AI 中文总结
针对智能系统潜在语义状态组织缺乏合理解释的问题，提出语义最小能量原理，通过变分框架阐述，统一语义相关过程并产生理论预测，为研究语义智能提供第一性原理基础，虽有待验证但意义重大。
AI 中文摘要
尽管人工智能和认知神经科学取得了显著进展，但尚无普遍接受的第一性原理解释智能系统为何以当前方式组织潜在语义状态。现有框架虽有助于理解通信、学习和预测，但未明确解释语义智能的出现和组织。本文提出语义最小能量原理（SLEP），即智能系统通过最大化语义效用并逐步最小化语义、预测和计算能量来演化内部表示。我们在变分框架中阐述该假设，语义认知由语义作用泛函支配，其平稳解定义了潜在语义流形上的有效轨迹。这一表述产生了一系列理论预测，包括语义几何、语义热力学和低能量潜在语义状态。SLEP在一个通用数学框架中统一了语义抽象、推理、规划和通信，同时为人工和生物智能生成了可实验验证的预测。尽管该假设有待严格验证，但它为从第一性原理角度研究语义智能提供了有原则的基础。
英文摘要
Despite remarkable advances in artificial intelligence and cognitive neuroscience, no generally accepted first-principle explains why intelligent systems organize latent semantic states as they do. Existing frameworks such as information theory, the Information Bottleneck, the Degree of Information Abstraction, predictive coding and the Free Energy Principle provide powerful frameworks for understanding communication, learning, and prediction, but do not explicitly explain the emergence and organization of semantic intelligence. Here we propose the \textbf{Semantic Least-Energy Principle (SLEP)} as a hypothesis that intelligent systems evolve internal representations by maximizing semantic utility while progressively minimizing semantic, predictive, and computational energy. We formulate this hypothesis within a variational framework in which semantic cognition is governed by a Semantic Action Functional whose stationary solutions define efficient trajectories on a latent semantic manifold. This formulation emerges a series of theoretical predictions, including semantic geometry, semantic thermodynamics, and low-energy latent semantic states as complementary consequences of the same underlying optimization process. SLEP unifies semantic abstraction, reasoning, planning, and communication within a common mathematical framework while generating experimentally testable predictions for both artificial and biological intelligence. Although the hypothesis remains to be rigorously validated, it provides a principled foundation for investigating semantic intelligence from a first-principle perspective.
