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+source_url: https://arxiv.org/abs/2605.19376
+ingested: 2026-05-23
+sha256: unknown
+---
+
+# Generative Recursive Reasoning
+
+- **Authors**: Junyeob Baek^1*, Mingyu Jo^1*, Minsu Kim^1,2, Mengye Ren^3, Yoshua Bengio^2,4, Sungjin Ahn^1,3†
+- **Institutions**: 1 KAIST, 2 Mila – Québec AI Institute, 3 New York University, 4 Université de Montréal
+- **arXiv**: 2605.19376 (v2, 2026-05-19)
+- **Category**: cs.AI
+- **Project Page**: https://ahn-ml.github.io/gram-website
+
+## Abstract
+
+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. GRAM turns recursive latent reasoning into probabilistic multi-trajectory computation, treating reasoning as a stochastic latent trajectory that enables 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 both conditional reasoning p_θ(y|x) and unconditional generation 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.
+
+## Key Contributions
+
+1. Formulates recursive reasoning as a latent-variable generative process
+2. Introduces width-based inference-time scaling (depth + parallel trajectories)
+3. Empirical evidence on Sudoku-Extreme, ARC-AGI, N-Queens, Graph Coloring, binarized MNIST