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We introduce Generative Recursive reAsoning Models (GRAM), a recursion-based generative model that is effective for complex planning and rea… (see more)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)