Validates triadic color system predictions using active inference and error minimization loops.
This skill provides a specialized framework for training and evaluating self-validation models within triadic color systems and topological chemputing environments. By implementing a rigorous prediction-observation cycle—utilizing efference copies and real-time data comparison—it allows developers to minimize 'surprise' and maximize model accuracy. It is specifically optimized for complex systems involving ternary generators like splitmix_ternary and xoroshiro_3color, offering a structured workflow for auditing the reliability of self-modeling agents and active inference systems.
主な機能
01Surprise and accuracy metric calculation with 99% pass thresholds
02Integration with Gay MCP tools and topological chemputer simulations
03Support for multiple ternary sources including splitmix and xoroshiro
047 GitHub stars
05Detailed JSON logging of efference copies and error aggregates
06Automated prediction-observation cycles for triadic streams
ユースケース
01Auditing the error rates and reliability of self-modeling systems
02Benchmarking active inference agents against ternary color indices
03Validating predictive models in topological chemputing simulations
