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
The paper identifies a core technical challenge in multimodal large language models (MLLMs), specifically a feature-space mismatch, which explains previous instability in visual reasoning and calls for new paradigms.
Improving visual reasoning in MLLMs is critical for advancing general AI capabilities, enabling more sophisticated human-computer interaction and automation across various industries.
The proposed 'Granular Alignment Paradigm' moves beyond current output-as-input latent methods by addressing fundamental normalization and alignment issues, potentially leading to more stable and effective visual reasoning in MLLMs.
- · AI research institutions
- · Multimodal AI developers
- · Generative AI platforms
- · Computer vision sector
- · Developers relying on unstable MLLM visual reasoning
- · Models with unaddressed feature-space mismatches
More reliable visual understanding and generation capabilities become accessible in advanced AI models.
This improved reliability accelerates the development of advanced AI agents capable of complex visual tasks and interaction.
Enhanced visual reasoning could lead to new applications in robotics, design, and scientific discovery, bridging data types more effectively.
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Read at arXiv cs.LG