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Transimaginary Bayesian Collapse: A Field-Theoretic Framework for Quantum-Semantic Token Generation…
- The paper presents a speculative theoretical framework combining probability theory, complex and transimaginary number systems, and artificial intelligence for token generation in large language models.
- It introduces the Transimaginary Bayesian Collapse (TBC) framework, treating token generation as a quantum-like wavefunction collapse in transimaginary number space.
- The framework incorporates real, complex, and dual components to encode probabilistic amplitude, semantic phase relationships, and contextual drift for semantic processing in artificial systems.
- Token selection is reconceptualized as a quantum-semantic wavefunction collapse, enabling dynamic probability generation and drift-aware selection in artificial language models.
- The mathematical foundation extends from real numbers to transimaginary numbers, enhancing the representation of semantic cognition phenomena like ambiguity and metaphorical reasoning.
- The architecture integrates concepts like Transimaginary State Function, Hamiltonian-like Operators, measurement-induced wavefunction collapse, and extended embedding spaces for improved token processing.
- The framework's theoretical implications include dynamic probability generation, coherence maintenance, and phase-dependent semantics for more nuanced modeling of semantic relationships.
- Practical applications involve advanced prompt engineering, controllable text generation, and interpretability enhancement in language models.
- While the TBC framework offers a principled theoretical foundation for sophisticated language understanding, future work should focus on empirical validation, computational complexity analysis, and algorithmic efficiency.
- The TBC framework represents a critical step towards developing AI systems that dynamically compute and collapse meaning from rich semantic fields for human-like language understanding.
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