Recursive Reasoning Models

Latest papers

12 papers3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Nested Shared-Operator Recursion on ARC-AGI-1: An Empirical Study of HRM, TRM and URM
Oct 19, 2026Sara Neri, Eugenio Gaeta, Andrea Ceschini +2Recursive Reasoning Models Abstract Reasoning
University of Padova Padova, Italy · Universidad Politécnica de Madrid Madrid, Spain · Escuela Técnica Superior de Ingenieros de Telecomunicación Universidad Politécnica de Madrid Av. Complutense, 30 28040 Madrid, Spain +2
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 19, 2026J/K move · Enter open · S save

Nested Shared-Operator Recursion on ARC-AGI-1: An Empirical Study of HRM, TRM and URM

Sara Neri, Eugenio Gaeta, Andrea Ceschini, Andrea Facchinetti+1

University of Padova Padova, Italy · Universidad Politécnica de Madrid Madrid, Spain · Escuela Técnica Superior de Ingenieros de Telecomunicación Universidad Politécnica de Madrid Av. Complutense, 30 28040 Madrid, Spain+2

Recursive Reasoning Models Abstract Reasoning ARC-AGI

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Recursive reasoning models refine persistent internal states by repeatedly applying learned operators. We investigate whether adding a persistent intermediate state that integrates repeated updates of a faster state through the same operator improves predictive performance relative to each model family's baseline at matched parameter counts. We define this organisation operationally and implement it within three architectures: the Hierarchical Reasoning Model (HRM), Tiny Recursive Model (TRM) and Universal Reasoning Model (URM). Using the official codebases on ARC-AGI-1, we compare each baseline with its parameter-matched nested variant. We evaluate held-out queries from tasks whose demonstrations were available during training. The experiments use a single-GPU budget far below the published training protocols. Under the tested conditions, nested variants show no consistent advantage in token accuracy, predictive loss or exact solving, which remains near zero for all configurations. The results therefore leave the hypothesis open. We specify a confirmatory comparison, with an informative baseline, multiple seeds, controls that isolate nesting and predefined criteria, to determine whether this organisation merits further development.