6 papers
Optimize Once, Execute Fast: Latency-Aware Multi-Agent Workflow Learning for Recurrent Queries
Enpei Zhang, Feiyu Qu, Zheng Huang +3
Large Language Model-based Multi-Agent Systems (LLM-based MAS) have shown remarkable success in solving complex tasks by coordinating specialized agents through multi-step workflow…
MemWM: Memory-Augmented Text-Based World Model
Yujun Wang, Tao Zhang, Jinhe Bi +9
World models are increasingly used to support planning in agents by predicting how environment states evolve in response to agent actions. Yet fluent next-state predictions can sti…
HalluGuard: Demystifying Data-Driven and Reasoning-Driven Hallucinations in LLMs
Xinyue Zeng, Junhong Lin, Yujun Yan +4
The reliability of Large Language Models (LLMs) in high-stakes domains such as healthcare, law, and scientific discovery is often compromised by hallucinations. These failures typi…
One-Step Bellman Alignment Enables Provably Efficient Transfer in Online RL
Elynn Chen, Enpei Zhang, Jinhang Chai +1
We study online transfer reinforcement learning (RL) in episodic Markov decision processes, where experience from related source tasks is available during learning on a target task…
Transfer Denoising of Low-Rank Plus Sparse Matrices under Ambient and Rank Growth
Jinhang Chai, Xuyuan Liu, Elynn Chen +1
We study transfer learning for low-rank plus sparse matrix denoising when both ambient and latent dimensions grow. A data-rich source task provides subspace information for a highe…
Representation Interventions Enable Lifelong Knowledge Memory Control in LLMs
Xuyuan Liu, Shengyu Chen, Xinshuai Dong +6
Large language models (LLMs) often produce incorrect or outdated content after being employed. Efficient and accurate knowledge updates without costly retraining are a major challe…