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On September 7, 2026, arXiv published a study on artificial intelligence reasoning mechanisms. The study indicated that the slowdown in the speed of modern reasoning models when handling complex tasks such as Sudoku, maze solving, visual puzzles, and mathematical logic is not an accidental failure, but rather results from their being trapped in the “fractal basin” of dynamical systems. The study found that as the difficulty of the task increases, the time taken for the model to be caught at鞍 points near the potential correct solution prolongs, leading to increased transient chaos and a significant slowdown in reasoning. This phenomenon was identified as a new category of dynamical systems, confirming that the slowdown in reasoning is an inevitable result of increasing task difficulty.
arXiv:2609.04963v1 发布新研究指出，推理模型在复杂任务上表现出的减速现象源于其陷入鞍点附近的瞬态混沌。该研究发现，Sudoku、迷宫求解、视觉谜题及数学逻辑等多样化任务中，领先推理模型均表现为具有分形盆地的动力系统，且分形度随任务难度增加而提升。这种瞬态混沌由模型在接近问题潜在正确解的鞍点处被长时间捕获所致，导致推理速度下降。研究证实，现代人工智能模型中的推理减速是问题难度的必然结果，并将推理痕迹确立为一种丰富的新动力学系统类别。