3 papers
cs.LG2025
From Solving to Verifying: A Unified Objective for Robust Reasoning in LLMs
Xiaoxuan Wang, Bo Liu, Song Jiang +4
The reasoning capabilities of large language models (LLMs) have been significantly improved through reinforcement learning (RL). Nevertheless, LLMs still struggle to consistently v…
cs.CV2025
CAFe: Unifying Representation and Generation with Contrastive-Autoregressive Finetuning
Hao Yu, Zhuokai Zhao, Shen Yan +7
The rapid advancement of large vision-language models (LVLMs) has driven significant progress in multimodal tasks, enabling models to interpret, reason, and generate outputs across…
cs.CV2024
CompCap: Improving Multimodal Large Language Models with Composite Captions
Xiaohui Chen, Satya Narayan Shukla, Mahmoud Azab +8
How well can Multimodal Large Language Models (MLLMs) understand composite images? Composite images (CIs) are synthetic visuals created by merging multiple visual elements, such as…