DiffThinker: Towards Generative Multimodal Reasoning with Diffusion Models

• URL: http://arxiv.org/abs/2512.24165v1
• Date: Tue, 30 Dec 2025 11:51:18 GMT
• Title: DiffThinker: Towards Generative Multimodal Reasoning with Diffusion Models
• Authors: Zefeng He, Xiaoye Qu, Yafu Li, Tong Zhu, Siyuan Huang, Yu Cheng,
• Abstract summary: We introduce DiffThinker, a generative multimodal reasoning framework.<n>We show it can achieve superior logical consistency and spatial precision in vision-centric tasks.
• Score: 40.38351627330629
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While recent Multimodal Large Language Models (MLLMs) have attained significant strides in multimodal reasoning, their reasoning processes remain predominantly text-centric, leading to suboptimal performance in complex long-horizon, vision-centric tasks. In this paper, we establish a novel Generative Multimodal Reasoning paradigm and introduce DiffThinker, a diffusion-based reasoning framework. Conceptually, DiffThinker reformulates multimodal reasoning as a native generative image-to-image task, achieving superior logical consistency and spatial precision in vision-centric tasks. We perform a systematic comparison between DiffThinker and MLLMs, providing the first in-depth investigation into the intrinsic characteristics of this paradigm, revealing four core properties: efficiency, controllability, native parallelism, and collaboration. Extensive experiments across four domains (sequential planning, combinatorial optimization, constraint satisfaction, and spatial configuration) demonstrate that DiffThinker significantly outperforms leading closed source models including GPT-5 (+314.2\%) and Gemini-3-Flash (+111.6\%), as well as the fine-tuned Qwen3-VL-32B baseline (+39.0\%), highlighting generative multimodal reasoning as a promising approach for vision-centric reasoning.

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