How does it achieve high fidelity in response generation?

It utilizes principles of Active Inference and Markov Blankets, minimizing free energy between predicted and observed behavior while using Gibbs distributions to ensure statistical authenticity.

What are the ethical guidelines for using this skill?

Usage requires explicit consent from the subject, clear disclosure of AI-generated content, and the maintenance of a kill-switch for the subject to invalidate the surrogate at any time.

What is a Cognitive Surrogate?

A Cognitive Surrogate is a specialized generative model designed to predict and replicate a specific subject's cognitive patterns, values, and communication styles based on their interaction history.

What kind of data is needed to train a profile?

The skill requires an interaction corpus, which can include message threads, emails, published writings, or any structured record of the subject's communication and reasoning.

Are the generated responses reproducible?

Yes, the skill integrates with gay-mcp deterministic seeding to ensure that surrogate generation is reproducible, auditable, and consistent.

Cognitive Surrogate

Name: Cognitive Surrogate
Author: plurigrid

byplurigrid

•

データサイエンスとML

Builds, trains, and validates high-fidelity psychological models to predict and generate authentic cognitive responses.

概要

The Cognitive Surrogate skill provides a sophisticated framework for creating digital representations of specific cognitive patterns and psychological profiles. Based on Active Inference and Markov Blanket theories, it allows users to extract value systems, communication styles, and reasoning patterns from interaction corpora to generate authentic responses with over 90% fidelity. This skill is ideal for developers and researchers building self-modeling agents, predicting intellectual trajectories, or maintaining precise persona consistency across AI-driven communication systems.

主な機能

Integrate with ACSets for structured, algebraic cognitive data storage
Validate surrogate fidelity against real-world data using multi-dimensional metrics
Extract structured psychological profiles from interaction data and corpus history
Project long-term intellectual and professional trajectories through Monte Carlo simulations
Train high-precision response predictors using Langevin/Gibbs integration
2 GitHub stars

ユースケース

Creating authentic digital twins for cognitive research and behavioral simulation
Maintaining consistent brand or personal voice in automated content generation
Predicting future skill acquisition paths and research interests for professional development

概要

主な機能

Integrate with ACSets for structured, algebraic cognitive data storage
Validate surrogate fidelity against real-world data using multi-dimensional metrics
Extract structured psychological profiles from interaction data and corpus history
Project long-term intellectual and professional trajectories through Monte Carlo simulations
Train high-precision response predictors using Langevin/Gibbs integration
2 GitHub stars

ユースケース

Creating authentic digital twins for cognitive research and behavioral simulation
Maintaining consistent brand or personal voice in automated content generation
Predicting future skill acquisition paths and research interests for professional development