新研究将扩散模型转化为迭代推理器，解决数独和迷宫问题表现优异，无需并行回滚或外部验证器。
研究人员将持久隐藏状态添加到扩散去噪器中，移除其时间步条件，创建了一个可运行任意深度的共享更新模型。该模型在Sudoku-Extreme上达到99.90%的精确解决率，在Maze-Unique上获得98.93%的解决率。推理过程中，每步用高斯噪声替换所有非线索变量，无需渐进去噪即可实现近乎完美的求解。
Diffusion as a Training Curriculum for Timestep-Free Iterative Reasoning
Diffusion models and recursive reasoners are both iterative, but they carry information across iterations differently. We add a persistent hidden state to a diffusion denoiser and remove its timestep conditioning, leaving a single shared update that can be run to arbitrary depth. The result is an anytime solver: accuracy keeps improving with inference depth far beyond the rollout lengths and backpropagation window used in training, reaching 99.90% exact solve on Sudoku-Extreme. We also obtain 98.93% solve rate on Maze-Unique. Surprisingly, progressive denoising is unnecessary at inference: holding corruption at its maximum by replacing every non-clue variable with fresh Gaussian noise at each step retains near-perfect solving and converges to stable solutions. This simple noise-injection mechanism enables a single trajectory to efficiently explore the solution space and settle on the correct answer without parallel rollouts, candidate selection, or external verifiers required by prior reasoning models. Nonetheless, ordered annealed corruption remains critical during training, which suggests that diffusion's primary contribution to our anytime solver is not a sampling procedure at inference, but a denoising training curriculum.