Paper Detail
Yutong Bian, Dongjie Cheng, Heming Xia, Yongqi Li, Wenjie Li
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.
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@misc{bian2026optical,
title = {Optical Reasoning: Rethinking Images as an Expressive Reasoning Medium Beyond Text},
author = {Yutong Bian and Dongjie Cheng and Heming Xia and Yongqi Li and Wenjie Li},
year = {2026},
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},
url = {https://huggingface.co/papers/2606.09585},
keywords = {Chain-of-Thought, Large Language Models, Multimodal Large Language Models, interleaved-modal reasoning, optical reasoning, typographic-based optical reasoning, graphical-based optical reasoning, visual rationales, token efficiency, code available, huggingface daily},
eprint = {2606.09585},
archiveprefix = {arXiv},
}
{}