Boosting Inference with Guided Reasoning: Stochastic Exploration for Recursive Models
The paper discusses a novel approach to enhancing inference in recursive neural networks through guided reasoning and stochastic exploration. This method improves accuracy in structured reasoning tasks, such as Sudoku and maze-solving, by modeling reasoning trajectories with a latent dynamical system. The authors present diagnostics that help predict the effectiveness of the inference process without requiring retraining of the model.
- ▪The proposed framework utilizes guided stochastic exploration to enhance inference in recursive architectures.
- ▪It achieves a significant accuracy improvement on Sudoku-Extreme, raising the exact-solve rate from 85.9% to 98.0%.
- ▪The framework includes three diagnostics: local stability, guide alignment, and cloud-token entropy, which assess the model's performance.
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Computer Science > Artificial Intelligence arXiv:2605.25230 (cs) [Submitted on 24 May 2026] Title:Boosting Inference with Guided Reasoning: Stochastic Exploration for Recursive Models Authors:Andrew Corbett, Archit Sood, Anna Tzatzopoulou, Sai-Aakash Ramesh, Tim Dodwell View a PDF of the paper titled Boosting Inference with Guided Reasoning: Stochastic Exploration for Recursive Models, by Andrew Corbett and 4 other authors View PDF HTML (experimental) Abstract:Recent work on recursive architectures has shown that tiny neural networks can be surprisingly powerful on structured reasoning tasks. The trick is to model reasoning trajectories with a latent dynamical system.
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