Papers

Filtered to video world models · clear filter

Browse by term

continual learning 86reinforcement learning 48benchmarking 13large language models 12benchmarks 11vision-language models 10language models 9robotics 7world models 7natural language processing 6recursive self-improvement 6generative models 5on-policy distillation 5video generation 5attention mechanisms 4coding agents 4LLMs 4multi-agent systems 4multimodal learning 4multimodal models 4self-distillation 4self-supervised learning 4transformers 4vision-language-action models 4world modeling 4agent-based systems 3agentic models 3agentic search 3agents 3autonomous systems 3

Matching papers

SolarWM: Open Data and Scalable Training for Long-Horizon Video World Models

133 upvotes · 2 SEP 2026 · Junchao Huang, Guian Fang, Shengju Qian et al.

This paper introduces SolarWM, an open framework for training video world models from diverse datasets and using different video backbones, allowing for more consistent and reproducible results. Practitioners can use SolarWM to build interactive video world models that can be applied to various real-world applications.

video world modelsopen datacontinual learningvideo backbonesinteractive systems

DreamX-Phi 1.0: Action-Conditioned Video World Model for Robotic Manipulation

87 upvotes · 13 AUG 2026 · DreamX Team, Rui Chen, Xiangxiang Chu et al.

This paper introduces a new AI model called DreamX-Phi 1.0 that can predict what will happen in a robotic manipulation scenario, given an initial state and instructions. This model is useful for robotics developers because it can help them design more reliable and efficient robotic systems.

robotic manipulationaction-conditioned modelsvideo world modelsscene geometryobject manipulationreinforcement learning