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Junyeob BAEK

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Generative Recursive Reasoning
Junyeob BAEK
Mingyu Jo
Minsu Kim
Mengye Ren
Yoshua Bengio
Sungjin Ahn
How should future neural reasoning systems implement extended computation? Recursive Reasoning Models (RRMs) offer a promising alternative t… (voir plus)o 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
2026-05-18
arXiv (prépublication)
doi.org
arxiv.org
Generative Recursive Reasoning Models
Junyeob BAEK
Mingyu Jo
Minsu Kim
Yoshua Bengio
Sungjin Ahn
We introduce Generative Recursive reAsoning Models (GRAM), a recursion-based generative model that is effective for complex planning and rea… (voir plus)soning problems. GRAM reformulates recent latent recursive architectures as a stochastic generative process with probabilistic latent transitions, enabling efficient and stable computation entirely in latent space without relying on token-level sequences as in chain-of-thought (CoT) prompting. We optimize this generative recursion via amortized variational inference, allowing the model to represent and explore multiple plausible latent trajectories conditioned on the input. This formulation supports both conditional reasoning through
2026-03-04
RSI @ International Conference on Learning Representations (poster)
openreview.net
openreview.net