Author

Fan-Xin Kong

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Preprint Oct 2026

SLoSa: Small, Low-Power, and Safe Foundation Models for Robotic Task Planning

Safe robotic task planning requires planners that can reliably achieve task goals while satisfying safety constraints. Classical planners search over explicit symbolic task representations and can provide formal guarantees with respect to encoded safety constraints, but require rigid input formats. Their computational...

Wei-Zhe Xu, Jia-Liang Fan, Essien Taylor et al. · 0 citations

#artificial intelligence Preprint Oct 2026

Vulnerability Analysis for Safe Multi-Agent Reinforcement Learning

Safe Multi-Agent Reinforcement Learning (SMARL) aims to learn cooperative control policies under prescribed safety constraints. Al- though existing SMARL methods focus on enforcing safety during policy optimization, their vulnerability remains largely unexplored. This paper investigates inference-time observation attac...

Tao-Zheng Zhu, Shi-Xiong Jiang, Fan-Xin Kong · 0 citations

#artificial intelligence Preprint Oct 2026

SafeInferCom: Safe Inference-Time Compute via Verifier-Guided Mid-Generation Intervention for Robotic Task Planning

Large Reasoning Language Models (LRLMs) enable multi-step reasoning for robotic task planning, but continued reasoning can overwrite valid intermediate plans or leave constraint violations unresolved, reducing planning reliability and wasting inference-time computation. We develop an inference-time monitor that exposes...

Wei-Zhe Xu, Jia-Liang Fan, Meng-Yu Liu et al. · 0 citations