unworlding-involution

Frame-invariant involution uses self-inverse mappings (ι∘ι = id) to derive stable agent representations across different observer perspectives. Applied to triadic interactions, it models how agents converge to Nash equilibria while preserving behavioral invariances regardless of the observation frame.

GF(3) color conservation enforces galois-field arithmetic constraints on agent state transitions during best-response interactions. This mechanism ensures that triadic agent dynamics maintain invariant properties while driving convergence toward equilibrium configurations.

Yes. Frame-invariant involution identifies fixed points that remain stable under perspective changes in triadic observer-generator setups. The self-inverse property guarantees that applying the transformation twice returns the original state, exposing invariant structures.

Run `just unworlding-demo` to execute concrete demonstrations of involution, best-response dynamics, and fixed-point extraction. The demo exemplifies how frame invariance and GF(3) color conservation operate in triadic agent interaction patterns.

Involution's self-inverse structure and frame invariance ensure that best-response trajectories in triadic systems stabilize at equilibrium points that are invariant across observer frames, avoiding perspective-dependent artifacts in convergence analysis.

This Skill has no external dependencies and integrates with the 3-MATCH gadget for pattern extraction and testing. It operates independently to derive frame-invariant self-representations from interaction dynamics without requiring external multi-agent libraries.