Papers by Xiaoqian Lu

2 papers
Med-VRAgent: A Framework for Medical Visual Reasoning-Enhanced Agents (2025.emnlp-main)

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Challenge: Visual Language Models (VLMs) have shown strong performance in tasks like radiology report generation but struggle with hallucinations, vague descriptions, Inconsistent logic and poor localization.
Approach: They propose a framework for medical visual reasoning based on Visual Guidance and Self-Reward paradigms and Monte Carlo Tree Search to improve the model's visual reasoning capabilities.
Outcome: The proposed framework outperforms existing models on multiple medical VQA benchmarks.
The Essence of Contextual Understanding in Theory of Mind: A Study on Question Answering with Story Characters (2025.acl-long)

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Challenge: Theory-of-Mind (ToM) is a psychological capability that allows humans to understand and interpret the mental states of others.
Approach: They propose a CharToM-QA benchmark to assess the importance of comprehensive contextual understanding about personal backgrounds in ToM.
Outcome: The proposed model outperforms existing models on 1,035 ToM questions based on classic novels and shows that educated participants perform better when they have read the novels than non-educated participants.

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