papers

Publications (6)

cs.CR2026

To trust or not to trust: Attention-based Trust Management for LLM Multi-Agent Systems

Pengfei He, Zhenwei Dai, Xianfeng Tang +9

Large Language Model-based Multi-Agent Systems (LLM-MAS) have demonstrated strong capabilities in solving complex tasks but remain vulnerable when agents receive unreliable message…

cs.CL2025

Examples as the Prompt: A Scalable Approach for Efficient LLM Adaptation in E-Commerce

Jingying Zeng, Zhenwei Dai, Hui Liu +6

Prompting LLMs offers an efficient way to guide output generation without explicit model training. In the e-commerce domain, prompting-based applications are widely used for tasks…

cs.IR2024

REAPER: Reasoning based Retrieval Planning for Complex RAG Systems

Ashutosh Joshi, Sheikh Muhammad Sarwar, Samarth Varshney +3

Complex dialog systems often use retrieved evidence to facilitate factual responses. Such RAG (Retrieval Augmented Generation) systems retrieve from massive heterogeneous data stor…

cs.AI2025

RRO: LLM Agent Optimization Through Rising Reward Trajectories

Zilong Wang, Jingfeng Yang, Sreyashi Nag +5

Large language models (LLMs) have exhibited extraordinary performance in a variety of tasks while it remains challenging for them to solve complex multi-step tasks as agents. In pr…

cs.CR2025

Keeping an Eye on LLM Unlearning: The Hidden Risk and Remedy

Jie Ren, Zhenwei Dai, Xianfeng Tang +9

Although Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks, growing concerns have emerged over the misuse of sensitive, copyrighte…

cs.CL2025

Cite Before You Speak: Enhancing Context-Response Grounding in E-commerce Conversational LLM-Agents

Jingying Zeng, Hui Liu, Zhenwei Dai +5

With the advancement of conversational large language models (LLMs), several LLM-based Conversational Shopping Agents (CSA) have been developed to help customers smooth their onlin…