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

arXiv: https://arxiv.org/abs/2608.18041 · HTML: https://mlsanity.com/paper/2608.18041/

## 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.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [The Linear Representation Hypothesis and the Geometry of Large Language Models](https://mlsanity.com/paper/2311.03658/) (2023)
- [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016)
- [Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet](https://mlsanity.com/paper/2605.29358/) (2026)
- [Learning to Generate Reviews and Discovering Sentiment](https://mlsanity.com/paper/1704.01444/) (2017)
- [In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents](https://mlsanity.com/paper/2503.08026/) (2025)
- [Grounding Gaps in Language Model Generations](https://mlsanity.com/paper/2311.09144/) (2023)
- [From partners to populations: A hierarchical Bayesian account of coordination and convention](https://mlsanity.com/paper/2104.05857/) (2021)
- [Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agents](https://mlsanity.com/paper/2601.07468/) (2026)
- [CNM: An Interpretable Complex-valued Network for Matching](https://mlsanity.com/paper/1904.05298/) (2019)
- [Quantum-inspired Complex Word Embedding](https://mlsanity.com/paper/1805.11351/) (2018)
- [A quantum semantic framework for natural language processing](https://mlsanity.com/paper/2506.10077/) (2025)
- [The Open-domain Paradox for Chatbots: Common Ground as the Basis for Human-like Dialogue](https://mlsanity.com/paper/2303.11708/) (2023)
- [Tug-of-war between idioms' figurative and literal interpretations in LLMs](https://mlsanity.com/paper/2506.01723/) (2025)
- [Language as a Wave Phenomenon: Semantic Phase Locking and Interference in Neural Networks](https://mlsanity.com/paper/2512.01208/) (2025)
- [When Is Next-Token Prediction Useful? Marginalization, Ergodicity, Mixture Identifiability, Local Sufficiency, RAG, Tools, and Programming](https://mlsanity.com/paper/2605.23278/) (2026)
- [Bad company corrupts good morals: Understanding and Measuring Narrative-Induced Moral Reasoning Degradation in LLMs](https://mlsanity.com/paper/2606.28981/) (2026)
- [Complex-Valued Phase-Coherent Transformer](https://mlsanity.com/paper/2605.10123/) (2026)
- [LATTE: Forecasting Peer Anchored Preference Trajectories for Personalized LLM Generation](https://mlsanity.com/paper/2605.26612/) (2026)
- [The Need for an External Observer Formalizing the Sufficiency Gap: A Mathematical Extension of Mixture Identifiability and Contextual Grounding in Sequence Models](https://mlsanity.com/paper/2605.26711/) (2026)
- [Private Etymology: Designing Relational Reuse of Shared Symbols in Long-Term Human-AI Interaction](https://mlsanity.com/paper/2608.08443/) (2026)

## 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.