Lattice Deduction Transformers

Abstract:We introduce the Lattice Deduction Transformer (LDT), a recurrent transformer that approximates logically sound deduction by projecting its latent state through a lattice between forward passes. We train on-policy in a process that mirrors deduction in a search-based constraint solver and supervise training via a domain-agnostic approximation of the set of solution candidates based on abstract interpretation. An $800$K-parameter LDT outperforms prior small recurrent reasoners on Sudoku-Extreme with under $2\%$ of their estimated training FLOPs, solving $99.999\%$ of the $422{,}786$ test puzzles. The model is nearly always sound, in that it gives a wrong answer for $4$ of $1.27$M puzzle evaluations, and otherwise returns a correct answer or abstains. A $1.8$M-parameter LDT variant reaches $99.8\%$ accuracy on Maze-Hard. Our code is available at this http URL.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)

arXiv:2605.08605 [cs.LG]

(or arXiv:2605.08605v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.08605 Focus to learn more arXiv-issued DOI via DataCite

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