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

TL;DR

This paper introduces a formal framework for mapping ambiguous text into a multi-interpretation state space, preventing premature semantic collapse in large language models during inference.

Contribution

It presents a novel text-to-state mapping approach that preserves multiple interpretations, extending Non-Resolution Reasoning to improve LLM inference with ambiguity.

Findings

Hybrid extraction yields higher state entropy (H=1.087 bits) than collapse-based baselines.

The framework maintains interpretive multiplicity across ambiguity categories.

Empirical validation shows 0% collapse with principle-satisfying operators.

Abstract

Large language models exhibit a systematic tendency toward early semantic commitment: given ambiguous input, they collapse multiple valid interpretations into a single response before sufficient context is available. This premature collapse discards information that may prove essential as dialogue evolves. We present a formal framework for text-to-state mapping (phi: T -> S) that transforms natural language into a non-collapsing state space where multiple interpretations coexist. The mapping decomposes into three stages: conflict detection, interpretation extraction, and state construction. We instantiate phi with a hybrid extraction pipeline that combines rule-based segmentation for explicit conflict markers with LLM-based enumeration of implicit ambiguity. On a test set of 68 ambiguous sentences, the resulting states preserve interpretive multiplicity: hybrid extraction yields mean state entropy H = 1.087 bits across ambiguity categories, compared to H = 0 for collapse-based baselines that commit to a single interpretation. We also instantiate the rule-based conflict detector for Japanese markers to illustrate cross-lingual portability. This framework extends Non-Resolution Reasoning (NRR) by providing the algorithmic bridge between text and the NRR state space, enabling architectural collapse deferment in LLM inference. Design principles for state-to-state transformations are detailed in the Appendix, with empirical validation on 580 test cases demonstrating 0% collapse for principle-satisfying operators versus up to 17.8% for violating operators.