| title | Optical Reasoning: Rethinking Images as an Expressive Reasoning Medium Beyond Text |
|---|---|
| authors | Yutong Bian, Dongjie Cheng, Heming Xia, Yongqi Li, Wenjie Li |
| date | 2026-06-08 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.09585 |
| affiliation | ModalityDance |
| upvotes | 5 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| 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. |
cite
@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},
eprint={2606.09585},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.09585}
}