| title | SanSi: A Looped Typed Decision Model for System 1.5 Thinking |
|---|---|
| authors | Shuyu Gan, Young-Jun Lee, Dongyeop Kang |
| date | 2026-10-06 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.07730 |
| affiliation | Minnesota NLP |
| upvotes | 19 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Typed decision models answer a declared question without generating text: a decision head returns a probability for each of the declared options in a single forward pass. A single pass is fast, intuitive System 1 thinking. We study what lies between one pass and generated reasoning: looping, in which the same layers are recursively applied several times before one typed readout. Each loop lets the model revise its hidden state before it commits to an answer, without generating a token; we call this System 1.5 thinking. We propose SanSi, which turns a pre-trained looped language model into a typed decision model. The option probabilities are read after every loop, and every loop is trained with a proper scoring rule, so that one model serves every budget from one loop to eight in a single run. On 10,027 test decisions from 59 sources, SanSi reaches 72.0% accuracy: 13.5 points above a non-looped model of the same shape trained with the same recipe, 5.3 points above a newer non-looped model of its size, and 1.8 points below one with three times the parameters. On two depth-controlled tasks, loops extend the solvable depth beyond the depths seen in training, where the larger single-pass model fails. Used as the judge for policy optimization with reinforcement learning, without gold answers, SanSi raises the generator's F1 by 7.7 points. |
cite
@misc{gan2026sansi,
title={SanSi: A Looped Typed Decision Model for System 1.5 Thinking},
author={Shuyu Gan and Young-Jun Lee and Dongyeop Kang},
year={2026},
eprint={2610.07730},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.07730}
}