WAMpy: Efficient Synthesis of Prolog Programs in Python

Abstract:We present WAMpy, a Python framework optimized for synthesizing Prolog programs. Unlike general-purpose Prolog systems, WAMpy targets workloads that repeatedly generate and evaluate small candidate programs. WAMpy compiles Prolog clauses into NumPy array-based WAM instructions and supports partial recompilation of hypotheses against fixed background knowledge. Performance-critical routines are accelerated using Numba just-in-time (JIT) compilation. In a benchmark of repeated compilation-and-evaluation workloads, WAMpy improves end-to-end performance compared with SWI-Prolog accessed from Python using Janus.

4 pages, 2 figures. Accepted as a demo at the 6th International Joint Conference on Learning and Reasoning (IJCLR 2026). Code: this https URL

Programming Languages (cs.PL); Artificial Intelligence (cs.AI)

arXiv:2610.03234 [cs.PL]

(or arXiv:2610.03234v1 [cs.PL] for this version)

https://doi.org/10.48550/arXiv.2610.03234 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.