Generative Recursive Reasoning

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In this episode, we explore the paper "Generative Recursive Reasoning (GRAM)," a fascinating new approach to AI reasoning co-authored by Yoshua Bengio and researchers from Mila and Samsung AI.

Most modern AI systems reason by generating more tokens. GRAM takes a different approach: instead of extending a chain of thought, it repeatedly refines an internal latent state. The key innovation is introducing probabilistic reasoning trajectories, allowing the model to explore multiple possible solutions simultaneously rather than committing to a single deterministic path.

We discuss:

• Recursive Reasoning Models (RRMs) and why they differ from traditional transformers
• The limitations of deterministic latent reasoning
• How GRAM introduces stochastic latent trajectories
• Variational inference and the roles of pθ and qϕ
• Multi-hypothesis reasoning and inference-time scaling
• Results on Sudoku, ARC-AGI, N-Queens, and other structured reasoning benchmarks
• Why latent-space reasoning may become an alternative to longer chain-of-thought prompting

The paper also demonstrates unconditional generation capabilities, suggesting a path toward reasoning systems that can both solve problems and generate structured outputs through recursive latent computation.

PDF:
Generative Recursive Reasoning

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