arXiv:2606.01372v1 Announce Type: new Abstract: Can neural networks learn abstract algebraic rules, or do they merely memorize training patterns? We investigate this using MNIST digits as states and modular arithmetic operations as actions in a JEPA-style latent world model. Standard supervised baselines and JEPA models with additive operation embeddings fit seen operations but fail to extrapolate reliably to unseen ones. To bridge this gap, we introduce a block-rotation predictor that imposes the circular structure of modulo-10 arithmetic in latent space. This enables strong zero-shot general
The paper addresses a core limitation of current neural networks, their inability to perform abstract reasoning and extrapolate to unseen scenarios, which is a critical frontier for advanced AI development.
This work represents a step towards AI systems that can learn and apply abstract rules, moving beyond mere pattern recognition, which is essential for more robust and generalizable AI.
Traditional neural networks often fail to extrapolate abstract rules like modular arithmetic; this research suggests a new architecture that imposes structural constraints to enable such extrapolation.
- · AI researchers
- · Deep learning frameworks
- · Sectors requiring explainable AI
- · AI models reliant solely on memorization
- · Purely data-driven approaches
- · Benchmarks favoring interpolation
AI systems will become more capable of understanding and applying underlying mathematical or logical structures.
This improved abstract reasoning could accelerate progress in AI agents and other complex autonomous systems.
It might lead to more robust and less 'brittle' AI, capable of handling novel situations with greater reliability and requiring less training data for new tasks.
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Read at arXiv cs.LG