mlsanityData to 14 Oct 2026

Language Has Two Parameters: Narrative-Induced Semantic Plasticity and Phase-Sensitive Interpretation

arXiv 2608.18041 ↗First posted 18 Aug 2026cs.CLcited by 0

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Abstract (arXiv)

Reading fiction or encountering narrative generally does not merely add information. The encounter changes the reader. This paper proposes that encounters alter persistent relations among simultaneously active meanings, producing individual and shared histories that population-trained language models do not necessarily retain. A model may be told of an encounter and reproduce its consequences while the history remains in context; this is not the same as being changed by the encounter. This paper formalizes this missing relational state as phase, sets out testable predictions about encounter order, quotation, and suppressed meanings, and argues that future AI agents will need persistent semantic states indexed to particular individuals and relationships. The matching risk is semantic poisoning: an attack that re-signs relations among meanings already present.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 94 references that are in the map. First posted on arXiv 18 Aug 2026.

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References · 24

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12 more
  1. 2019CNM: An Interpretable Complex-valued Network for Matchingcited by 13
  2. 2018Quantum-inspired Complex Word Embeddingcited by 6
  3. 2025A quantum semantic framework for natural language processingcited by 5
  4. 2023The Open-domain Paradox for Chatbots: Common Ground as the Basis for Human-like Dialoguecited by 4
  5. 2025Tug-of-war between idioms' figurative and literal interpretations in LLMscited by 3
  6. 2025Language as a Wave Phenomenon: Semantic Phase Locking and Interference in Neural Networkscited by 2
  7. 2026When Is Next-Token Prediction Useful? Marginalization, Ergodicity, Mixture Identifiability, Local Sufficiency, RAG, Tools, and Programmingcited by 2
  8. 2026Bad company corrupts good morals: Understanding and Measuring Narrative-Induced Moral Reasoning Degradation in LLMscited by 2
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  10. 2026LATTE: Forecasting Peer Anchored Preference Trajectories for Personalized LLM Generationcited by 1
  11. 2026The Need for an External Observer Formalizing the Sufficiency Gap: A Mathematical Extension of Mixture Identifiability and Contextual Grounding in Sequence Modelscited by 1
  12. 2026Private Etymology: Designing Relational Reuse of Shared Symbols in Long-Term Human-AI Interactioncited by 1

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