WorldAgen is a unified framework that jointly learns world modeling and action prediction using a shared Transformer backbone with two specialized heads. It introduces a Mixed Unidirectional Attention Mask to separate the world model and agent model, and enables Test-Time Training (TTT) by sampling exploratory actions and updating the world model with real state transitions. Experiments on CALVIN and LIBERO show that WorldAgen matches or surpasses state‑of‑the‑art methods, especially when TTT is applied to a few samples.
By Chi Wan, Kangrui Wang, Yuan Si, Pingyue Zhang, Manling Li
arXiv:2609.38334v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly deployed as agents for multi-step decision-making, yet transfer poorly to unseen environments. World-mode...
By Yuhan Guo, Jinming Liu, Liang Xu, Ziqiang Li, Jianguo Huang, Zhicheng Wang, Hu Zhu, Qiuyu Chen, Yuntao Wei, Xin Jin, Wenjun Zeng
Hierarchical World Models with Implicit Dynamics (ID‑WM) introduces a two‑level approach that separates state‑space reachability from local control. A high‑level model samples feasible transitions across the state manifold, while a low‑level world model acts as an inverse dynamics solver to translate these transitions into precise actions. This design enables planners to identify reachable subgoals without being constrained by low‑level execution details, producing novel long‑horizon plans that outperform state‑of‑the‑art policy and diffusion‑based planners by 38.5% and single‑level MPC by 135% in complex, unseen configurations.
By Gaoyue Zhou, Yvonne Wu, Zichen Jeff Cui, Nicolas Ballas, Mido Assran, Lerrel Pinto, Yann LeCun
Multi-turn long-horizon planning is critical for foundation model agents, yet how to fundamentally improve it remains unclear. Existing models are trained on uncontrollable and opaque Internet data, making it difficult to identify how planning ability is acquired, shaped, and integrated.
arXiv:2607. 24720v1 Announce Type: cross Abstract: Multi-turn long-horizon planning is critical for foundation model agents, yet how to fundamentally improve it remains unclear.
By Tianyi Men, Zhuoran Jin, Kang Liu, Jun Zhao
arXiv:2601.13247v2 Announce Type: replace-cross
Abstract: Current Large Language Models (LLMs) exhibit a critical modal disconnect: they possess vast semantic knowledge but lack the procedural ground...
By Baochang Ren, Yunzhi Yao, Rui Sun, Shuofei Qiao, Ningyu Zhang, Huajun Chen