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Author

Kun Zhou

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Preprint Oct 2026

Video2World: Benchmarking Coding Agents for Interactive World Modeling from Embodied Videos

Building interactive simulators from real-world observations is a promising way to scale embodied data, but current pipelines still rely heavily on manual environment construction and calibration. We study whether frontier foundation models and coding agents can automate this process end to end. We formulate \emph{auto...

Jin-Zhou Tang, Z. Zhang, Jing Yang et al. · 0 citations
Preprint Sep 2026

CausalWM: Causal Chain-of-Thought Reasoning for Embodied World Model

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
#artificial intelligence Preprint Oct 2026

Video2World: Benchmarking Coding Agents for Interactive World Modeling from Embodied Videos

Building interactive simulators from real-world observations is a promising way to scale embodied data, but current pipelines still rely heavily on manual environment construction and calibration. We study whether frontier foundation models and coding agents can automate this process end to end. We formulate \emph{auto...

Jin-Zhou Tang, Z. Zhang, Jing Yang et al. · 0 citations
#machine learning Preprint Sep 2026

RSIGame: Autonomous Agentic Game Development with Recursive Self-improvement

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
#artificial intelligence Preprint Sep 2026

RSIAgent: Autonomous Exploration for Recursive Self-improvement in New Environments

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
Preprint Jul 2026

StructAgent: Harness Long-horizon Digital Agents with Unified Causal Structure

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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