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Identifying Quantum Structure in AI Language: Evidence for Evolutionary Convergence of Human and Artificial Cognition
Diederik Aerts1, Jonito Aerts Arguëlles1, Lester Beltran1
1Center Leo Apostel for Interdisciplinary Studies, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium.
Entropy (Basel, Switzerland)
|June 26, 2026
Summary
Large Language Models (LLMs) exhibit non-classical probability and Bose-Einstein statistics in cognitive tests, mirroring human cognition. This suggests quantum-like structures emerge in both human and artificial intelligence conceptual domains.
Area of Science:
- Cognitive Science
- Artificial Intelligence
- Quantum Physics
Background:
- Large Language Models (LLMs) are increasingly sophisticated AI systems.
- Understanding the cognitive processes and underlying organizational principles of LLMs is crucial.
- Previous research indicated quantum-like structures in human cognition and language.
Purpose of the Study:
- To investigate conceptual combination abilities in LLMs using cognitive tests.
- To determine if LLMs exhibit non-classical probability models and specific statistical distributions.
- To explore the convergence of cognitive and linguistic structures between humans and AI.
Main Methods:
- Cognitive tests were administered to Large Language Models (LLMs), specifically ChatGPT and Google Gemini Advanced.
- Bell's inequalities were tested to assess probability models.
- Word distribution in texts was analyzed to identify statistical patterns (Bose-Einstein vs. Maxwell-Boltzmann statistics).
Main Results:
- Significant violation of Bell's inequalities was observed, indicating non-classical probability models in LLMs.
- LLMs demonstrated Bose-Einstein statistics in word distribution, contrary to expected Maxwell-Boltzmann statistics.
- Findings in LLMs mirrored previous results from human cognitive and information retrieval tests.
Conclusions:
- Non-classical quantum-like structures systematically emerge in conceptual-linguistic domains for both humans and artificial intelligence.
- The distributive semantic structure of vector spaces in LLMs, rather than their neural network classification, is key to knowledge organization.
- A unifying framework is proposed to explain the pervasive quantum organization of meaning across biological and artificial cognitive agents.
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