Generative Recursive Reasoning
For researchers in neural reasoning and generative modeling, GRAM provides a novel method to extend recursive reasoning with stochasticity, enabling multi-hypothesis generation and improved performance on tasks requiring diverse solutions.
Generative Recursive Reasoning Models (GRAM) introduce probabilistic multi-trajectory computation into recursive latent reasoning, enabling multiple hypotheses and inference-time scaling. GRAM improves over deterministic baselines on structured reasoning and multi-solution constraint satisfaction tasks.
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 $p_θ(y \mid x)$ 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. \href{https://ahn-ml.github.io/gram-website/}{https://ahn-ml.github.io/gram-website}