Optical Reasoning: Rethinking Images as an Expressive Reasoning Medium Beyond Text
2026-06-08 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors explore whether images alone can be used to show reasoning steps, instead of just text. They call this idea optical reasoning and test two methods: one that arranges text visually and another that mixes graphics with text. Their experiments show that using images to explain reasoning works as well or better than traditional text explanations, while using fewer tokens. This suggests images can be a more efficient way to represent reasoning in both language and multimodal tasks.
Chain-of-ThoughtLarge Language ModelsMultimodal ReasoningOptical ReasoningVisual LayoutsToken EfficiencyRationale EncodingGraphical ReasoningLanguage TasksMultimodal Tasks
Authors
Yutong Bian, Dongjie Cheng, Heming Xia, Yongqi Li, Wenjie Li
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 as a standalone reasoning medium. We instantiate this concept with two variants: typographic-based optical reasoning, which optimizes visual layouts for compact rationale rendering, and graphical-based optical reasoning, which composes text and graphical elements into structured visual rationales. Across mathematical, scientific, and interleaved-modal reasoning benchmarks, optical reasoning can match or even exceed traditional text reasoning while reducing reasoning tokens by an average of 28.57% on language tasks and 16% on multimodal tasks, achieving 1.96 times the token efficiency of text reasoning. These results show that images can effectively and efficiently encode rationales while providing a unified visual canvas for reasoning.