Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers
Zhaochen Su, Peng Xia, Hangyu Guo, Zhenhua Liu, Yan Ma, Xiaoye Qu, Jiaqi Liu, Yanshu Li, Kaide Zeng, Zhengyuan Yang, Linjie Li, Yu Cheng, Heng Ji, Junxian He, Yi R. Fung

TL;DR
This survey explores the evolution of multimodal reasoning in AI from static image understanding to dynamic, thinking-with-images paradigms, highlighting foundational principles, methods, benchmarks, and future challenges.
Contribution
It establishes a three-stage framework for think-with-image AI, reviews core methods, analyzes benchmarks, and outlines future research directions.
Findings
Introduction of a three-stage evolution framework
Comprehensive review of core methods at each stage
Analysis of benchmarks and future challenges
Abstract
Recent progress in multimodal reasoning has been significantly advanced by textual Chain-of-Thought (CoT), a paradigm where models conduct reasoning within language. This text-centric approach, however, treats vision as a static, initial context, creating a fundamental "semantic gap" between rich perceptual data and discrete symbolic thought. Human cognition often transcends language, utilizing vision as a dynamic mental sketchpad. A similar evolution is now unfolding in AI, marking a fundamental paradigm shift from models that merely think about images to those that can truly think with images. This emerging paradigm is characterized by models leveraging visual information as intermediate steps in their thought process, transforming vision from a passive input into a dynamic, manipulable cognitive workspace. In this survey, we chart this evolution of intelligence along a trajectory of…
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