| title | Robust Explanatory Analysis for Structured Optimization Networks (REASON) in Learning |
|---|---|
| authors | Vahid Jebraeeli, Hamid Krim, Keisuke Fujii |
| date | 2026-10-14 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.19378 |
| upvotes | 4 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | We seek a hierarchical resolution of residual error under a composite learning objective as a principled mechanism for identifying and exploiting additional information, particularly under distribution shift and perturbation. Inspired by the System 1/System 2 paradigm, we formulate this principle as a two-loop mechanism serving a common inference goal. A fast, System 1-like loop resolves the residual addressable by the available evidence; the remaining epistemic residual triggers a medium speed, System 2-like loop that actively acquires additional evidence and measures its informational value through residual reduction. Over nested solution spaces, the medium-loop optimum is provably no worse than the fast-loop optimum. Experiments show that the resulting two-loop model consistently improves upon the fast loop at approximately half the computation of the medium loop, including under unseen corruptions, and, when provided a cleaner observation, outperforms staged classifiers and learning-to-defer methods at equal computational cost. |
cite
@misc{jebraeeli2026robust,
title={Robust Explanatory Analysis for Structured Optimization Networks (REASON) in Learning},
author={Vahid Jebraeeli and Hamid Krim and Keisuke Fujii},
year={2026},
eprint={2610.19378},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.19378}
}