Teaching a Minimalist Machine to Discover Recursive Programs for Arithmetic
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 22 references that are in the map. First posted on arXiv 5 Oct 2026.
Builds on
4 of 22 references.
References · 4
- 2020A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learningcited by 87
- 2020Inductive logic programming at 30: a new introductioncited by 85
- 2026Why AI systems don't learn and what to do about it: Lessons on autonomous learning from cognitive sciencecited by 11
- 2024Can humans teach machines to code?cited by 2
Built on by
Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2026Arithmetic Pedagogy for Language Models
- 2025Thinking Machines: Mathematical Reasoning in the Age of LLMs
- 2026The Missing Primitive: Diagnosing and Repairing Mathematical Reasoning in Large Language Models
- 2024MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules
- 2024Unraveling Arithmetic in Large Language Models: The Role of Algebraic Structures
- 2025CORE: Concept-Oriented Reinforcement for Bridging the Definition-Application Gap in Mathematical Reasoning
- 2022Neural-Symbolic Recursive Machine for Systematic Generalization
- 2024System-2 Mathematical Reasoning via Enriched Instruction Tuning