Teaching a Minimalist Machine to Discover Recursive Programs for Arithmetic

arXiv:2610.06304 · cs.AI · Submitted 2026-10-05 · Read on arXiv

cs.AI

Submitted: 2026-10-05

Updated: 2026-10-05

Comments: 15 pages, 5 figures. Accepted for oral presentation at the 6th International Joint Conference on Learning and Reasoning 2026. Code: https://github.com/cognitive-modeling/minimalist-machine/tree/IJCLR-2026

Code: https://github.com/cognitive-modeling/minimalist-machine

License: http://creativecommons.org/licenses/by-sa/4.0/

The gist: Humans can often acquire and synthesize complex, recursive concepts from minimal experience.

Abstract

Humans can often acquire and synthesize complex, recursive concepts from minimal experience. Leveraging cognitive insights, we propose the Minimalist Machine, a framework for inductive program synthesis designed to model such conceptual learning. The system uses a compact relational subset of Prolog: Programs are searched within a fixed schema of body-free facts and two-body conjunctive Horn clauses. Recursion is not defined by a dedicated metarule. Instead, it emerges when a target predicate is reused inside the body of a learned clause. Inspired by a primary school curriculum, the model is taught through a human-curated, sequential introduction of new concepts in arithmetic. Starting from initially empty knowledge base, it first acquires simple structural predicates, then successor-based state transformations, and finally recursive programs for addition, subtraction, multiplication, and division. Ultimately, this approach yields the fully transparent, inductive reasoning trace necessary for human-like conceptual learning.

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