Recursive Reasoning Models

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11 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Resample the Answer: Where to Spend Test-Time Compute in Recursive Reasoning
Oct 12, 2026Maxime IllouzRecursive Reasoning Models Inference-Time Search
Concordia University · Mila – Québec AI Institute · Montréal, Canada
Pretraining and Test-Time Training Budgets Matter for ARC: A Controlled Study of VARC and TRM
Oct 9, 2026Adrien Deliege, Marc Van DroogenbroeckRecursive Reasoning Models Abstract Reasoning
Montefiore Institute of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium
What Limits Recursive Reasoning Models: Optimization, Architecture and Test-Time Scaling
Sep 30, 2026Yuliana Shakhvalieva, Dmitrii Kharchev, Viacheslav Bezrukov +4Recursive Reasoning Models Neural Algorithmic Reasoning
RND NLP, DAIMLD, Russian Federation
Energy-guided Recursive Model
Jul 11, 2026Yifei Zhao, Ying TangLanguage Modeling Recursive Language Models
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China · School of Physics, University of Electronic Science and Technology of China, Chengdu 611731, China
What Survives When You Compress a Recursive Reasoner for the Edge?
Jun 25, 2026Pearse Jim, Steven Kolawole, Opegbemi Matthias Busoye +2Model Compression Efficient Neural Network Inference
ML Collective · Carnegie Mellon University
Boosting Inference with Guided Reasoning: Stochastic Exploration for Recursive Models
May 24, 2026Andrew Corbett, Archit Sood, Anna Tzatzopoulou +2Inference-Time Search Recursive Reasoning Models
digiLab, UK · University of Bristol, UK
Interaction Locality in Hierarchical Recursive Reasoning
May 20, 2026Yosuke Miyanishi, Tetsuro MorimuraRecursive Reasoning Models Spatial Reasoning
CyberAgent Inc.
Probabilistic Tiny Recursive Model
May 19, 2026Amin Sghaier, Ali Parviz, Alexia Jolicoeur-MartineauRecursive Reasoning Models Test-Time Scaling
Mila – Quebec AI Institute ILLS & ETS Montreal · Independent
Generative Recursive Reasoning
May 19, 2026Junyeob Baek, Mingyu Jo, Minsu Kim +3Latent Variable Models Recursive Reasoning Models
KAIST · Mila – Québec AI Institute · New York University +1
Form Follows Function: Recursive Stem Model
Mar 3, 2026Navid HakimiRecursive Reasoning Models Neural Algorithmic Reasoning
Recursive Models for Long-Horizon Reasoning
Mar 2, 2026Chenxiao Yang, Nathan Srebro, Zhiyuan LiRecursive Language Models Language Modeling
Toyota Technological Institute at Chicago

Oct 12, 2026J/K move · Enter open · S save

Resample the Answer: Where to Spend Test-Time Compute in Recursive Reasoning

Maxime Illouz

Concordia University · Mila – Québec AI Institute · Montréal, Canada

Recursive Reasoning Models Inference-Time Search Algorithmic Reasoning Adaptive Computation

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Recursive reasoning models such as the Tiny Recursive Model (TRM) iteratively refine two latent states: an answer state y, decoded into the output, and a reasoning state z, which is never decoded. Many of their failures have been attributed to bad basins, regions of latent space that trap the recursion on wrong answers. Recent test-time approaches perturb y, z or both to recover these failures, but the impact of this choice on performance, test-time compute efficiency and scaling properties remains unclear. Through controlled experiments on TRM, we find that a failed trajectory carries its failure in its answer state. At equal compute, in depth and in width, resampling y recovers more failures than any perturbation of z we tested, and the performance gap widens as the test-time compute budget grows. Our approach, Gated Answer Resampling (GAR), pairs this resampling of the answer state with the model's own halting head and restarts only the puzzles that the head rejects. With at most 64 restarts per puzzle and no training or hyperparameter search, GAR takes TRM from 85.9% to 99.4% on Sudoku-Extreme and from 68.8% to 85.6% on Pencil Puzzle Bench validation. On the same checkpoints, GAR also outperforms Probabilistic TRM (PTRM), which perturbs z at each step, even when PTRM uses 42 to 197 times as much compute.