Hierarchical Prompt-Domain Control and Learning for Resource-Constrained Agentic Language Models

In three linesHierarchical framework for compact LLMs in resource-constrained agentic systems. Model distillation + oracle-controller loop monitors protocol validity, projects histories into feasible prompt domain, triggers lightweight fine-tuning under drift. Separates schema learning from semantic adaptation. Evaluated on Multi-Fidelity Bayesian Optimization with improved reliability and cost-efficiency.

Summary generated by Claude — human-verified