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.

arXiv comment4 pages, 2 figures. Accepted as a demo at the 6th International Joint Conference on Learning and Reasoning (IJCLR 2026). Code: https://github.com/cognitive-modeling/WAMpy