Learning to Access Computation: Accessibility Plasticity as a Principle of Adaptive Intelligence
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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…
1Key Takeaways
- 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….
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3Why it matters
Research breakthroughs often arrive in products months later—early signals matter for strategy. arXiv ML reports that arXiv:2607.22748v1 Announce Type: new Abstract: Modern neural networks primarily adapt through parameter modification within predefined computational structures.
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