2 citations · 7 across the 37 of their papers we have counts for
10 papers · 1 filter
A Blind Spot in Alignment: Quantifying Biosecurity Risks in Large Language Models
Shu Quan, Tianfang Hao, Sitong Fang +8
Large Language Models (LLMs) are accelerating biological research, yet this same capability poses a critical biosecurity threat: models that assist in protein engineering can equal…
AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments
Zhiheng Xi, Dingwen Yang, Jiaqi Liu +21
Language agents, i.e., LLM agents, progress rapidly and are increasingly deployed in production environments. This trend underscores the urgent need for rigorous and realistic eval…
SPADE-Bench: Evaluating Spontaneous Strategic Deception in Agents via Plan-Action Divergence
Yuyan Bu, Haowei Li, Qirui Zheng +7
As LLM-based agents expand their operational scope, reliability becomes a prerequisite for real-world deployment. However, in practical applications, human users cannot monitor eve…
SafeMCP: Proactive Power Regulation for LLM Agent Defense via Environment-Grounded Look-Ahead Reasoning
Lichao Wang, Zhaoxing Ren, Tianzhuo Yang +4
As Large Language Model (LLM) agents increasingly leverage the Model Context Protocol (MCP) to operate in complex environments, the expansion of their action spaces offers agents u…
MiraBench: Evaluating Action-Conditioned Reliability in Robotic World Models
Tianzhuo Yang, Zihan Shen, Zirui Mi +7
Action-conditioned world models are increasingly used as scalable simulators for robot learning, yet current evaluations provide limited evidence that their predictions are reliabl…
RedVLA: Physical Red Teaming for Vision-Language-Action Models
Yuhao Zhang, Borong Zhang, Jiaming Fan +4
The real-world deployment of Vision-Language-Action (VLA) models remains limited by the risk of unpredictable and irreversible physical harm. However, we currently lack effective m…
ShuttleEnv: An Interactive Data-Driven RL Environment for Badminton Strategy Modeling
Ang Li, Xinyang Gong, Bozhou Chen +5
We present ShuttleEnv, an interactive and data-driven simulation environment for badminton, designed to support reinforcement learning and strategic behavior analysis in fast-paced…
VISA: Value Injection via Shielded Adaptation for Personalized LLM Alignment
Jiawei Chen, Tianzhuo Yang, Guoxi Zhang +3
Aligning Large Language Models (LLMs) with nuanced human values remains a critical challenge, as existing methods like Reinforcement Learning from Human Feedback (RLHF) often handl…
What, Whether and How? Unveiling Process Reward Models for Thinking with Images Reasoning
Yujin Zhou, Pengcheng Wen, Jiale Chen +6
The rapid advancement of Large Vision Language Models (LVLMs) has demonstrated excellent abilities in various visual tasks. Building upon these developments, the thinking with imag…
Enhance the Safety in Reinforcement Learning by ADRC Lagrangian Methods
Mingxu Zhang, Huicheng Zhang, Jiaming Ji +2
Safe reinforcement learning (Safe RL) seeks to maximize rewards while satisfying safety constraints, typically addressed through Lagrangian-based methods. However, existing approac…