The Minimum-Action Semantic Framework (MAF): A Variational Principle for Conversational Stability in Large Language Models

This record presents the Minimum-Action Semantic Framework (MAF), together with an ML-native translation framing conversational behavior in large language models as a variational cost-minimization process MAF models dialogue as a trajectory through a latent semantic space and defines a composite action functional penalizing semantic displacement, predictive entropy, and proximity to constraint boundaries. The framework proposes that structured interpretive frameworks induce low-cost trajectories, yielding reduced semantic drift, lower entropy, and greater alignment stability compared to unstructured interactions. MAF is a phenomenological behavioral model rather than a token-level optimization method, and does not claim models explicitly compute gradients or action integrals during inference. The contribution is a testable theoretical account of how structure shapes conversational stability, expressed in standard machine learning terminology and suitable for empirical validation.