Generative Recursive Reasoning
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 ...
| Authors | Junyeob Baek, Mingyu Jo, Minsu Kim et al. |
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| Primary category | cs.AI |
| Published | 19 May 2026 |
| Last updated at source | 15 October 2026 |
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