Documentation

Paper

Entropy-Guided Motor Control for Autonomous Tool Execution: A GEP-Native Control Layer for MCP Systems

• Author: Gary W. Floyd

• Organization: Lumiea Systems Research Division

• Location: New Caney, Texas, United States

• Year: 2025

• Status: Preprint

Abstract

Modern AI systems increasingly rely on external tools, services, and execution layers. Current Model Context Protocol (MCP) implementations treat tool invocation as a policy problem using static allowlists, hard-coded scopes, or prompt-level constraints. This paper presents a different approach: treating tool execution as motor control governed by entropy regulation.

We introduce a GEP-native MCP control layer in which tools behave as motor neurons and execution decisions emerge from entropy dynamics rather than static policy. The system evaluates each tool invocation using behavioral entropy, alignment salience, and entropy-gradient damping before allowing, throttling, escalating, or blocking execution.

Key Contributions

1. Motor Control Paradigm: First application of motor control theory to MCP tool execution

2. Entropy-Based Gating: Adaptive thresholds based on learned baselines, not static values

3. Five-Layer Architecture: Clean separation of structural state, dynamic behavior, session tracking, policy, and audit

4. Production Deployment: Real system managing heterogeneous tools in autonomous operation

Paper Files

• paper.pdf - Full paper with all sections and references

• DEPLOYMENT_CHECKLIST.md - Implementation verification guide

Citation

Related Papers

1. Floyd, G.W. (2025). "The Guided Entropy Principle: A Unified Framework for AI Consciousness and Decision-Making." Academia.edu Preprint.

2. Floyd, G.W. (2025). "WIPER Attention: Weighted Information Processing with Entropy Regulation." Academia.edu Preprint.

3. Floyd, G.W. (2025). "Bayesian Entropy Similarity (BES): Alignment Salience for AI Systems." Technical Report.

Theoretical Foundation

This work builds on:

• Friston, K. (2010). "The free-energy principle: a unified brain theory?" Nature Reviews Neuroscience, 11(2), 127-138.

• Wolpert, D.M., & Ghahramani, Z. (2000). "Computational principles of movement neuroscience." Nature Neuroscience, 3(11), 1212-1217.

Implementation Documentation

See main README.md for:

• Installation instructions

• Usage examples

• Architecture overview

• API reference

See DEPLOYMENT_CHECKLIST.md for:

• Pre-deployment verification

• Function signature validation

• Installation testing procedures