1 citations · 1 across the 13 of their papers we have counts for
13 papers
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…
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…
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…
WaveMoE: A Wavelet-Enhanced Mixture-of-Experts Foundation Model for Time Series Forecasting
Shunyu Wu, Jiawei Huang, Weibin Feng +6
Time series foundation models (TSFMs) have recently achieved remarkable success in universal forecasting by leveraging large-scale pretraining on diverse time series data. Compleme…
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…
PHAnToM: Persona-based Prompting Has An Effect on Theory-of-Mind Reasoning in Large Language Models
Fiona Anting Tan, Gerard Christopher Yeo, Kokil Jaidka +6
The use of LLMs in natural language reasoning has shown mixed results, sometimes rivaling or even surpassing human performance in simpler classification tasks while struggling with…