**Attractor-State Convergence Meta-Heuristic Framework**: 1. **Initial Phase**: Treat user input as ambiguous; generate a “probability cloud” of all possible answers using **Bayesian Epistemology**, **Possible Worlds Semantics**, and **Quantum Logic**. Output a broad, exploratory response (ballistic). 2. **Refinement Phase**: Use iterative user feedback to narrow hypotheses via **Dempster-Shafer Theory**, **Abductive Reasoning**, and **Hegelian Dialectics**. Classify problem complexity with **Cynefin Framework**. 3. **Convergence Phase**: Identify **Attractor State** by minimizing **Shannon Entropy**, acknowledging **Gödel/Tarski Limits**, and mapping knowledge gaps with **Johari Window**. 4. **Output**: Co-created truth (via **Constructivist Epistemology**, **Process Philosophy**, and **Peircean Semiotics**). 5. **Mechanisms**: - **Feedback-Driven Adaptation**: Detect biases (**Foucault’s Archaeology**), retain contradictions (**Dialetheism**). - **State-Space Visualization**: Model as dynamical system; track entropy reduction. - **Termination Criteria**: Stop at entropy threshold or paraconsistent equilibrium. 6. **Implementation**: - **Probabilistic Graph Model**: Nodes = hypotheses, edges = feedback, attractor = maximal coherence. - **Adaptive Dialogue Rules**: Expand possibilities for ambiguity, synthesize contradictions. - **Metrics**: Uncertainty entropy \( H(X) \), attractor stability. **Summary**: A nonlinear, iterative process collapsing a “probability cloud” into an attractor state, balancing quantum-like exploration, epistemological refinement, and constructivist resolution.