collaborators

11 papers

cs.AI2026

Precedent-Informed Reasoning: Mitigating Overthinking in Large Reasoning Models via Test-Time Precedent Learning

Qianyue Wang, Jinwu Hu, Huanxiang Lin +5

Reasoning in Large Language Models (LLMs) often suffers from inefficient long chain-of-thought traces with redundant self-exploration and validation, which inflate computational co…

cs.CL2025

SeaLLMs-Audio: Large Audio-Language Models for Southeast Asia

Chaoqun Liu, Mahani Aljunied, Guizhen Chen +4

We introduce SeaLLMs-Audio, the first large audio-language model (LALM) tailored for multiple Southeast Asian (SEA) languages-Indonesian (id), Thai (th), and Vietnamese (vi)-alongs…

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.CV2025

MMR1: Enhancing Multimodal Reasoning with Variance-Aware Sampling and Open Resources

Sicong Leng, Jing Wang, Jiaxi Li +12

Large multimodal reasoning models have achieved rapid progress, but their advancement is constrained by two major limitations: the absence of open, large-scale, high-quality long c…

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…