Compositionality Emerges in a Narrow Depth-Connectivity Regime: Architecture Constraints and Solution Manifolds
arXiv:2606.19941v1 Announce Type: new Abstract: Compositionality is believed to be the foundation for generalization, enabling models to reuse meaningful primitives in novel combinations. Yet, models trained with standard gradient-based optimization rarely, and often only weakly, exhibit compositional internal structure, and it remains unclear how or why such compositionality forms. In this work, we show that compositionality emerges in a narrow connectivity-depth sweet spot. Along the connectivity axis, compositionality only appears in some specifically sparse networks, heavily depends on whi
This research provides a theoretical understanding of how compositionality, a key aspect of advanced AI, emerges in specific neural network architectures.
A strategic reader should care because understanding the architectural constraints for compositional AI can significantly accelerate the development of more generalizable and efficient AI models.
This research shifts our understanding by identifying specific depth and connectivity regimes where compositionality naturally arises, guiding future AI architecture design.
- · AI researchers
- · Deep learning framework developers
- · AI hardware manufacturers
- · Developers relying solely on brute-force scaling
- · AI models lacking generalizability
Architectural design for neural networks will begin to prioritize sparse and specific connectivity patterns to foster compositionality.
AI models capable of true compositional reasoning will emerge, leading to breakthroughs in complex problem-solving and AI agent development.
The increased efficiency and generalizability of AI could accelerate automation across various sectors, impacting labor markets and economic structures.
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Read at arXiv cs.LG