8 papers
AgentPanel: Toward a New Paradigm for Human--AI Collaboration in Exploring Scientific Questions
Zhiyao Cui, Qianyi Wang, Haoyang Yan +26
Identifying promising scientific ideas remains an important challenge in research practice. Researchers commonly rely on small-group discussions or one-to-one interactions with a s…
Structured In-context Environment Scaling for Large Language Model Reasoning
Peng Yu, Zeyuan Zhao, Shao Zhang +3
Large language models (LLMs) have achieved significant advancements in reasoning capabilities through reinforcement learning (RL) via environmental exploration. As the intrinsic pr…
Sequence Pathfinder for Multi-Agent Pickup and Delivery in the Warehouse
Zeyuan Zhao, Chaoran Li, Shao Zhang +1
Multi-Agent Pickup and Delivery (MAPD) is a challenging extension of Multi-Agent Path Finding (MAPF), where agents are required to sequentially complete tasks with fixed-location p…
Towards Monotonic Improvement in In-Context Reinforcement Learning
Wenhao Zhang, Shao Zhang, Xihuai Wang +2
In-Context Reinforcement Learning (ICRL) has emerged as a promising paradigm for developing agents that can rapidly adapt to new tasks by leveraging past experiences as context, wi…
Leveraging Dual Process Theory in Language Agent Framework for Real-time Simultaneous Human-AI Collaboration
Shao Zhang, Xihuai Wang, Wenhao Zhang +10
Agents built on large language models (LLMs) have excelled in turn-by-turn human-AI collaboration but struggle with simultaneous tasks requiring real-time interaction. Latency issu…
Audio Turing Test: Benchmarking the Human-likeness of Large Language Model-based Text-to-Speech Systems in Chinese
Xihuai Wang, Ziyi Zhao, Siyu Ren +9
Recent advances in large language models (LLMs) have significantly improved text-to-speech (TTS) systems, enhancing control over speech style, naturalness, and emotional expression…