3 citations · 11 across the 33 of their papers we have counts for
15 papers · 1 filter
Beyond Perfect Teammates: Multi-Agent Collaboration with Unreliable Colleagues
Chen Huang, Yiyang Zhang, Yuhao Wu +2
LLM-based multi-agent collaboration is increasingly used to solve complex tasks by dividing work among specialized agents and integrating complementary expertise. Yet existing syst…
RealWorldShop: Benchmarking and Improving Conversational Shopping Agents in Real-World E-commerce
Xinwei Yang, Kelong Mao, Yudong Guo +4
Large language models are reshaping ecommerce from static recommenders into interactive shopping assistants, yet real-world shopping requires session-level decision support: users…
Clearing the Fog: Towards Installing and Refining Proactive Exploration Capabilities in LLM Agents
Zhizhao Guan, Chen Huang, Ziming Liu +5
We study proactive exploration in LLM agents, i.e., the ability to explore an environment to acquire information that improves future decision-making. In this regard, we first iden…
Towards Effective Structured Context Modeling for Conversational Recommender Systems via Dual-node Monte Carlo Tree Search
Jincheng Zhang, Chen Huang, Wenqiang Lei +2
We investigate the role of conversational context modeling in user preference tracking for Conversational Recommendation Systems (CRSs). In this regard, we propose DREAMS, a novel…
Clarify User Expertise: Towards Proactive Conversational Agents Tailoring Responses to User Proficiency
Zhihong Cao, Chen Huang
In the context of information seeking, conversational agents are undergoing an evolution from reactive tools to proactive, personalized assistants. A critical aspect of this evolut…
ExpConCAD: Experience-Guided Text-to-CAD Generation from Shape Descriptions with Implicit Spatial Constraints
Jingyao Liu, Jinkang Tang, Chen Huang +2
Text-to-CAD aims to generate executable CAD programs from natural-language descriptions. However, real-world descriptions are often underspecified and omit critical spatial constra…
HippoSpark: An On-Demand Experience System for LLM Reasoning
Jingyao Liu, Danling Meng, Chen Huang +5
Distilling historical trajectories into reusable experience to enhance future problem-solving has become a focal point of recent LLM research. However, existing methods predominant…
Unlocking Biological Workflows for Robust Protein-Text Question Answering: A Dual-Dimensional RAG Framework
Li Ding, Duanyu Feng, Chen Huang +4
Protein-Text Question Answering (QA) is crucial for interpreting biological sequences through natural language. The integration of Large Language Models (LLMs) with Retrieval-Augme…
Bilevel Optimization of Agent Skills via Monte Carlo Tree Search
Chenyi Huang, Haoting Zhang, Jingxu Xu +2
Agent \texttt{skills} are structured collections of instructions, tools, and supporting resources that help large language model (LLM) agents perform particular classes of tasks. E…
Beyond Prompt: Fine-grained Simulation of Cognitively Impaired Standardized Patients via Stochastic Steering
Weikang Zhang, Zimo Zhu, Zhichuan Yang +3
Simulating Standardized Patients with cognitive impairment offers a scalable and ethical solution for clinical training. However, existing methods rely on discrete prompt engineeri…
METER: Evaluating Multi-Level Contextual Causal Reasoning in Large Language Models
Pengfeng Li, Chen Huang, Chaoqun Hao +4
Contextual causal reasoning is a critical yet challenging capability for Large Language Models (LLMs). Existing benchmarks, however, often evaluate this skill in fragmented setting…
METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues
Haofu Yang, Jiaji Liu, Chen Huang +3
Developing non-collaborative dialogue agents traditionally requires the manual, unscalable codification of expert strategies. We propose \ours, a method that leverages large langua…
Towards Proactive Information Probing: Customer Service Chatbots Harvesting Value from Conversation
Chen Huang, Zitan Jiang, Changyi Zou +2
Customer service chatbots are increasingly expected to serve not merely as reactive support tools for users, but as strategic interfaces for harvesting high-value information and b…
STAR: Semantic-Tuned and Tail-Adaptive Retriever for Graph-Augmented Generation
Shuai Li, Chen Huang, Duanyu Feng +2
To augment Large Language Models (LLMs) for multi-hop question answering, a mainstream solution within Graph Retrieval Augmented Generation (GraphRAG) leverages lightweight retriev…
LETGAMES: An LLM-Powered Gamified Approach to Cognitive Training for Patients with Cognitive Impairment
Jingwei Shi, Shengyu Tao, Xinxiang Yin +3
The application of games as a therapeutic tool for cognitive training is beneficial for patients with cognitive impairments. However, effective game design for individual patient i…