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WAMpy: Efficient Synthesis of Prolog Programs in Python

Dominik Magiera, Lukas Röhrig, Frank Jäkel

Latestcs.CLcs.LGcs.AIcs.CV
arXiv ID
2610.03234 v1
Category
Submitted
2026-10-02

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.

Comment: 4 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

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