| title | Generative Recursive Reasoning |
|---|---|
| authors | Junyeob Baek, Mingyu Jo, Minsu Kim, Mengye Ren, Yoshua Bengio, Sungjin Ahn |
| date | 2026-05-20 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.19376 |
| upvotes | 31 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| 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 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_θ(y mid x) and, with fixed or absent inputs, unconditional generation via p_θ(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. https://ahn-ml.github.io/gram-website |
cite
@misc{baek2026generative,
title={Generative Recursive Reasoning},
author={Junyeob Baek and Mingyu Jo and Minsu Kim and Mengye Ren and Yoshua Bengio and Sungjin Ahn},
year={2026},
eprint={2605.19376},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.19376}
}