MoR-MLLM: Mixture of Recursions for Efficient Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) have demonstrated remarkable reasoning capabilities across vision and language tasks. However, their massive computational and memory demands hinder real-world deployment. While recent efforts reduce costs by employing lightweight language backbones, existing paradigms remain computation-dense due to their static sparsity and depth allocation, which cannot adapt to the semantic complexity of each token. To this end, we propose MoR-MLLM, a computation-sparse MLLM based on the recent Mixture-of-Recursions (MoR) framework. MoR-MLLM introduces adaptive per-token recursion, allowing the model to dynamically adjust its recursive depth and allocate more c...
| Authors | Pengcheng Zheng, Chaoning Zhang, Jiaxin Yan et al. |
|---|---|
| Primary category | cs.CV |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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