arXiv Machine Learning
Oct 8

The Dichotomy Between Pattern Recognition and Step-by-Step Reasoning

The paper argues that pattern recognition and step‑by‑step reasoning lie on a spectrum, with large language models (LLMs) learning the latter when the next token depends on only a few preceding tokens. It formalises reasoning traces as paths on a De Bruijn graph, showing that the number of edges is far smaller than the number of possible traces, making step‑by‑step reasoning sample‑efficient. Experiments fine‑tuning Qwen2.5‑1.5B‑Instruct demonstrate that a moderate density of states balances accuracy and robustness, and that real‑world models like Qwen3 retain most of their performance even when attention is limited to a small sliding window.

By Amrut Nadgir, Pratik Chaudhari, Vijay Balasubramanian
arXiv AI
Oct 15

Tiny Recursive Message Passing

Tiny Recursive Message Passing applies recursive transformers and deep supervision to message passing neural networks, achieving high efficiency. The approach yields about 99% test accuracy on Sudoku-Extreme with only 16k parameters, training quickly on modern GPUs or CPUs. It also demonstrates zero‑shot transfer to larger problem instances, outperforming popular neural baselines on N‑queens and Boolean satisfiability with fewer parameters.

By Tommaso Salvatori, Matteo Forasassi, Alexis Toumi, Giovanni de Felice