NRR-Phi: Text-to-State Mapping for Ambiguity Preservation in LLM Inference

Researchers developed NRR-Phi, a framework that prevents large language models from prematurely committing to single interpretations of ambiguous text. The system maintains multiple valid interpretations in a non-collapsing state space, achieving 1.087 bits of mean entropy compared to zero for traditional collapse-based models.

• →Large language models suffer from premature semantic commitment, collapsing ambiguous inputs into single responses too early.
• →NRR-Phi framework uses text-to-state mapping to preserve multiple interpretations simultaneously in AI inference.
• →The hybrid extraction pipeline combines rule-based segmentation with LLM-based enumeration for detecting ambiguity.
• →Testing on 68 ambiguous sentences showed the system maintains interpretive multiplicity while traditional models collapse to single meanings.
• →The framework demonstrates cross-lingual portability with Japanese language implementation and extends Non-Resolution Reasoning capabilities.