Fill the GAP: A Granular Alignment Paradigm for Visual Reasoning in Multimodal Large Language Models

arXiv:2605.12374v3 Announce Type: replace-cross Abstract: Visual latent reasoning lets a multimodal large language model (MLLM) create intermediate visual evidence as continuous tokens, avoiding external tools or image generators. However, existing methods usually follow an output-as-input latent paradigm and yield unstable gains. We identify evidence for a feature-space mismatch that can contribute to this instability: dominant visual-latent models build on pre-norm MLLMs and reuse decoder hidden states as predicted latent inputs, even though these states occupy a substantially different norm

Source: arXiv cs.LG — read the full report at the original publisher.

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