[2603.19349] A Mathematical Theory of Understanding

[2603.19349] A Mathematical Theory of Understanding

arXiv - Machine Learning 4 min read

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Abstract page for arXiv paper 2603.19349: A Mathematical Theory of Understanding

Computer Science > Machine Learning arXiv:2603.19349 (cs) [Submitted on 19 Mar 2026] Title:A Mathematical Theory of Understanding Authors:Bahar Taşkesen View a PDF of the paper titled A Mathematical Theory of Understanding, by Bahar Ta\c{s}kesen View PDF HTML (experimental) Abstract:Generative AI has transformed the economics of information production, making explanations, proofs, examples, and analyses available at very low cost. Yet the value of information still depends on whether downstream users can absorb and act on it. A signal conveys meaning only to a learner with the structural capacity to decode it: an explanation that clarifies a concept for one user may be indistinguishable from noise to another who lacks the relevant prerequisites. This paper develops a mathematical model of that learner-side bottleneck. We model the learner as a mind, an abstract learning system characterized by a prerequisite structure over concepts. A mind may represent a human learner, an artificial learner such as a neural network, or any agent whose ability to interpret signals depends on previously acquired concepts. Teaching is modeled as sequential communication with a latent target. Because instructional signals are usable only when the learner has acquired the prerequisites needed to parse them, the effective communication channel depends on the learner's current state of knowledge and becomes more informative as learning progresses. The model yields two limits on the speed of lear...

Originally published on March 23, 2026. Curated by AI News.

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