arXiv:2609.33149v2 Announce Type: replace
Abstract: A common principle of effective learning is to practice material that is neither already mastered nor too difficult to permit progress. We ask how...
By Hongbo Chen, Guohua Lu, Ting Dang, Hong Jia
Flow Reasoning Models (FRMs) are a new framework that turns continuous flow models into efficient recurrent reasoners for structured tasks. By self‑conditioning a flow model on its own past outputs, FRMs iteratively refine solutions, allowing parallel decision making and revision. The authors introduce Fixed‑Point Forcing (FPF) to mitigate exposure bias at deeper recursion, and report near‑perfect solve rates on Sudoku‑Extreme, Zebra, and Maze‑Unique, outperforming existing masked‑diffusion and specialized baselines while using far fewer inference FLOPs.
By Alec Helbling, Andrey Bryutkin, Mauro Martino, Duen Horng Chau, Nima Dehmamy, Hendrik Strobelt
The paper reports that reasoning models in AI exhibit transient chaos, a phenomenon linked to computational complexity. It finds that these models behave as dynamical systems with fractal basins, and that the fractality grows with task difficulty across domains such as Sudoku, maze solving, visual puzzles, and mathematical logic. The study attributes slowdowns to the models becoming trapped near saddle points representing nearly‑correct solutions.
By Jeffrey Lai, Anthony Bao, John Quinn, William Gilpin
arXiv:2609.36826v1 Announce Type: new
Abstract: Video generation models have demonstrated emerging zero-shot capabilities for visual reasoning, perception, and other vision tasks. However, diffusion-...
By Zhenghao Ni, Weimin Qiu, Meng Tang
arXiv:2506.21734v4 Announce Type: replace
Abstract: Reasoning, the process of devising and executing complex goal-oriented action sequences, remains a critical challenge in AI. Current large language...
By Guan Wang, Jin Li, Yuhao Sun, Xing Chen, Changling Liu, Yue Wu, Meng Lu, Sen Song, Yasin Abbasi Yadkori
arXiv:2609.39967v1 Announce Type: cross
Abstract: Recursive reasoning models apply a small shared Transformer block many times to refine a latent state. This gives them large effective depth with few...
By Yuliana Shakhvalieva, Dmitrii Kharchev, Viacheslav Bezrukov, Inessa Fedorova, Dmitry Bocharov, Ivan Oseledets, Valerii Ternovskii