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

summary

Video file (mp4)

The gist

The gist The work proposes a new research area focused on redundant step detection for agent trajectories, introducing RedundancyBench to systematically evaluate how effectively LLMs can identify

In short

The research introduces redundant step detection in LLM agent trajectories to improve computational efficiency. A new benchmark, RedundancyBench, was created to systematically evaluate how well different LLMs can identify low-value steps. Results show that while coarse redundancy is possible, detecting fine-grained, step-level redundancy remains very challenging for state-of-the-art models without prior context.

Key concepts

Redundant Step Detection
This is the research problem of automatically identifying individual actions or steps within a long agent trajectory that consume significant computation but do not meaningfully contribute to achieving the final task goal. The goal is to filter out these low-value actions to make agents more efficient.
RedundancyBench
A new benchmark containing 1,920 trajectories annotated with step-level labels (redundant or necessary). It was created through a multi-round human annotation process to provide high-quality ground truth for training and testing LLM detection strategies on agent behaviors.
Step-Level Score
A metric used to evaluate how accurately an LLM can identify individual steps as redundant. It calculates the average F1-score across all trajectories, providing a balanced measure of both precision (avoiding false positives) and recall (finding all true redundancies).
Context Information
The amount of information provided to the LLM during detection. The study found that context plays a vital role; however, excessively long contexts can sometimes hurt performance due to attention dilution. A moderate context size seems optimal for detecting redundancy.

Terminology used across episodes

This episode discusses

The paper

Redundant or Necessary? A Benchmark for Detecting Redundant Steps in Agent Trajectories · Read on arXiv

Minyang Hu, Bo Yang, Zhinuo Zhou, Jiachen Liang, Jiahao Guo, Yiyang Yin, Xiongwei Han

Huawei Technologies Institute of Computing Technology Chinese Academy of Sciences

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: 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 RedundancyBench, a new benchmark that contains 1,920 trajectories collected from 6 different LLMs with 3,613 step-level annotations. Using RedundancyBench, we develop and evaluate 6 frontier LLMs and Jev as redundant step detectors, summarizing their strengths and limitations. Our experiments reveal that the redundant step detection is still challenging even for SOTA models, such as Claude-Opus-5, particularly without prior information. Code and dataset in this paper are both available in https://anonymous.4open.science/r/RedundancyBench-E0CB.

Transcript

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "Redundant or Necessary? A Benchmark for Detecting Redundant Steps in Agent Trajectories".

Jane: The gist The work proposes a new research area focused on redundant step detection for agent trajectories,

Tom: First, who's behind it and why it matters.

Title and authors: Tom: The paper is titled Redundant or Necessary? A Benchmark for Detecting Redundant Steps in Agent Trajectories and it’s authored by Minyang Hu, Bo Yang, Zhinuo Zhou, Jiachen Liang, Jiahao Guo, Yiyang Yin and Xiongwei Han >

Jane: These authors are coming from Huawei Technologies at the Institute of Computing Technology at Chinese Academy of Sciences >

Lu: The title itself sets up the problem by asking whether a step in an agent's trajectory is redundant or necessary for achieving the final goal >

Meng: I don't know much about how many different agents these authors are testing, but what stands out is that they’re creating this benchmark to test AI systems on something beyond just simple task completion.

Lalam: They also mention they developed three representative methods to answer whether a step within a trajectory is redundant on RedundancyBench >

The paper's summary: Tom: Basically, the paper sets up RedundancyBench, which has over two hundred trajectories with more than eight thousand steps each that are carefully annotated so we know if each step is redundant or necessary >

Jane: To get those annotations right they used a multi-round procedure involving heuristic detection methods and then human experts for error verification and refinement before getting consensus on the four types of redundancy: Abnormal Step, Duplicated Step, Incorrect Step, and Exploratory Step >

Lu: The problem is formally defined by that indicator t(tau) = one if removing the t-th agent step does not change task success from one to zero > <ref:2605.29893#pg2>

Meng: I like that they spent time on annotation quality because if you’re going to build a benchmark, the ground truth has to be solid, otherwise, it’s just noise >

Lalam: They also developed two evaluation metrics: a trajectory-level score which looks at coarse-grained trajectory evaluation and a step-level score that evaluates the fine-grained step detection ability >

The paper's improvements: Tom: One major thing they suggest is that we can improve the naive question of pruning low-value steps by using this benchmark to automatically prune those steps if in the compressed trajectory we still obtain task success, meaning the removed step didn't contribute to task completion >

Jane: They are also looking at three detection strategies for Large Language Models with different receptive fields: One-to-One, Window-to-One, and All-to-All >

Lu: The results show that the One-to-One strategy consistently underperforms compared to the other two strategies across all three LLMs >

Meng: If I'm looking at deployment on a real system, I wonder if we can actually use Window-to-One to leverage contextual information from six neighboring steps during prediction for better results >

Lalam: And they also suggest developing a system that can perform All-to-All classification, enabling the LLM to jointly predict labels for all steps in one pass so it can capture those long-term dependencies across the entire trajectory >

Conclusion: Tom: So to wrap up, the paper shows that while LLMs can handle coarse-grained redundancy detection at the task level, they struggle with fine-grained step-level redundancy because they can't model causal relationships between steps >

Jane: They also pointed out that adding ground truth actions significantly improves the performance of LLM on both trajectory-level and step-level detection because those ground truth actions usually contribute to task completion >

Lu: The paper concludes that the main contribution is introducing RedundancyBench to bridge the gap between existing evaluation practices and the goal of improving agent efficiency >

Meng: My concern is that they admit a limitation, saying all trajectories in RedundancyBench are collected only from tau two-Bench using Qwen-three point six-Plus, which limits the diversity of redundant behaviors covered in the benchmark >

Lalam: They also note that the limited scale of RedundancyBench, containing only two hundred trajectories due to high annotation costs, restricts large-scale exploration >

Tom: It seems like accurately detecting redundant steps remains highly challenging even for strong LLM-based methods, and they suggest that a moderate context size achieves a better balance between necessary information and unrelated content in redundancy detection >

Jane: The main point is that this RedundancyBench will serve as a catalyst for future research on efficient agent design, trajectory optimization, and more fine-grained evaluation methodologies for LLM agents >

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