Accessibility Plasticity: New AI Principle Prioritizes Reorganization Over Retraining

Modern neural networks primarily adapt through parameter modification within fixed computational structures. While techniques like modularity and conditional computation offer improvements, they generally treat computational capability and accessibility as a single variable. Zhaowen Fan's new paper introduces Accessibility Plasticity, a principle that distinguishes these two dimensions: capability is what a system can compute, while accessibility determines which computations can interact and participate at a given time. The work formalizes this through a relationship-based operational realization and establishes a reuse-first hierarchy of adaptation, where modifying accessibility (e.g., re-routing or gating existing modules) is attempted before resorting to more costly capability or structural changes.

A proof-of-concept evaluation on sequential learning tasks demonstrates that accessibility adaptation can reduce capability modification while maintaining comparable task performance. This suggests that systems can become more efficient by reusing and reconfiguring existing computational building blocks. The paper lays a foundation for future dynamic neural systems whose computational relationships evolve with changing environments, potentially leading to AI that is more modular, adaptable, and resource-efficient without requiring constant retraining or expansion.