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pith:L35TTSYH

pith:2025:L35TTSYHKB5XDMBT6B6DTWQZFP

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Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning

Alice Luo, Andrew Mathau, Andrew Z. Wang, Boxin Wang, Brendan Johnson, David W. Romero, Dinghao Yang, Elena Lantz, Fangyin Wei, Francesco Ferroni, George Kurian, Hannah Brandon, Haoxiang Wang, Huayu Chen, Imad El Hanafi, Jacob Huffman, Jenna Diamond, Jiashu Xu, Jiaxin Cao, Jingxu Zhang, Jingyi Jin, Jinju Chu, Jinwei Gu, Junjie Bai, Liang Feng, Lindsey Pavao, Lyne Tchapmi, Maosheng Liao, Ming-Yu Liu, Misha Smelyanskiy, Nayeon Lee, NVIDIA: Alisson Azzolini, Prithvijit Chattopadhyay, Rama Govindaraju, Rizwan Khan, Shuran Song, Siddharth Gururani, Tsung-Yi Lin, Wei Ping, Xiangyu Lu, Xiaodong Yang, Xiaohui Zeng, Xuan Li, Yao Xu, Yen-Chen Lin, Yifan Ding, Yin Cui, Yun Ni, Zekun Hao, Zhaoshuo Li, Zhe Zhang, Zhuolin Yang

Cosmos-Reason1 models understand the physical world and generate embodied decisions through long chain-of-thought reasoning in natural language.

arxiv:2503.15558 v3 · 2025-03-18 · cs.AI · cs.CV · cs.LG · cs.RO

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Claims

C1strongest claim

Cosmos-Reason1 models can understand the physical world and generate appropriate embodied decisions (e.g., next step action) in natural language through long chain-of-thought reasoning processes.

C2weakest assumption

The hierarchical ontology for physical common sense and the two-dimensional ontology for embodied reasoning sufficiently capture the knowledge needed to generalize across physical tasks and embodiments.

C3one line summary

Cosmos-Reason1-7B and 56B models are trained with physical common sense and embodied reasoning ontologies via supervised fine-tuning and reinforcement learning to produce next-step physical actions.

References

60 extracted · 60 resolved · 20 Pith anchors

[1] Agibot world colosseum.https://github.com/OpenDriveLab/AgiBot-World, 2024 2024

[2] Do as i can, not as i say: Grounding language in robotic affordances 2022

[3] Flamingo: a visual language model for few-shot learning 2022

[4] Covla: Comprehensive vision-language-action dataset for autonomous driving 2024

[5] Qwen2.5-VL Technical Report 2025 · arXiv:2502.13923

Formal links

2 machine-checked theorem links

Cited by

30 papers in Pith

MAVEN: A Multi-stage Agentic Annotation Pipeline for Video Reasoning Tasks

Grounded Reinforcement Learning for Visual Reasoning

Self-Supervised Bootstrapping of Action-Predictive Embodied Reasoning

Contrastive Reasoning Alignment: Reinforcement Learning from Hidden Representations

How to Instruct Your Robot: Dense Language Annotations Power Robot Policy Learning

Receipt and verification

First computed	2026-05-17T23:38:47.832540Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

5efb39cb07507b71b033f07c39da192bec8d1652a1d07700b132fffe02c2cbe5

Aliases

arxiv: 2503.15558 · arxiv_version: 2503.15558v3 · doi: 10.48550/arxiv.2503.15558 · pith_short_12: L35TTSYHKB5X · pith_short_16: L35TTSYHKB5XDMBT · pith_short_8: L35TTSYH

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/L35TTSYHKB5XDMBT6B6DTWQZFP \
  | 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: 5efb39cb07507b71b033f07c39da192bec8d1652a1d07700b132fffe02c2cbe5

Canonical record JSON

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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2025-03-18T22:06:58Z",
    "title_canon_sha256": "271076f7bff7127ea066e33b2f91610943489f69dfae2f3f4b9424a2736cae51"
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