Redundant or Necessary? A Benchmark for Detecting Redundant Steps in Agent Trajectories

ArXi:2605.29893v1 Announce Type: new LLM-based agents have nstrated strong capabilities in solving complex tasks through multi-step reasoning and tool use. However, existing evaluation protocols primarily focus on task success, overlooking a critical aspect of agent behavior: execution efficiency. In practice, agent trajectories often contain redundant steps that consume substantial resources while contributing little to task completion. In this work, we propose and formulate a new research area: \textbf{redundant step detection} for agent trajectories. To this initiative, we