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

5 citations · 5 across the 5 of their papers we have counts for

collaborators

10 papers

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.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…

cs.CV2025

VL-Cogito: Progressive Curriculum Reinforcement Learning for Advanced Multimodal Reasoning

Ruifeng Yuan, Chenghao Xiao, Sicong Leng +9

Reinforcement learning has proven its effectiveness in enhancing the reasoning capabilities of large language models. Recent research efforts have progressively extended this parad…

cs.CL20255 cited

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…