arXiv:2606.09585v1 Announce Type: new Abstract: Chain-of-Thought (CoT) improves the performance of Large Language Models (LLMs) and has been extended to Multimodal Large Language Models (MLLMs). More recent work further moves from text-based multimodal reasoning toward interleaved-modal reasoning, where intermediate steps can incorporate both textual rationales and visual evidence. In this work, we propose a bolder and more ambitious idea: could images alone serve as the reasoning medium for both language and multimodal tasks? To explore this, we propose optical reasoning, which treats images
The paper builds on recent advancements in Chain-of-Thought reasoning for LLMs and MLLMs, extending the conceptual frontier by proposing images as a primary reasoning medium.
This research introduces a novel paradigm for AI reasoning, potentially enabling more efficient and perhaps more intuitive processing of multimodal information, broadening AI application scope.
Traditional text-centric or interleaved multimodal reasoning might be augmented or even supplanted by image-based reasoning for certain tasks, shifting development priorities.
- · AI researchers in multimodal AI
- · Developers of visual reasoning systems
- · Industries reliant on visual data analysis
- · Purely text-based reasoning models
- · Companies slow to adapt to multimodal AI advancements
AI models will likely become more proficient at understanding and generating insights directly from visual inputs.
This could lead to new forms of human-computer interaction and data representation, leveraging visual metaphors.
Optical reasoning might enable the development of more generalizable AI that can learn from the structure of information, irrespective of its textual or visual encoding.
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Read at arXiv cs.AI