8 papers
HyMem: Hierarchical Context Management for Long-Horizon Agents via Information Isolation
XinQi Wang, Jinwei Xiao, Sijia Cui +4
Large language model (LLM) agents often perform poorly on complex, long-horizon tasks because their context becomes increasingly cluttered over time. As interactions accumulate, de…
Bridging What the Model Thinks and How It Speaks: Expressive Speech Generation via Self-Aware Intent-Realization Alignment
Kuang Wang, Lai Wei, Ping Lin +8
Speech Language Models (SLMs) exhibit strong semantic understanding, yet often fail to translate this capacity into expressive acoustic realization, producing speech with flattened…
CoMIC: Collaborative Memory and Insights Circulation for Long-Horizon LLM Agents in Web Cloud-Edge Systems
Yannan Wang, Longli Yang, Zhen Liu +2
LLM-based Web agents are increasingly deployed on edge servers to carry out long-horizon tasks on behalf of users with low latency. However, the lightweight models that fit on edge…
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…
Coarse-to-Fine Grounded Memory for LLM Agent Planning
Wei Yang, Jinwei Xiao, Hongming Zhang +3
Recent advancements in Large Language Models (LLMs) have driven growing interest in LLM-based agents for complex planning tasks. To avoid costly agent training, many studies adopte…
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…