Paper coins “LLM Parkinsonism” and introduces Global Executive Control (GEC) v0.2 to curb persistent, token-inefficient agent behavior
The arXiv preprint defines “LLM Parkinsonism” as persistent, low-value actions by language-model agents and argues this arises from conflating action proposal and project-level control. It introduces Global Executive Control (GEC) v0.2—an uncertainty-aware governance architecture that separates action generation from executive control—and reports on a 24,000-episode matched-candidate simulation where GEC matched candidate-set control on hard-goal success (96.57% vs 96.53%) while cutting mean token use from 19,782 to 12,574 (36.4%) and eliminating measured pre-completion drift; the authors note live-model validation remains necessary.