[2605.19376] Generative Recursive Reasoning

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Computer Science > Artificial Intelligence

arXiv:2605.19376 (cs)

[Submitted on 19 May 2026]

Title:Generative Recursive Reasoning

Authors:Junyeob Baek, Mingyu Jo, Minsu Kim, Mengye Ren, Yoshua Bengio, Sungjin Ahn<br>View a PDF of the paper titled Generative Recursive Reasoning, by Junyeob Baek and 5 other authors

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Abstract:How should future neural reasoning systems implement extended computation? Recursive Reasoning Models (RRMs) offer a promising alternative to autoregressive sequence extension by performing iterative latent-state refinement with shared transition functions. Yet existing RRMs are largely deterministic, following a single latent trajectory and converging to a single prediction. We introduce \emph{Generative Recursive reAsoning Models (GRAM)}, a framework that turns recursive latent reasoning into probabilistic multi-trajectory computation. GRAM models reasoning as a stochastic latent trajectory, enabling multiple hypotheses, alternative solution strategies, and inference-time scaling through both recursive depth and parallel trajectory sampling. This yields a latent-variable generative model supporting conditional reasoning via $p_\theta(y \mid x)$ and, with fixed or absent inputs, unconditional generation via $p_\theta(x)$. Trained with amortized variational inference, GRAM improves over deterministic recurrent and recursive baselines on structured reasoning and multi-solution constraint satisfaction tasks, while demonstrating an unconditional generation capability. \href{this https URL}{this https URL}

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Artificial Intelligence (cs.AI)

Cite as:<br>arXiv:2605.19376 [cs.AI]

(or<br>arXiv:2605.19376v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2605.19376

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Junyeob Baek [view email]<br>[v1]<br>Tue, 19 May 2026 05:20:56 UTC (7,933 KB)

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