arXiv:2603.16123v2 Announce Type: replace Abstract: Neural networks often learn the parts of a task but fail on novel combinations of those parts. We argue that this failure is architectural: a decoder generalizes compositionally only when it respects the algebraic laws of the task, i.e. when it descends from freely generated sequences to the quotient determined by those laws. We make this principle constructive by compiling Higher Inductive Type (HIT) specifications into neural architectures. Basepoints, path constructors, and 2-cells are mapped to base constraints, generator networks, struct
This paper leverages advanced mathematical concepts (Higher Inductive Types) to address a fundamental architectural limitation in neural networks, promising more robust and generalizable AI.
Architectural breakthroughs improving compositional generalization could significantly accelerate AI development, leading to more reliable and adaptable systems across various applications.
Neural network designs could move beyond current empirical approaches to incorporate algebraically rigorous principles, enabling AI that understands and manipulates relationships more effectively.
- · AI researchers
- · Deep learning practitioners
- · Sectors requiring robust AI generalization
- · AI models lacking compositional understanding
More generalizable AI models emerge that perform better on novel combinations of learned concepts.
Accelerated deployment of AI in complex, dynamic environments previously deemed too challenging due to generalization failures.
The development of AI systems capable of truly abstract reasoning and scientific discovery, bridging the gap between current AI and more human-like intelligence.
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Read at arXiv cs.LG