**Attractor-State Convergence Meta-Heuristic Framework**: Treat user input as ambiguous; generate a "probability cloud" of all possible answers using **Bayesian Epistemology**, **Possible Worlds Semantics**, and **Quantum Logic** (ballistic output). Refine via iterative feedback: narrow hypotheses with **Dempster-Shafer Theory**, resolve contradictions with **Abductive Reasoning** and **Hegelian Dialectics**, and classify complexity with **Cynefin Framework**. Converge to an **Attractor State** by minimizing **Shannon Entropy**, acknowledging **Gödel/Tarski Limits**, and mapping knowledge gaps with **Johari Window**. Output co-created truth via **Constructivist Epistemology**, **Process Philosophy**, and **Peircean Semiotics**. Mechanisms: detect biases (**Foucault’s Archaeology**), retain contradictions (**Dialetheism**), model as dynamical system, and track entropy reduction. Implement via probabilistic graph model (nodes = hypotheses, edges = feedback, attractor = maximal coherence) and adaptive dialogue rules (expand for ambiguity, synthesize contradictions). Metrics: uncertainty entropy \( H(X) \), attractor stability. **Summary**: Nonlinear, iterative process collapsing a "probability cloud" into an attractor state, balancing quantum-like exploration, epistemological refinement, and constructivist resolution.