Teaching a Minimalist Machine to Discover Recursive Programs for Arithmetic
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.
Sources
- Why AI systems don't learn and what to do about it: Lessons on autonomous learning from cognitive science
- Inductive logic programming at 30: a new introduction
- Can humans teach machines to code?
- A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning
Related papers
- MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
- Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
- Agents' Last Exam
- The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
- MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
- Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems