Researchers presented a blackbox prompt-minimization framework for large language models (LLMs) that reduces few-shot prompts to their necessary minimal subset. The framework, called ramework, preserves propositional output fidelity and shows that models preferentially retain logical identifiers and constraint declarations while discarding natural language prose and cross-prompt relational annotations.