7 papers
TrustMed-RL: Long-Horizon Reinforcement Learning for Evidence-Grounded Clinical Diagnosis
Wenxin Zhan, Yizheng Jiao, Haifeng Song +4
Medical language models can produce correct diagnoses despite incomplete investigations and unsupported reasoning. To support long-horizon, evidence-grounded diagnosis, we introduc…
Rethinking the Evaluation of Efficiency Methods for Multi-Agent Systems
Jiamu Zhang, Lingxi Zhang, Pengjun Lu +6
Efficiency is increasingly important for Large Language Model (LLM)-based multi-agent systems (MAS), as larger models and more agents introduce substantial execution costs. Recent…
SwiftBot: A Decentralized Platform for LLM-Powered Federated Robotic Task Execution
YueMing Zhang, Shuai Xu, Zhengxiong Li +3
Federated robotic task execution systems require bridging natural language instructions to distributed robot control while efficiently managing computational resources across heter…
Empowering LLMs with Parameterized Skills for Adversarial Long-Horizon Planning
Sijia Cui, Shuai Xu, Aiyao He +2
Recent advancements in Large Language Models(LLMs) have led to the development of LLM-based AI agents. A key challenge is the creation of agents that can effectively ground themsel…
Self-Guided Function Calling in Large Language Models via Stepwise Experience Recall
Sijia Cui, Aiyao He, Shuai Xu +5
Function calling enables large language models (LLMs) to interact with external systems by leveraging tools and APIs. When faced with multi-step tool usage, LLMs still struggle wit…
Strategy-Augmented Planning for Large Language Models via Opponent Exploitation
Shuai Xu, Sijia Cui, Yanna Wang +2
Efficiently modeling and exploiting opponents is a long-standing challenge in adversarial domains. Large Language Models (LLMs) trained on extensive textual data have recently demo…
TUMS: Enhancing Tool-use Abilities of LLMs with Multi-structure Handlers
Aiyao He, Sijia Cui, Shuai Xu +2
Recently, large language models(LLMs) have played an increasingly important role in solving a wide range of NLP tasks, leveraging their capabilities of natural language understandi…