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arXiv AI By Christopher J. Hazard, Michael Resnick, Jacob Beel, Jack Xia, Cade Mack, Dominic Glennie, Matthew Fulp, David Maze, Andrew Bassett, Martin Koistinen

A Theory of the Mechanics of Information: Generalization Through Measurement of Uncertainty (Learning is Measuring)

Read the original on arXiv AI →

The paper proposes a model‑free, instance‑based learning framework that uses surprisal—an information‑theoretic measure of uncertainty—to analyze raw data directly, bypassing explicit distribution modeling. By quantifying relevance through uncertainty, the method supports generalizable inference across diverse tasks such as generative modeling, causal discovery, anomaly detection, and time‑series forecasting, while offering traceability and interpretability. The authors claim state‑of‑the‑art performance on anomaly detection and some supervised tasks, and argue that the mathematical foundations create a ‘physics’ of information that can underpin scalable, human‑understandable AI.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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