Papers › Cosmos World Foundation Model Platform for Physical AI

Cosmos World Foundation Model Platform for Physical AI

7 Jan 2025arXiv:2501.03575archive 2025-07-28

Nvidia, :, Niket Agarwal, Arslan Ali, Maciej Bala, Yogesh Balaji, Erik Barker, Tiffany Cai, Prithvijit Chattopadhyay, Yongxin Chen, Yin Cui, Yifan Ding, Daniel Dworakowski, Jiaojiao Fan, Michele Fenzi, Francesco Ferroni, Sanja Fidler, Dieter Fox, Songwei Ge, Yunhao Ge, Jinwei Gu, Siddharth Gururani, Ethan He, Jiahui Huang, Jacob Huffman, Pooya Jannaty, Jingyi Jin, Seung Wook Kim, Gergely Klár, Grace Lam, Shiyi Lan, Laura Leal-Taixe, Anqi Li, Zhaoshuo Li, Chen-Hsuan Lin, Tsung-Yi Lin, Huan Ling, Ming-Yu Liu, Xian Liu, Alice Luo, Qianli Ma, Hanzi Mao, Kaichun Mo, Arsalan Mousavian, Seungjun Nah, Sriharsha Niverty, David Page, Despoina Paschalidou, Zeeshan Patel, Lindsey Pavao, Morteza Ramezanali, Fitsum Reda, Xiaowei Ren, Vasanth Rao Naik Sabavat, Ed Schmerling, Stella Shi, Bartosz Stefaniak, Shitao Tang, Lyne Tchapmi, Przemek Tredak, Wei-Cheng Tseng, Jibin Varghese, Hao Wang, Haoxiang Wang, Heng Wang, Ting-Chun Wang, Fangyin Wei, Xinyue Wei, Jay Zhangjie Wu, Jiashu Xu, Wei Yang, Lin Yen-Chen, Xiaohui Zeng, Yu Zeng, Jing Zhang, Qinsheng Zhang, Yuxuan Zhang, Qingqing Zhao, Artur Zolkowski

Physical AI needs to be trained digitally first. It needs a digital twin of itself, the policy model, and a digital twin of the world, the world model. In this paper, we present the Cosmos World Foundation Model Platform to help developers build customized world models for their Physical AI setups. We position a world foundation model as a general-purpose world model that can be fine-tuned into customized world models for downstream applications. Our platform covers a video curation pipeline, pre-trained world foundation models, examples of post-training of pre-trained world foundation models, and video tokenizers. To help Physical AI builders solve the most critical problems of our society, we make Cosmos open-source and our models open-weight with permissive licenses available via https://github.com/nvidia-cosmos/cosmos-predict1.

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2501.03575")

Code

Syntology Ran 1 of 14 code samples harvested from 3 repositories linked to this paper; 13 have no recorded run. Of those that ran: 1 ran · fixture could not drive it.

By repository: official repository: 14 samples from 3 repositories, 1 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

nvidia-cosmos/cosmos-predict1 officialmentioned in papermentioned on GitHubpytorchApache-2.0 report
nvidia/cosmos-tokenizer officialmentioned in papermentioned on GitHubpytorchApache-2.0 report
nvlabs/tokenbench officialmentioned in paperpytorchApache-2.0 report
nvidia-cosmos/cosmos-transfer1 mentioned on GitHubpytorchApache-2.0 report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

14 samples harvested; 1 ran; 0 honoured the contract we drafted; 13 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · fixture could not drive it
13unverified

Licence: 0 of the 14 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from 3 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

get_1d_sincos_pos_embed_from_grid nvidia-cosmos/cosmos-predict1/cosmos_predict1/autoregressive/modules/embedding.py official repository ran · fixture could not drive it fingerprinted Apache-2.0 (permissive) · e5947aba1d10885f · report
LPIPS nvlabs/tokenbench/token_bench/metrics_cli.py official repository unverified Apache-2.0 (permissive) · ef1f7e1ebf6386e6 · report
PSNR nvlabs/tokenbench/token_bench/metrics_cli.py official repository unverified Apache-2.0 (permissive) · 2ec238b8692bb23a · report
SSIM nvlabs/tokenbench/token_bench/metrics_cli.py official repository unverified Apache-2.0 (permissive) · 4ea7aadfca86cd09 · report
batch2time nvidia/cosmos-tokenizer/cosmos_tokenizer/modules/utils.py official repository unverified Apache-2.0 (permissive) · cc3e6d9b4eee82e0 · report
compute_llama3_ffn_hidden_dim nvidia-cosmos/cosmos-predict1/cosmos_predict1/autoregressive/modules/mlp.py official repository unverified Apache-2.0 (permissive) · 826dcfd43fab7282 · report
create_norm nvidia-cosmos/cosmos-predict1/cosmos_predict1/autoregressive/modules/normalization.py official repository unverified Apache-2.0 (permissive) · 4431273b1abad22f · report
get_vit_config nvidia-cosmos/cosmos-predict1/cosmos_predict1/autoregressive/networks/vit.py official repository unverified Apache-2.0 (permissive) · aa4f611e6a714f05 · report
precompute_freqs_cis_2d nvidia-cosmos/cosmos-predict1/cosmos_predict1/autoregressive/networks/vit.py official repository unverified Apache-2.0 (permissive) · 439497162c72f1dc · report
preprocess_single nvlabs/tokenbench/token_bench/fvd.py official repository unverified Apache-2.0 (permissive) · 7b03dd8d6c1862b8 · report
reshape_for_broadcast nvidia-cosmos/cosmos-predict1/cosmos_predict1/autoregressive/networks/vit.py official repository unverified Apache-2.0 (permissive) · 7176ff743ab3af05 · report
scaled_dot_product_attention nvidia-cosmos/cosmos-predict1/cosmos_predict1/autoregressive/modules/attention.py official repository unverified Apache-2.0 (permissive) · abc31b2f673701c7 · report
space2batch nvidia/cosmos-tokenizer/cosmos_tokenizer/modules/utils.py official repository unverified Apache-2.0 (permissive) · b48fa21d32c1e846 · report
time2batch nvidia/cosmos-tokenizer/cosmos_tokenizer/modules/utils.py official repository unverified Apache-2.0 (permissive) · e9de9d559844e9ae · report

Tasks

model

1 archive task tag without a task page not shown.

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections