MSE-Bench

E3 framework reduces LLM agent token costs 91% by estimating task complexity before execution

Researchers introduce E3 (Estimate, Execute, Expand), a framework that addresses over-reading behavior in LLM agents by having them estimate task complexity and execute a minimum viable path before expanding scope. On MSE-Bench, a 121-edit deterministic benchmark, E3 matches 100% task success while cutting cost by 85%, tokens by 91%, and file inspections by 92% versus strong baselines. The authors also validate the approach on a live GPT-4o agent editing a real open-source library, graded against an actual pytest suite. The work formalizes the Agent Cognitive Redundancy Ratio (ACRR) and positions task-aware execution as a step toward engineering-grounded AI.