Embodied world models learn to predict future physical dynamics from visual observations and control signals, where physical knowledge is implicitly entangled within latent representations. We introduce CausalWM, a 16B embodied world model that performs explicit causal chain-of-thought reasoning before future video pre...
Zi-Ming Xu, Shuang Liang, R. Han et al.· 1 citation
Recent advances in large language models have made automatic game generation increasingly feasible, yet reliably improving generated games beyond a playable version remains challenging. Naive iterative refinement can easily overfit a small set of test cases, producing fragile games with unresolved bugs, missing behavio...
Wen-Yi Wu, Ming-Hao Fu, Jie-Yu You et al.· 0 citations
RSIAgent is introduced, a training-free multi-agent framework for recursive self-improvement through autonomous memory construction that substantially improves strong open-source models, enabling Kimi-K3 and GLM-5.3 to outperform frontier closed-source models including GPT-6.3.
Sibo Zhu, Shi-Cheng Fan, Xin-Yue Wang et al.· 5 citations
This paper presents StructAgent, a state-centered framework that introduces a unified state for maintaining compact, verifiable task progress and a structured workflow that regulates progress through verifier-backed state transitions and generalizes beyond desktop environments to Minecraft, demonstrating the generality...
Wenyi Wu, Sibo Zhu, Kun Zhou et al.· 1 citation
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