Vision Inference Former: Sustaining Visual Consistency in Multimodal Large Language Models
In recent years, multimodal large language models (MLLMs) have achieved remarkable progress, primarily attributed to effective paradigms for integrating visual and textual information. The dominant connector-based paradigm projects visual features into textual sequence, enabling unified multimodal alignment and reasoning within a generative architecture. However, our experiments reveal two key limitations: (1) Although visual information serves as the core evidential modality in MLLMs, it is treated on par with textual tokens, diminishing the unique contribution of the visual modality; (2) As generation length increases, particularly within a limited context window, the model's dependence...
| Authors | Xinpeng Dong, Min Zhang, Kairong Han et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 May 2026 |
| Last updated at source | 2 June 2026 |
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