Author

Jinqiao Wang

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Preprint Sep 2026

From World Models to World Action Models: Rethinking Next-State Prediction

Predicting the next state is a core paradigm of World Models for modeling physical dynamics, emphasizing prediction fidelity. As World Models evolve into World-Action Models (WAMs), existing methods still fix the next state before training as RGB, a single latent feature, or a static combination of predefined targets,...

Ting-Yu Yuan, Zi-Ming Ji, Biao-Liang Guan et al. · 1 citation

Preprint Aug 2026

HarnessWAM: Bridging Prediction and Deliberation in World Action Models

Results demonstrate that model-external structured state maintenance and closed-loop agentic decision making can effectively extend the local control capabilities of WAMs into embodied task execution that is plannable, verifiable, and recoverable.

Zhao-Peng Gu, Bing-Ke Zhu, Tianxin Lin et al. · 4 citations

BERM: Low-Overhead Prompt-Injection Detection via In-Situ Benign Representation Modeling

BERM is introduced, a lightweight framework that performs in-situ detection by modeling a host LLM’s internal representations extracted during prefill, adding negligible overhead and reducing incremental inference overhead to near-zero.

Maihao Guo, Chaoyang Zhao, Jin-Qiao Wang · 0 citations