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cs.CL2025

Scaling Language-Centric Omnimodal Representation Learning

Chenghao Xiao, Hou Pong Chan, Hao Zhang +3

Recent multimodal embedding approaches leveraging multimodal large language models (MLLMs) fine-tuned with contrastive learning (CL) have shown promising results, yet the underlyin…

cs.CL2025

ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning

Yu Sun, Xingyu Qian, Weiwen Xu +8

Reasoning-based large language models have excelled in mathematics and programming, yet their potential in knowledge-intensive medical question answering remains underexplored and…

cs.CL2025

GeoPQA: Bridging the Visual Perception Gap in MLLMs for Geometric Reasoning

Guizhen Chen, Weiwen Xu, Hao Zhang +4

Recent advancements in reinforcement learning (RL) have enhanced the reasoning abilities of large language models (LLMs), yet the impact on multimodal LLMs (MLLMs) is limited. Part…

cs.CL2025

Analyzing LLMs' Knowledge Boundary Cognition Across Languages Through the Lens of Internal Representations

Chenghao Xiao, Hou Pong Chan, Hao Zhang +4

While understanding the knowledge boundaries of LLMs is crucial to prevent hallucination, research on the knowledge boundaries of LLMs has predominantly focused on English. In this…

cs.CL2025

Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning

LASA Team, Weiwen Xu, Hou Pong Chan +16

Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in understanding common visual elements, largely due to their large-scale datasets and advanced t…

cs.CL2025

FINEREASON: Evaluating and Improving LLMs' Deliberate Reasoning through Reflective Puzzle Solving

Guizhen Chen, Weiwen Xu, Hao Zhang +6

Many challenging reasoning tasks require not just rapid, intuitive responses, but a more deliberate, multi-step approach. Recent progress in large language models (LLMs) highlights…