How a 10M‑Parameter Model Beats Large Models on Sudoku and ARC with Multi‑Trajectory Reasoning

The GRAM model introduced by Yoshua Bengio’s team replaces deterministic recursive updates with probabilistic multi‑trajectory sampling, enabling a 10 M‑parameter network to achieve 97 % accuracy on Sudoku‑Extreme, 52 %/11 % on ARC‑AGI, and near‑perfect results on N‑Queens and graph‑coloring, while also supporting unconditional generation tasks.

ARC‑AGIGRAMSudoku

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How a 10M‑Parameter Model Beats Large Models on Sudoku and ARC with Multi‑Trajectory Reasoning