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

Researchers introduce RedundancyBench, a new benchmark for detecting redundant steps in LLM-based agent trajectories, revealing that current methods struggle significantly with this task—the best approach achieves only 24.88% accuracy. This work highlights a critical gap in agent evaluation: while task success is commonly measured, execution efficiency and resource optimization remain largely unmeasured, suggesting AI agents require substantial improvements in reasoning efficiency.

The emergence of this research addresses a fundamental blind spot in how AI agents are currently evaluated. While large language model-based agents have shown impressive capabilities in multi-step reasoning and tool use, the industry has predominantly focused on whether agents complete tasks correctly, not whether they do so efficiently. RedundancyBench introduces systematic evaluation of step necessity—a metric increasingly important as agent systems scale and operational costs become material concerns.

This benchmarking effort reflects broader industry maturation in AI evaluation. As LLM-based agents move from research prototypes toward production systems, efficiency metrics become as critical as accuracy metrics. The field has similarly evolved with task completion rates, reasoning chains, and hallucination detection—each representing deeper scrutiny of agent behavior beyond simple pass/fail outcomes.

The stark performance gap—with top methods achieving barely above-random results—signals that detecting redundancy requires sophisticated understanding of task semantics and multi-step planning dynamics. This has practical implications for developers deploying agents in cost-sensitive environments where unnecessary API calls, database queries, or computations directly impact operating expenses. For enterprises using autonomous agents, tool-use efficiency directly affects both deployment costs and user experience latency.

The research establishes a foundation for future work in agent efficiency optimization. As agents become more autonomous and interact with expensive external systems, methods for identifying and eliminating wasteful steps will become commercially valuable. This benchmark enables comparative progress measurement and could drive development of agent architectures that inherently minimize redundancy, much as recent work in prompt optimization and token efficiency has benefited from standardized benchmarks.

• →Current methods for detecting redundant steps in AI agent trajectories perform poorly, with the best approach achieving only 24.88% accuracy on RedundancyBench.
• →Execution efficiency remains a largely unmeasured dimension of agent evaluation despite significant resource implications for production deployments.
• →RedundancyBench provides a standardized benchmark with annotated trajectories to drive progress on redundancy detection methods.
• →The gap between random guessing and best-performing methods suggests detecting step necessity requires sophisticated understanding of task semantics.
• →Redundancy detection becomes increasingly important as LLM-based agents move toward production systems with material operational costs.