LLM Parkinsonism: Executive-Control Failure, Token-Inefficient Persistence, and an Uncertainty-Aware Global Executive Control Architecture for Autonomous Language-Model Agents
A paper introduces a governance architecture, Global Executive Control (GEC), to address the issue of persistent action in large language models (LLMs) despite diminishing task value. The architecture separates action generation from project-level control, improving success rates and reducing token use.
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