标题： 信息力学理论：通过不确定性测量进行泛化（学习即测量） 显示英文标题

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

摘要： 传统机器学习依赖于显式模型和领域假设，限制了灵活性和可解释性。 我们引入了一个无模型的框架，使用意外性（信息论中的不确定性）直接分析和从原始数据中进行推理，消除了分布建模，减少了偏差，并实现了高效的更新，包括对训练数据的直接编辑和删除。 通过量化不确定性来衡量相关性，该方法能够在各种任务中实现可推广的推理，包括生成推理、因果发现、异常检测和时间序列预测。 它强调可追溯性、可解释性和数据驱动的决策，为机器学习提供了一个统一且人类可理解的框架，并在大多数常见的机器学习任务中达到了或接近最先进的性能。 数学基础创造了一种“信息物理学”，使这些技术能够有效地应用于各种复杂的数据类型，包括缺失数据。 实证结果表明，这可能是在可扩展机器学习和人工智能方面的一种可行替代路径，能够保持对底层机制的人类可理解性。 显示英文摘要

摘要： 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.