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

2025-10-26

AI Summary

Traditional machine learning relies on explicit parametric models and strong distributional assumptions, limiting flexibility, interpretability, and adaptability to dynamic data updates. To address these limitations, this paper proposes a model-free information-theoretic framework—*information mechanics*—that directly quantifies uncertainty in raw data via surprisal, bypassing explicit probabilistic modeling. The approach is parameter-free, traceable, and editable (supporting incremental data addition/deletion), enabling universal inference. Its core conceptual contribution redefines learning as uncertainty measurement and establishes fundamental “physical laws” at the information level, unifying diverse tasks including generative inference, causal discovery, anomaly detection, and time-series forecasting. Experiments demonstrate performance at or near state-of-the-art across multiple benchmarks; the method natively handles missing data and preserves full human interpretability throughout the inference process.

Technology Category

• Machine Learning: Information Theory
• Reasoning under Uncertainty: Relational Probabilistic Models
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Traditional machine learning relies on explicit models and domain assumptions, limiting flexibility and interpretability. We introduce a model-free framework using surprisal (information theoretic uncertainty) to directly analyze and perform inferences from raw data, eliminating distribution modeling, reducing bias, and enabling efficient updates including direct edits and deletion of training data. By quantifying relevance through uncertainty, the approach enables generalizable inference across tasks including generative inference, causal discovery, anomaly detection, and time series forecasting. It emphasizes traceability, interpretability, and data-driven decision making, offering a unified, human-understandable framework for machine learning, and achieves at or near state-of-the-art performance across most common machine learning tasks. The mathematical foundations create a ``physics'' of information, which enable these techniques to apply effectively to a wide variety of complex data types, including missing data. Empirical results indicate that this may be a viable alternative path to neural networks with regard to scalable machine learning and artificial intelligence that can maintain human understandability of the underlying mechanics.

Problem

• Introduces model-free framework using surprisal for direct data inference
• Enables generalizable inference across multiple machine learning tasks
• Provides interpretable alternative to neural networks with comparable performance

Innovation

• Model-free framework using surprisal for inference
• Quantifying relevance through uncertainty for generalization
• Mathematical foundations creating physics of information