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Language model states are not static points but possess dynamic "native reference frames" that can be approximated to unlock near-perfect interpretability and steerability.
REAG transforms acceptance testing for LLM-based software by achieving a 3.91 to 4.30 improvement in oracle quality while ensuring 98.8% accuracy in verdict reliability.
Semantic Reference Frames reveal that optimizing the trajectory of language model computation can significantly enhance parameter efficiency and reduce complexity.
LLMs can lose up to 69% of functional correctness during multi-turn code refinements, revealing critical gaps in their reliability for software engineering tasks.