Multimodal large language models can recognize visual content yet struggle to reason reliably over it across multiple steps. In counting, a model may see all objects but lose track of which ones it has counted. We argue that such failures, commonly attributed to perception, reveal a reference gap: the failure to bind l...
Gui-Zhen Chen, Wei-Wen Xu, Wo-Tao Yin et al.· 0 citations
This work proposes Interleaved Distillation and Reinforcement Learning (IDRL), a novel post-training paradigm that tightly integrates on-policy distillation and reinforcement learning within a single training loop and enables more effective knowledge transfer, greater optimization stability, and more precise credit ass...
Heyu Chen, Xiao-Han Lan, Jia-Xin Li et al.· 0 citations
This work proposes a comprehensive data-curation procedure that efficiently acquires rich medical knowledge data not only from medical imaging but also from extensive medical texts and general domain data and develops a medical-specialized MLLM, Lingshu-Med, which consistently outperforms existing multimodal baselines...
Wei-Wen Xu, Hou-Pong Chan, Long Li et al.· IEEE Transactions on Pattern...· 0 citations
The causes of modal divergence are probed, offering insights into fostering culturally robust MLLMs, and a Multilingual, Multimodal Alignment framework for Cultural grounding evaluation is proposed.
Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty et al.· Annual Meeting of the Associ...· 0 citations
Experiments across video understanding and reasoning benchmarks show that the Evidence-Grounded Self-Teacher framework consistently improves upon Standard OPSD across multiple backbones and achieves performance comparable to GRPO while requiring substantially less training time, establishing an effective and efficient...
Zi-Yue Wang, Shi-Qi Huang, Wei-Wen Xu et al.· 5 citations
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