Learning to Access Computation: Accessibility Plasticity as a Principle of Adaptive Intelligence

A new preprint on arXiv (2607.22748v1) introduces Accessibility Plasticity, a principle of adaptive computation in which systems adapt by reorganizing which existing computations can interact, rather than only modifying computational parameters. The authors formalize this through a relationship-based operational realization and a reuse-first hierarchy, showing in proof-of-concept sequential learning tasks that accessibility adaptation reduces capability modification while maintaining performance. The work suggests accessibility as a distinct adaptive dimension for future dynamic neural systems.

arXiv:2607.22748v1 Announce Type: new Abstract: Modern neural networks primarily adapt through parameter modification within predefined computational structures. While recent methods introduce modularity, conditional computation, and parameter-efficient adaptation, they generally do not distinguish computational capability from computational accessibility as separate adaptive variables. This work introduces Accessibility Plasticity, a principle of adaptive computation in which systems adapt not only by changing what computation exists, but also by reorganizing which existing computations can interact and participate. We formalize Accessibility Plasticity through a relationship-based operational realization and establish a reuse-first hierarchy of adaptation, where accessibility modification precedes more costly capability and structural changes. A proof-of-concept evaluation on sequential learning tasks shows that accessibility adaptation can reduce capability modification while maintaining comparable task performance. These results suggest accessibility as a distinct adaptive dimension and provide a foundation for future dynamic neural systems whose computational relationships evolve with changing environments.