[2601.19933] NRR-Phi: Text-to-State Mapping for Ambiguity Preservation in LLM Inference

Computer Science > Computation and Language arXiv:2601.19933 (cs) [Submitted on 12 Jan 2026 (v1), last revised 4 Mar 2026 (this version, v4)] Title:NRR-Phi: Text-to-State Mapping for Ambiguity Preservation in LLM Inference Authors:Kei Saito View a PDF of the paper titled NRR-Phi: Text-to-State Mapping for Ambiguity Preservation in LLM Inference, by Kei Saito View PDF HTML (experimental) 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 (adversative conjunctions, hedging expressions) with LLM-based enumeration of implicit ambiguity (epistemic, lexical, structural). On a test set of 68 ambiguous sentences, the resulting states preserve interpretive multiplicity: using hybrid extraction, we obtain mean state entropy H = 1.087 bits across ambiguity categories, compared to H = 0 for collapse-based baselines that ...