"citing_arxiv_id":"2602.15922","citing_title":"World Action Models are Zero-shot Policies","ref_index":87,"is_internal_anchor":true},{"citing_arxiv_id":"2506.09985","citing_title":"V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning","ref_index":57,"is_internal_anchor":true},{"citing_arxiv_id":"2604.05672","citing_title":"A1: A Fully Transparent Open-Source, Adaptive and Efficient Truncated Vision-Language-Action Model","ref_index":59,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":2,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5LJABTGJ7K3BFTPVTGXJZACZCJ","json":"https://pith.science/pith/5LJABTGJ7K3BFTPVTGXJZACZCJ.json","graph_json":"https://pith.science/api/pith-number/5LJABTGJ7K3BFTPVTGXJZACZCJ/graph.json","events_json":"https://pith.science/api/pith-number/5LJABTGJ7K3BFTPVTGXJZACZCJ/events.json","paper":"https://pith.science/paper/5LJABTGJ"},"agent_actions":{"view_html":"https://pith.science/pith/5LJABTGJ7K3BFTPVTGXJZACZCJ","download_json":"https://pith.science/pith/5LJABTGJ7K3BFTPVTGXJZACZCJ.json","view_paper":"https://pith.science/paper/5LJABTGJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.22020&json=true","fetch_graph":"https://pith.science/api/pith-number/5LJABTGJ7K3BFTPVTGXJZACZCJ/graph.json","fetch_events":"https://pith.science/api/pith-number/5LJABTGJ7K3BFTPVTGXJZACZCJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5LJABTGJ7K3BFTPVTGXJZACZCJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5LJABTGJ7K3BFTPVTGXJZACZCJ/action/storage_attestation","attest_author":"https://pith.science/pith/5LJABTGJ7K3BFTPVTGXJZACZCJ/action/author_attestation","sign_citation":"https://pith.science/pith/5LJABTGJ7K3BFTPVTGXJZACZCJ/action/citation_signature","submit_replication":"https://pith.science/pith/5LJABTGJ7K3BFTPVTGXJZACZCJ/action/replication_record"}},"created_at":"2026-05-17T23:38:48.986149+00:00","updated_at":"2026-05-17T23:38:48.986149+00:00"}