Abstract Reasoning

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

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
Structured Sparse Memory for Recurrent Reasoning
Sep 27, 2026Zixuan Zhao, Samuel Wheeler, Neil Getty +3Memory-Augmented Neural Networks Recurrent Neural Networks
Department of Computer Science University of Chicago Chicago, IL 60637 · Argonne National Laboratory Lemont, IL 60439
PotARCin: Multi-Dimensional Evaluation of Skill Acquisition in Abstract Reasoning Tasks
Sep 23, 2026Claas Beger, Ryan Yi, Melanie MitchellAbstract Reasoning LLM Evaluation
Santa Fe Institute
BDH-CQ: In-Context Learning with Recurrent Latent Reasoning
Aug 10, 2026Björn Engdahl, Adrian Kosowski, Jan Chorowski +6Cost-Aware Inference Abstract Reasoning
New York University
GEB-Bench: Abstract Structures Told in Many Voices
Aug 4, 2026Tong Zhang, Zhiyuan Shi, Yun Peng +1Analogical Reasoning Abstract Reasoning
Fudan University · Peking University
Cost-Effective Agent Harnesses for Abstract Reasoning and Generalization on ARC-AGI-1
Jul 7, 2026Kabir Moghe, Peter ChinLLM-Based Program Synthesis Abstract Reasoning
Department of Computer Science Dartmouth College
Auto-Relational Reasoning
Apr 29, 2026Ioannis Konstantoulas, Dimosthenis Tsimas, Pavlos Peppas +1Abstract Reasoning Relational Reasoning
Department of Electrical and Computer Engineering, University of Patras, Greece
One Step Forward and K Steps Back: Better Reasoning with Denoising Recursion Models
Apr 20, 2026Chris Cameron, Wangzheng Wang, Nikita Ivanov +3Abstract Reasoning Recurrent Transformers
Huawei Technologies · University of Waterloo · University of Toronto
The ARC of Progress towards AGI: A Living Survey of Abstraction and Reasoning
Mar 9, 2026Sahar Vahdati, Andrei Aioanei, Haridhra Suresh +1Abstract Reasoning ARC-AGI
TIB - Leibniz Information Centre for Science and Technology · L3S Research Center, Leibniz University of Hannover · Dresden University of Technology +1
Recursive Inference Machines for Neural Reasoning
Mar 5, 2026Mieszko Komisarczyk, Saurabh Mathur, Maurice Kraus +2Abstract Reasoning Neural Algorithmic Reasoning
Department of Computer Science, Technical University of Darmstadt, Germany · Department of Computer Science, The University of Texas at Dallas, USA · Hessian Center for Artificial Intelligence (hessian.ai), Darmstadt, Germany +1
Exploring Human Behavior During Abstract Rule Inference and Problem Solving with the Cognitive Abstraction and Reasoning Corpus
Feb 25, 2026Caroline Ahn, Quan Do, Leah Bakst +4Inductive Inference Abstract Reasoning
Graduate Program for Neuroscience, Boston University, Boston, MA, USA · Cognitive Neuroimaging Center, Boston University, Boston, MA, USA · Center for Systems Neuroscience, Boston University, Boston, MA, USA +1

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Pretraining and Test-Time Training Budgets Matter for ARC: A Controlled Study of VARC and TRM

Adrien Deliege, Marc Van Droogenbroeck

Montefiore Institute of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium

Recursive Reasoning Models Abstract Reasoning Test-Time Training Visual Autoregressive Models

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The Abstraction and Reasoning Corpus (ARC) is increasingly used to evaluate whether neural networks can infer task-specific transformations from few demonstrations. Recent ARC models differ in architecture, but also in broader training and evaluation protocols, including pretraining data, data augmentation, test-time setup, and optimization budgets, making conclusions about architecture benefits debatable. In this work, we study how the performance of the vision-inspired VARC and recursion-based TRM models is affected by pretraining and test-time training (TTT) budgets under a common inductive protocol on ARC-AGI-1. We cross five pretraining budgets with four TTT budgets for each test task. Each budget specifies a task-variant count and total sample presentations, defining the controlled training budgets. We observe that increasing TTT sample presentations improves the standard ARC pass@2 metric in all settings for both models. Increasing the TTT task-variant count consistently helps only TRM, whereas VARC often performs better with fewer variants. VARC also remains competitive without pretraining, while TRM benefits much more from it. These results show that pretraining and TTT budgets affect ARC evaluation and motivate reporting at comparable budgets.