4 papers
LLM-Guided Safety Agent for Edge Robotics with an ISO-Compliant Perception-Compute-Control Architecture
Xu Huang, Ruofan Zhang, Lu Cheng +8
Ensuring functional safety in human-robot interaction is challenging because AI perception is inherently probabilistic, whereas industrial standards require deterministic behavior.…
DART: Difficulty-Adaptive Reasoning Truncation for Efficient Large Language Models
Ruofan Zhang, Bin Xia, Zhen Cheng +4
Adaptive reasoning is essential for aligning the computational effort of large language models (LLMs) with the intrinsic difficulty of problems. Current chain-of-thought methods bo…
Beyond the Strongest LLM: Multi-Turn Multi-Agent Orchestration vs. Single LLMs on Benchmarks
Aaron Xuxiang Tian, Ruofan Zhang, Jiayao Tang +12
We study multi-turn multi-agent orchestration, where multiple large language model (LLM) agents interact over multiple turns by iteratively proposing answers or casting votes until…
Measuring Harmfulness of Computer-Using Agents
Aaron Xuxiang Tian, Ruofan Zhang, Janet Tang +3
Computer-using agents (CUAs), which can autonomously control computers to perform multi-step actions, might pose significant safety risks if misused. However, existing benchmarks m…