Sakana says multiple diffusion models plus MCTS beat test-time scaling on coding and math

SakanaAILabs · x · 2026-07-21

UnMaskFork uses multiple diffusion language models and MCTS to improve coding and math

Sakana AI says its ICML 2026 paper, UnMaskFork, explores whether test-time scaling can work for masked diffusion language models (MDLMs).

Instead of increasing randomness with temperature, the method creates diversity through model switching.
Multiple MDLMs collaborate to unmask a single answer, while Monte Carlo Tree Search searches for promising generation paths.
The approach requires no extra training and no model changes; it works at inference time by combining pre-trained models.
The team says the method consistently outperforms existing test-time scaling baselines on coding benchmarks and also scales well on math tasks.
Sakana frames the work as part of its broader “collective intelligence of AI” line of research, alongside AB-MCTS and Sakana Fugu.

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