graph-regularization

GRACE uses a typed semantic graph to represent persistent instructions for LLM agents, enabling scoped verification of updates to improve reliability under distribution shift. Experiments on a telecom agent harness show significant improvements in strict reliability over baselines.

The paper introduces a graph-regularized deep learning framework for EEG-based emotion recognition that incorporates psychologically-grounded emotion topology into the training objective, achieving up to +5.42% accuracy and 39% reduction in psychologically implausible misclassifications on SEED datasets.