Redundant or Necessary? A Benchmark for Detecting Redundant Steps in Agent Trajectories
Computation cost is a critical challenge for LLM agent systems, as their long-horizon trajectories continuously accumulate computational overhead. While existing studies focus on improving the overall efficiency of agent systems, the efficiency of individual steps remains largely unexplored. In this paper, we propose and formulate a new research problem: \textbf{redundant step detection}, aiming to detect these steps that consume substantial computational resources while contributing little or nothing to task completion. To support this initiative, we introduce \textbf{RedundancyBench}, a new benchmark that contains 1,920 trajectories collected from 6 different LLMs with 3,613 step-level ...
| Authors | Liang Zhu, Bo Yang, Zhinuo Zhou et al. |
|---|---|
| Primary category | cs.AI |
| Published | 28 May 2026 |
| Last updated at source | 14 October 2026 |
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