LLM Parkinsonism: Executive-Control Failure, Token-Inefficient Persistence, and a Global Executive Control Architecture for Autonomous Language-Model Agents
Large language models (LLMs) can plan, write code, call tools, manipulate software, and execute long-horizon workflows. Yet strong local competence does not guarantee project-level executive control. An agent can make large, useful early advances and then shift toward progressively smaller, lower-value actions—addition...