Learning to Access Computation: Accessibility Plasticity as a Principle of Adaptive Intelligence
This paper introduces Accessibility Plasticity, a principle where neural networks adapt not only by changing computation parameters but also by reorganizing which existing computations interact. A proof-of-concept on sequential learning shows reduced capability modification with maintained performance.
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[Submitted on 23 Jul 2026]
Title:Learning to Access Computation: Accessibility Plasticity as a Principle of Adaptive Intelligence
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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.
Comments: 22 pages, 6 figures, 2 tables
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
Cite as: arXiv:2607.22748 [cs.LG]
(or arXiv:2607.22748v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.22748
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zhaowen Fan [view email] [v1] Thu, 23 Jul 2026 09:48:11 UTC (2,946 KB)
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