mlsanityData to 14 Oct 2026

NRR-Phi: A Typed External Text-to-State Interface and Update Contract for Inspectable Ambiguity-State Maintenance

arXiv 2601.19933 ↗First posted 12 Jan 2026cs.CL cs.AI cs.LGcited by 0

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Abstract (arXiv)

Ambiguity-bearing inputs reach downstream systems through interfaces that favor a single resolved response before later context arrives. Even when alternatives are externalized, their representation and relative activation depend on the update rule. We address this state-maintenance problem within Non-Resolution Reasoning (NRR) by specifying a typed external text-to-state interface and explicit state-update contract. A mapping (phi: T -> S) constructs typed (v,c,w,m) records; declared operators make record carry-forward inspectable, while a record-weight entropy criterion separately tests normalized-weight concentration. The contract covers dampening, calibration, deferred resolution, contradiction-preserving integration, and temporal persistence. Across 580 constructed states/pairs, an executable suite performs 2,740 operator-state measurements. Tested non-violating transitions and calibration/identity checks show 0% record-weight entropy violations; a uniform-subtraction comparison violates the criterion at 1.7%, 6.1%, and 17.8% as subtraction increases. A separate 68-input construction audit finds multiple positive-weight typed records (mean record-weight entropy H = 1.087 bits) under the reported rule-based and archived LLM-assisted procedures, including a Japanese marker-set instantiation. The entropy criterion does not by itself certify record identity or cardinality, semantic adequacy, or end-to-end behavioral improvement. The repository provides deterministic reruns, archived prompt/output artifacts, transcript audit, and a fixed 18-set LLM-case sanity rerun. Phi therefore turns retained-state maintenance into a typed, executable, and falsifiable interface: record carry-forward is inspectable in declared operators, and normalized-weight concentration is separately testable before downstream commitment.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 34 references that are in the map. First posted on arXiv 12 Jan 2026.

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21 of 34 references.

References · 21

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  3. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  4. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
  5. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,073
  6. 2022Language Models (Mostly) Know What They Knowcited by 1,856
  7. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 913
  8. 2025LLMs Get Lost In Multi-Turn Conversationcited by 429
  9. 2020AmbigQA: Answering Ambiguous Open-domain Questionscited by 390
  10. 2016Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Modelscited by 301
  11. 2017End-to-end Neural Coreference Resolutioncited by 241
  12. 2023We're Afraid Language Models Aren't Modeling Ambiguitycited by 126
9 more
  1. 2023Clarify When Necessary: Resolving Ambiguity Through Interaction with LMscited by 98
  2. 2022CLAM: Selective Clarification for Ambiguous Questions with Generative Language Modelscited by 69
  3. 2024Aligning Language Models to Explicitly Handle Ambiguitycited by 56
  4. 2024Asking the Right Question at the Right Time: Human and Model Uncertainty Guidance to Ask Clarification Questionscited by 32
  5. 2024Belief Revision: The Adaptability of Large Language Models Reasoningcited by 30
  6. 2025Disambiguate First, Parse Later: Generating Interpretations for Ambiguity Resolution in Semantic Parsingcited by 13
  7. 2024Do Pre-Trained Language Models Detect and Understand Semantic Underspecification? Ask the DUST!cited by 7
  8. 2025It Depends: Resolving Referential Ambiguity in Minimal Contexts with Commonsense Knowledgecited by 3
  9. 2025NRR-Core: Non-Resolution Reasoning as a Computational Framework for Contextual Identity and Ambiguity Preservationcited by 1

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