Redundant or Necessary? A Benchmark for Detecting Redundant Steps in Agent Trajectories
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866918529479999488 |
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| author | Hu, Minyang Yang, Bo Zhou, Zhinuo Liang, Jiachen Jiahao, Guo Yin, Yiyang Han, Xiongwei |
| author_facet | Hu, Minyang Yang, Bo Zhou, Zhinuo Liang, Jiachen Jiahao, Guo Yin, Yiyang Han, Xiongwei |
| contents | LLM-based agents have demonstrated 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 support this initiative, we introduce \textbf{RedundancyBench}, a new benchmark that contains diverse tasks with carefully annotated trajectories, where each step is labeled according to its contribution to task completion. Using RedundancyBench, we develop and evaluate 3 representative methods to answer whether a step within trajectory is redundant or necessary. Our results show that even the best-performing method achieves only 24.88\% score in detecting redundant steps, while some methods perform worse than random guessing. These results highlight the task's complexity and the need for further research in this area. \footnote{Code and dataset in this paper are both available in \href{https://anonymous.4open.science/r/RedundancyBench}{https://anonymous.4open.science/r/RedundancyBench}.} |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_29893 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Redundant or Necessary? A Benchmark for Detecting Redundant Steps in Agent Trajectories Hu, Minyang Yang, Bo Zhou, Zhinuo Liang, Jiachen Jiahao, Guo Yin, Yiyang Han, Xiongwei Artificial Intelligence LLM-based agents have demonstrated 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 support this initiative, we introduce \textbf{RedundancyBench}, a new benchmark that contains diverse tasks with carefully annotated trajectories, where each step is labeled according to its contribution to task completion. Using RedundancyBench, we develop and evaluate 3 representative methods to answer whether a step within trajectory is redundant or necessary. Our results show that even the best-performing method achieves only 24.88\% score in detecting redundant steps, while some methods perform worse than random guessing. These results highlight the task's complexity and the need for further research in this area. \footnote{Code and dataset in this paper are both available in \href{https://anonymous.4open.science/r/RedundancyBench}{https://anonymous.4open.science/r/RedundancyBench}.} |
| title | Redundant or Necessary? A Benchmark for Detecting Redundant Steps in Agent Trajectories |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.29893 |