arXiv:2607. 04505v1 Announce Type: new Abstract: We advance the hypothesis that human mathematical reasoning, constrained by both the undecidability and the computational intractability of even modest logical fragments, relies fundamentally on pattern matching from domains external to pure deduction.
By Charanjit S. Jutla, Vimal Sharma
arXiv:2606. 08532v5 Announce Type: replace Abstract: Modern artificial intelligence excels at prediction but cannot explain.
By Lei Lin, Xinlong Pan, Ronghao Wang, Chunbao Zhou, Jue Wang, Yangang Wang, Ivana Rasovska
arXiv:2607. 05168v1 Announce Type: new Abstract: Why do intelligent systems need to perform explicit symbolic reasoning?
By Jun Sun
arXiv:2606. 17289v1 Announce Type: new Abstract: AI systems based on artificial neural networks are being developed with aspirations of pushing the boundary of human mathematical knowledge.
By Phoebe Zeng, Thomas L. Griffiths, Brenden M. Lake
arXiv:2607. 01571v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning enables large language models (LLMs) to solve complex problems by generating intermediate reasoning steps.
By Aria Masoomi, Mahsa Bazzaz, Adel Javanmard, Vahab Mirrokni
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