Year: 2026
Type: conference-paper
Abstract: Recursive models that progressively refine latent representations have demonstrated strong performance on a variety of reasoning tasks.However, these models only control whether and when to stop early, not how computation is distributed.In this work, we introduce shortcut reasoning, a framework for distilling recursive latent reasoning into a multiscale jump model that enables flexible test-time compute.We reinterpret recursive reasoning as a latent-time dynamical process and train a student model to predict the effect of multiple reasoning steps at once.To ensure robustness, we augment shortcut transitions with a repair mechanism, where a denoising variant of the base model projects latent states back onto a valid reasoning manifold.We further introduce stepwise improvement supervision, encouraging each shortcut step to increase the likelihood of the correct answer.Experiments on ARC-AGI show that our approach achieves competitive accuracy compared to recursive baselines while requiring fewer sequential updates.
Author: Shiv Shankar
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166818622