What question did this study set out to answer?

To develop a unified theoretical framework based on the QBN deficit variational principle for various artificial intelligence paradigms.

July 11, 2026Open Access

Deficit Variational Theory for Six Core Artificial Intelligence Paradigms: Transformers, Diffusion Models, LLMs, GNNs, Reinforcement Learning and Quantum Machine Learning (QBN Programme)

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To develop a unified theoretical framework based on the QBN deficit variational principle for various artificial intelligence paradigms.
Introduced a deficit variational foundation for six AI models: Transformers, Diffusion Models, LLMs, GNNs, RL, and QML.
Established mathematical proofs and rigorous interactions among AI models, focusing on deficit phase transitions and variational optimization techniques.
Formulated predictive models and scaling laws that specify critical parameters for cognitive abilities in AI.
Demonstrated unification of Transformers and Diffusion Models under a shared variational framework with clear mathematical relationships.
Identified key relationships between GNN structures and molecular dynamics, leading to testable predictions for molecular properties.
Predicted emergent cognitive capabilities across AI paradigms, relating them to deficit-driven phase transitions.

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This archive presents unified theoretical frameworks for core AI paradigms, suggesting a universal mathematical law for models.

Wengang Yu (2026) studied this question.

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