Nano World Models: A Minimalist Implementation of Future Video Prediction
Nano World Models is a minimalist codebase for future video prediction centered on diffusion forcing. It provides a unified interface for generative objectives, model scales, action-conditioning mechanisms, latent observation spaces, datasets, evaluation protocols, and long-horizon rollouts, enabling controlled studies of world-modeling components. Experiments across control environments, games, and real-robot data validate its effectiveness. Code, configs, and pretrained checkpoints are released for open, reproducible research.
Article intelligence
Key points
- Nano World Models is a minimal, reproducible codebase for future video prediction research.
- It integrates key design components like generative objectives, model scales, and action conditioning around diffusion forcing.
- Experiments on simple control environments, game simulations, and real robot data demonstrate its utility.
- Open-source code, configurations, and pretrained checkpoints promote open science and reproducibility.
Why it matters
This matters because nano World Models is a minimal, reproducible codebase for future video prediction research.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.23993] Nano World Models: A Minimalist Implementation of Future Video Prediction
[Submitted on 17 May 2026]
Title:Nano World Models: A Minimalist Implementation of Future Video Prediction
View a PDF of the paper titled Nano World Models: A Minimalist Implementation of Future Video Prediction, by Siqiao Huang and 6 other authors
View PDF HTML (experimental)
Abstract:World models have become a central paradigm for learning predictive simulators that support generation, planning, and decision-making. Yet, despite rapid progress in industry-scale interactive video generation, the broader research community still lacks compact, reproducible, and easily extensible implementations for studying the design choices underlying modern world models. We introduce Nano World Models, a minimalist codebase for future video prediction centered around diffusion forcing. Nano World Models provides a unified interface for generative objectives, model scales, action-conditioning mechanisms, latent observation spaces, datasets, evaluation protocols, and long-horizon rollout procedures. This design enables controlled studies of world-modeling components that are often entangled across separate implementations. Through experiments across simple control environments, game simulation, and real-robot data, we examine how prediction parameterization, architecture scale, action injection, sampling budget, and domain complexity affect video prediction quality and autoregressive rollout behavior. By releasing code, configurations, evaluation scripts, and pretrained checkpoints, Nano World Models aims to provide a compact yet extensible experimental substrate for open, reproducible, and scientific world-model research.
Comments: Project page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2605.23993 [cs.CV]
(or arXiv:2605.23993v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.23993
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Siqiao Huang [view email] [v1] Sun, 17 May 2026 22:46:44 UTC (3,709 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Nano World Models: A Minimalist Implementation of Future Video Prediction, by Siqiao Huang and 6 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.CV
new | recent | 2026-05
Change to browse by:
cs cs.AI cs.LG
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)