Pith Number

pith:5LJABTGJ

pith:2025:5LJABTGJ7K3BFTPVTGXJZACZCJ

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CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models

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

Adding explicit visual chain-of-thought by predicting future image frames before actions improves vision-language-action model performance on complex robot tasks.

arxiv:2503.22020 v1 · 2025-03-27 · cs.CV · cs.AI · cs.LG · cs.RO

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Claims

C1strongest 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.

C2weakest assumption

That autoregressive prediction of future image frames produces reliable visual goals that meaningfully improve downstream action generation for complex manipulation tasks.

C3one 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.

References

85 extracted · 85 resolved · 28 Pith anchors

[1] Gen2Act: Human Video Generation in Novel Scenarios enables Generalizable Robot Manipulation 2024 · arXiv:2409.16283

[2] Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models 2023 · arXiv:2310.10639

[3] RT-1: Robotics Transformer for Real-World Control at Scale 2022 · arXiv:2212.06817

[4] Emerg- ing properties in self-supervised vision transformers 2021

[5] GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation 2024 · arXiv:2410.06158

Formal links

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Cited by

27 papers in Pith

Point Tracking Improves World Action Models

ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning

GraspVLA: a Grasping Foundation Model Pre-trained on Billion-scale Synthetic Action Data

FLARE: Robot Learning with Implicit World Modeling

A Survey on Vision-Language-Action Models: An Action Tokenization Perspective

Receipt and verification

First computed	2026-05-17T23:38:48.986037Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

ead200ccc9fab612cdf599ae9c8059126dfb74b1d9eb8331e18424a25620f90e

Aliases

arxiv: 2503.22020 · arxiv_version: 2503.22020v1 · doi: 10.48550/arxiv.2503.22020 · pith_short_12: 5LJABTGJ7K3B · pith_short_16: 5LJABTGJ7K3BFTPV · pith_short_8: 5LJABTGJ

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/5LJABTGJ7K3BFTPVTGXJZACZCJ \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: ead200ccc9fab612cdf599ae9c8059126dfb74b1d9eb8331e18424a25620f90e

Canonical record JSON

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    "license": "http://creativecommons.org/licenses/by/4.0/",
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    "submitted_at": "2025-03-27T22:23:04Z",
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