From Specification to Architecture: A Theory Compiler for Knowledge-Guided Machine Learning
Theory-guided machine learning has demonstrated that including authentic domain knowledge directly into model design improves performance, sample efficiency and out-of-distribution generalisation. Yet the process by which a formal domain theory is translated into architectural constraints remains entirely manual, specific to each domain formalism, and devoid of any formal correctness guarantee. This translation is non-transferable between domains, not verified, and does not scale. We propose the Theory Compiler: a system that accepts a typed, machine-readable domain theory as input and automatically produces an architecture whose function space is provably constrained to be consistent wit...
| Authors | Asela Hevapathige, Yu Xia, Sachith Seneviratne et al. |
|---|---|
| Primary category | cs.LG |
| Published | 15 March 2026 |
| Last updated at source | 15 March 2026 |
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