This repository contains the "Speed is Confidence" research paper and its associated experiments.
Biological neural systems must be fast but are energy-constrained. Evolution's solution: act on the first signal. Winner-take-all circuits and time-to-first-spike coding implicitly treat when a neuron fires as an expression of confidence.
We apply this principle to ensembles of Tiny Recursive Models (TRM) [Jolicoeur-Martineau et al., 2025]. On Sudoku-Extreme, halt-first selection achieves 97% accuracy vs. 91% for probability averaging--while requiring 10x fewer reasoning steps. A single baseline model achieves 85.5% +/- 1.3%.
Can we internalize this as a training-only cost? Yes: by maintaining K=4 parallel latent states but backpropping only through the lowest-loss "winner," we achieve 96.9% +/- 0.6% accuracy--matching ensemble performance at 1x inference cost, with less than half the variance of the baseline. A key diagnostic: 89% of baseline failures are selection problems, revealing a 99% accuracy ceiling.
As in nature, this work was also resource constrained: all experiments used a single RTX 5090. A modified SwiGLU [Shazeer, 2020] made Muon [Jordan et al., 2024] and high LR viable, enabling baseline training in 48 minutes and full WTA (K=4) in 6 hours--compared to TRM's 20 hours on an L40S.
sudo apt install curl -y
curl -LsSf https://astral.sh/uv/install.sh | sh
uv sync
uv run code/sudoku/x182.py # Train K=4, for example.Much the code here builds on the excellent work of Alexia Jolicoeur-Martineau available from TinyRecursiveModels.
If you find our work useful, please consider citing:
@misc{dillon2026speedisconfidence,
title={Speed is Confidence},
author={Joshua V. Dillon},
year={2026},
eprint={2601.19085},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2601.19085},
}and the Tiny Recursive Models paper:
@misc{jolicoeurmartineau2025morerecursivereasoningtiny,
title={Less is More: Recursive Reasoning with Tiny Networks},
author={Alexia Jolicoeur-Martineau},
year={2025},
eprint={2510.04871},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2510.04871},
}Apache-2.0