8 citations · 8 across the 10 of their papers we have counts for
14 papers
PolyBridgeBench: Benchmarking Multimodal LLMs for Physics-Grounded Bridge Design
Zicheng Zhao, Dongyin Chen, Rui Xu +1
Multimodal large language models, or MLLMs, perform well at visual understanding and structured generation, yet these capabilities do not establish whether an engineering design wi…
Hybrid-IR: Dual-Path Hybrid Retrieval with Iterative Reasoning for Complex Medical Question Answering
Sheng Wan, Jiahui Zhang, Zicheng Zhao +1
Large language models (LLMs) have shown promising performance across a wide range of biomedical applications, including medical question answering (QA), yet they remain prone to ha…
QUACK: Questioning, Understanding, and Auditing Communicated Knowledge in Multimodal Social Deduction Agents
Ye Yuan, Rui Song, Weien Li +12
Social deduction games have become a popular testbed for probing reasoning, deception, coordination, and belief modeling in Large Language Model (LLM) agents. However, most environ…
Optimizing User Profiles via Contextual Bandits for Retrieval-Augmented LLM Personalization
Linfeng Du, Ye Yuan, Zichen Zhao +8
Large language models (LLMs) excel at general-purpose tasks, yet adapting their responses to individual users remains challenging. Retrieval augmentation provides a lightweight alt…
Well Begun, Half Done: Reinforcement Learning with Prefix Optimization for LLM Reasoning
Yiliu Sun, Zicheng Zhao, Yang Wei +2
Reinforcement Learning with Verifiable Rewards (RLVR) significantly enhances the reasoning capability of Large Language Models (LLMs). Current RLVR approaches typically conduct tra…
ORIGAMISPACE: Benchmarking Multimodal LLMs in Multi-Step Spatial Reasoning with Mathematical Constraints
Rui Xu, Dakuan Lu, Zicheng Zhao +5
Spatial reasoning is a key capability in the field of artificial intelligence, especially crucial in areas such as robotics, computer vision, and natural language understanding. Ho…
Analysing Knowledge Construction in Online Learning: Adapting the Interaction Analysis Model for Unstructured Large-Scale Discourse
Jindi Wang, Yidi Zhang, Zhaoxing Li +4
The rapid expansion of online courses and social media has generated large volumes of unstructured learner-generated text. Understanding how learners construct knowledge in these s…
G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledge
Linhao Luo, Zicheng Zhao, Junnan Liu +9
Large language models (LLMs) excel at complex reasoning but remain limited by static and incomplete parametric knowledge. Retrieval-augmented generation (RAG) mitigates this by inc…
CortexDebate: Debating Sparsely and Equally for Multi-Agent Debate
Yiliu Sun, Zicheng Zhao, Sheng Wan +1
Nowadays, single Large Language Model (LLM) struggles with critical issues such as hallucination and inadequate reasoning abilities. To mitigate these issues, Multi-Agent Debate (M…
Fast-Slow-Thinking: Complex Task Solving with Large Language Models
Yiliu Sun, Yanfang Zhang, Zicheng Zhao +3
Nowadays, Large Language Models (LLMs) have been gradually employed to solve complex tasks. To face the challenge, task decomposition has become an effective way, which proposes to…
Graph Retrieval-Augmented LLM for Conversational Recommendation Systems
Zhangchi Qiu, Linhao Luo, Zicheng Zhao +2
Conversational Recommender Systems (CRSs) have emerged as a transformative paradigm for offering personalized recommendations through natural language dialogue. However, they face…
GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation
Linhao Luo, Zicheng Zhao, Gholamreza Haffari +3
Retrieval-augmented generation (RAG) has proven effective in integrating knowledge into large language models (LLMs). However, conventional RAGs struggle to capture complex relatio…
Graph-constrained Reasoning: Faithful Reasoning on Knowledge Graphs with Large Language Models
Linhao Luo, Zicheng Zhao, Gholamreza Haffari +3
Large language models (LLMs) have demonstrated impressive reasoning abilities, but they still struggle with faithful reasoning due to knowledge gaps and hallucinations. To address…
Graph Stochastic Neural Process for Inductive Few-shot Knowledge Graph Completion
Zicheng Zhao, Linhao Luo, Shirui Pan +2
Knowledge graphs (KGs) store enormous facts as relationships between entities. Due to the long-tailed distribution of relations and the incompleteness of KGs, there is growing inte…