mlsanityData to 14 Oct 2026

Teaching a Minimalist Machine to Discover Recursive Programs for Arithmetic

arXiv 2610.06304 ↗First posted 5 Oct 2026cs.AIcited by 1

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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.

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4 of 22 references.

References · 4

  1. 2020A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learningcited by 87
  2. 2020Inductive logic programming at 30: a new introductioncited by 85
  3. 2026Why AI systems don't learn and what to do about it: Lessons on autonomous learning from cognitive sciencecited by 11
  4. 2024Can humans teach machines to code?cited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

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  1. 2026Arithmetic Pedagogy for Language Models
  2. 2025Thinking Machines: Mathematical Reasoning in the Age of LLMs
  3. 2026The Missing Primitive: Diagnosing and Repairing Mathematical Reasoning in Large Language Models
  4. 2024MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules
  5. 2024Unraveling Arithmetic in Large Language Models: The Role of Algebraic Structures
  6. 2025CORE: Concept-Oriented Reinforcement for Bridging the Definition-Application Gap in Mathematical Reasoning
  7. 2022Neural-Symbolic Recursive Machine for Systematic Generalization
  8. 2024System-2 Mathematical Reasoning via Enriched Instruction Tuning