Newest papers

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

Sara Neri, Eugenio Gaeta, Andrea Ceschini, Andrea Facchinetti, Giuseppe Fico · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26666 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

重复推理模型通过反复应用学习操作员来改进持久的内部状态。我们调查是否通过相同操作员整合重复更新更快状态的持久中间状态会改善每个模型家族基线的预测性能,以匹配的参数计数。我们操作性地定义了这个组织,并在三个架构中实施它: Hierarchical Reasoning Model (HRM)、Tiny Recursive Model (TRM) 和 Universal Reasoning Model (URM)。 使用ARC-AGI-1上的官方代码库,我们将每个基线与其参数匹配的嵌入式变量进行比较。 我们在培训期间可用的示范式的任务中评估了持有的查询。 实验使用了一个单一的GPU预算远低于发布的培训协议。 在测试条件下,嵌入式

Machine translation (Meta M2M100); the English original follows.

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.

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Topics: Nesting (process), Computer science, Recursion (computer science), Baseline (sea), Operator (biology), Zero (linguistics)

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