aiqbench analysis
This paper presents an empirical study on "Nested Shared-Operator Recursion" applied to ARC-AGI-1, investigating whether integrating persistent intermediate states improves predictive performance across Hierarchical, Tiny, and Universal Reasoning Models. The experiments reveal no consistent advantage in token accuracy or exact solving under constrained single-GPU conditions, leaving the hypothesis open for further confirmatory testing with more rigorous baselines.
Nested Shared-Operator Recursion on ARC-AGI-1: An Empirical Study of HRM, TRM and URM
arXiv:2610.26666v1 Announce Type: new Abstract: 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.
Abstract: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.
Machine Learning (cs.LG)
arXiv:2610.26666 [cs.LG]
(or arXiv:2610.26666v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.26666 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 259 words