EndoCoT: Scaling Endogenous Chain-of-Thought Reasoning in Diffusion Models
Recently, Multimodal Large Language Models (MLLMs) have been widely integrated into diffusion frameworks primarily as text encoders to tackle complex tasks such as spatial reasoning. However, this paradigm suffers from two critical limitations: (i) MLLMs text encoder exhibits insufficient reasoning depth. Single-step encoding fails to activate the Chain-of-Thought process, which is essential for MLLMs to provide accurate guidance for complex tasks. (ii) The guidance remains invariant during the decoding process. Invariant guidance during decoding prevents DiT from progressively decomposing complex instructions into actionable denoising steps, even with correct MLLM encodings. To this end, we propose Endogenous Chain-of-Thought (EndoCoT), a novel framework that first activates MLLMs' reasoning potential by iteratively refining latent thought states through an iterative thought guidance module, and then bridges these states to the DiT's denoising process. Second, a terminal thought grounding module is applied to ensure the reasoning trajectory remains grounded in textual supervision by aligning the final state with ground-truth answers. With these two components, the MLLM text encoder delivers meticulously reasoned guidance, enabling the DiT to execute it progressively and ultimately solve complex tasks in a step-by-step manner. Extensive evaluations across diverse benchmarks (e.g., Maze, TSP, VSP, and Sudoku) achieve an average accuracy of 92.1%, outperforming the strongest baseline by 8.3 percentage points.
69b378c42ad6c5d4cf486a68
Xuanlang Dai
claimed_verified
2026-03-23T10:59:29.448Z
66fb03d6b505f1a04c39d935
/avatars/e9b830c460ec02037758c9b3469bb8ad.svg
Xuanlang Dai
XuanlangDai
user
XuanlangDai
69b378c42ad6c5d4cf486a69
Yujie Zhou
claimed_verified
2026-03-23T10:59:27.376Z
64b51fd8bcfd8542d6473d9a
/avatars/ceaa73b79f448996187f07733d96b800.svg
yujie
yujieouo
user
yujieouo
69b378c42ad6c5d4cf486a6a
Long Xing
69b378c42ad6c5d4cf486a6b
Jiazi Bu
claimed_verified
2026-03-23T11:03:39.068Z
6564a63de8d6a163c0a4fd2b
/avatars/a4b7cc9ea6e4023f150fd745cab3a883.svg
Azusa
Bujiazi
user
Bujiazi
69b378c42ad6c5d4cf486a6c
Xilin Wei
admin_assigned
2026-03-13T09:04:49.395Z
62eb70462f0f5e54df42f778
/avatars/456049dba67638d3cdb330cdf383f272.svg
Xilin Wei
Wiselnn
user
Wiselnn
69b378c42ad6c5d4cf486a6d
Yuhong Liu
69b378c42ad6c5d4cf486a6e
Beichen Zhang
69b378c42ad6c5d4cf486a6f
Kai Chen
69b378c42ad6c5d4cf486a70
Yuhang Zang
claimed_verified
2026-03-13T08:12:52.176Z
63859cf3b2906edaf83af9f0
https://cdn-avatars.huggingface.co/v1/production/uploads/63859cf3b2906edaf83af9f0/kajwuVzd4pDucSPlwghxo.png
Yuhang Zang
yuhangzang
user
yuhangzang
EndoCoT: Scaling Endogenous Chain-of-Thought Reasoning in Diffusion Models
Recently, Multimodal Large Language Models (MLLMs) have been widely integrated into diffusion frameworks primarily as text encoders to tackle complex tasks such as spatial reasoning. However, this paradigm suffers from two critical limitations: (i) MLLMs text encoder exhibits insufficient reasoning depth. Single-step encoding fails to activate the Chain-of-Thought process, which is essential for MLLMs to provide accurate guidance for complex tasks. (ii) The guidance remains invariant during the decoding process. Invariant guidance during decoding prevents DiT from progressively decomposing complex instructions into actionable denoising steps, even with correct MLLM encodings. To this end, we propose Endogenous Chain-of-Thought (EndoCoT), a novel framework that first activates MLLMs' reasoning potential by iteratively refining latent thought states through an iterative thought guidance module, and then bridges these states to the DiT's denoising process. Second, a terminal thought grounding module is applied to ensure the reasoning trajectory remains grounded in textual supervision by aligning the final state with ground-truth answers. With these two components, the MLLM text encoder delivers meticulously reasoned guidance, enabling the DiT to execute it progressively and ultimately solve complex tasks in a step-by-step manner. Extensive evaluations across diverse benchmarks (e.g., Maze, TSP, VSP, and Sudoku) achieve an average accuracy of 92.1%, outperforming the strongest baseline by 8.3 percentage points.
A novel framework called Endogenous Chain-of-Thought is proposed to enhance multimodal large language models' reasoning capabilities in diffusion frameworks by enabling iterative thought refinement and progressive task execution.
Multimodal Large Language Models diffusion frameworks Chain-of-Thought iterative thought guidance module terminal thought grounding module denoising process spatial reasoning Maze TSP VSP Sudoku