# A quantum semantic framework for natural language processing

arXiv: https://arxiv.org/abs/2506.10077 · HTML: https://mlsanity.com/paper/2506.10077/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 92 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023)
- [Bias and Fairness in Large Language Models: A Survey](https://mlsanity.com/paper/2309.00770/) (2023)
- [Can language models learn from explanations in context?](https://mlsanity.com/paper/2204.02329/) (2022)
- [Do LLMs exhibit human-like response biases? A case study in survey design](https://mlsanity.com/paper/2311.04076/) (2023)
- [Conformal Language Modeling](https://mlsanity.com/paper/2306.10193/) (2023)
- [Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases](https://mlsanity.com/paper/2006.03955/) (2020)
- [Meaning without reference in large language models](https://mlsanity.com/paper/2208.02957/) (2022)
- [LADDER: Self-Improving LLMs Through Recursive Problem Decomposition](https://mlsanity.com/paper/2503.00735/) (2025)
- [Continuous Thought Machines](https://mlsanity.com/paper/2505.05522/) (2025)
- [Transformer-Squared: Self-adaptive LLMs](https://mlsanity.com/paper/2501.06252/) (2025)
- [A probabilistic framework for analysing the compositionality of conceptual combinations](https://mlsanity.com/paper/1305.5753/) (2013)
- [Spin and Wind Directions II: A Bell State Quantum Model](https://mlsanity.com/paper/1706.01188/) (2017)
- [Introduction to the Special Issue on Quantum Cognition](https://mlsanity.com/paper/1309.5673/) (2013)
- [Spin and Wind Directions I: Identifying Entanglement in Nature and Cognition](https://mlsanity.com/paper/1508.00434/) (2015)
- [HMCF: A Human-in-the-loop Multi-Robot Collaboration Framework Based on Large Language Models](https://mlsanity.com/paper/2505.00820/) (2025)
- [Advanced Analysis of Quantum Contextuality in a Psychophysical Double-Detection Experiment](https://mlsanity.com/paper/1611.04184/) (2016)

## Built on by (most cited first, 5 of 5)

- [Herding CATs: ALARA for Agent Harness Engineering in Portable Composable Multi-Agent Teams](https://mlsanity.com/paper/2603.20380/) (2026)
- [The production of meaning in the processing of natural language](https://mlsanity.com/paper/2603.20381/) (2026)
- [TinyTim: A Family of Language Models for Divergent Generation](https://mlsanity.com/paper/2508.11607/) (2025)
- [Phase-Associative Memory: Sequence Modeling in Complex Hilbert Space](https://mlsanity.com/paper/2604.05030/) (2026)
- [Language Has Two Parameters: Narrative-Induced Semantic Plasticity and Phase-Sensitive Interpretation](https://mlsanity.com/paper/2608.18041/) (2026)

## Abstract (arXiv)

Semantic degeneracy represents a fundamental property of natural language that extends beyond simple polysemy to encompass the combinatorial explosion of potential interpretations that emerges as semantic expressions increase in complexity. In this work, we argue this property imposes fundamental limitations on Large Language Models (LLMs) and other modern NLP systems, precisely because they operate within natural language itself. Using Kolmogorov complexity, we demonstrate that as an expression's complexity grows, the amount of contextual information required to reliably resolve its ambiguity explodes combinatorially. The computational intractability of recovering a single intended meaning for complex or ambiguous text therefore suggests that the classical view that linguistic forms possess intrinsic meaning in and of themselves is conceptually inadequate. We argue instead that meaning is dynamically actualized through an observer-dependent interpretive act, a process whose non-deterministic nature is most appropriately described by a non-classical, quantum-like logic. To test this hypothesis, we conducted a semantic Bell inequality test using diverse LLM agents. Our experiments yielded average CHSH expectation values from 1.2 to 2.8, with several runs producing values (e.g., 2.3-2.4) in significant violation of the classical boundary (|S|≤2), demonstrating that linguistic interpretation under ambiguity can exhibit non-classical contextuality, consistent with results from human cognition experiments. These results inherently imply that classical frequentist-based analytical approaches for natural language are necessarily lossy. Instead, we propose that Bayesian-style repeated sampling approaches can provide more practically useful and appropriate characterizations of linguistic meaning in context.