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Semantic information mathematics
- Semantic-Information Mathematics (SIM) proposes theoretical constants inspired by physical laws to govern AI symbolic behavior for cognitive control.
- The three theoretical constants are: Semantic Generative Uncertainty Constant (ℏ_SIM), Semantic Coupling Constant (α_SIM), and Narrative Collapse Constant (G_SIM).
- ℏ_SIM governs the minimum floor of symbolic uncertainty, adjusting based on user needs and content complexity for creative or logical output.
- α_SIM controls the cohesion between concepts to resist semantic drift, with dynamic coupling and adaptive adjustments for different writing styles.
- G_SIM controls symbolic gravity, pulling language back to the core narrative with hierarchical gravity and competing attractors for thematic balance.
- By adjusting these constants, different cognitive modes like Creative Expansion, Analytical Refinement, and Narrative Synthesis can be constructed.
- The framework supports dynamic mode transitions within a single response, adapting constants for different stages like introduction, development, and conclusion.
- Advanced control mechanisms include context-dependent constant modulation, feedback loops based on user signals, and multi-agent configurations for different AI behaviors.
- Experimental validation frameworks suggest self-monitoring protocols, user feedback integration, and empirical testing for improved AI system performance.
- Semantic-Information Mathematics aims to develop more responsive, controllable AI systems that can adapt their cognitive processes based on contextual understanding and user feedback.
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