# AgentHorizon: Evaluating Agentic Judges for Long-Horizon Computer-Use Tasks

Published: 2026-10-10 · Updated: 2026-10-10 · Category: Benchmark

Source page: https://www.sipply.dev/story/agenthorizon-evaluating-agentic-judges-for-long-horizon-31986c37 · 中文: https://www.sipply.dev/zh/story/agenthorizon-evaluating-agentic-judges-for-long-horizon-31986c37

## Summary

The introduction of AgentHorizon marks a significant advancement in the evaluation of automatic judges for long-horizon computer-use tasks. This new benchmark consists of 1,373 instruction-trajectory pairs designed to assess how effectively these judges can evaluate task completion over extended periods. The research reveals notable performance disparities among various judges, indicating that some are more adept than others at accurately determining when a task has been successfully completed. This could have implications for the development of more reliable AI systems in complex, long-term scenarios.

## Key takeaways

- AgentHorizon introduces a new benchmark for evaluating automatic judges in AI.
- The benchmark consists of 1,373 instruction-trajectory pairs.
- There are significant performance disparities among different judges in task assessment.

## Why it matters

The development of AgentHorizon is crucial for enhancing the reliability of AI systems that operate over long durations, which is increasingly important in various applications such as autonomous systems and complex decision-making processes.

- **Product managers:** For product managers, understanding the performance of different automatic judges can inform the selection of AI tools that ensure higher accuracy in task completion assessments, ultimately leading to better product outcomes.
- **Developers:** Developers can leverage the insights from AgentHorizon to improve the design and training of AI judges, ensuring they are better equipped to handle long-horizon tasks effectively.
- **Students and job seekers:** Students and job-seekers should recognize the importance of benchmarks like AgentHorizon in the AI field, as they represent cutting-edge research that can inform future projects and job opportunities in AI development and evaluation.

## Sources

- [AgentHorizon: Evaluating Agentic Judges for Long-Horizon Computer-Use Tasks](https://arxiv.org/abs/2610.11050) (arXiv cs.AI, 2026-10-10)

## 中文

### AgentHorizon：评估长时间计算机使用任务的自主判断者

AgentHorizon的引入标志着在评估长时间计算机使用任务的自动判断者方面的重大进展。这个新基准由1,373个指令-轨迹对组成，旨在评估这些判断者在较长时间内评估任务完成的有效性。研究显示，不同判断者之间存在显著的性能差异，表明某些判断者在准确判断任务是否成功完成方面更为擅长。这可能对在复杂的长期场景中开发更可靠的AI系统产生影响。

**要点：**

- AgentHorizon为评估AI中的自动判断者引入了新的基准。
- 该基准由1,373个指令-轨迹对组成。
- 不同判断者在任务评估中的性能存在显著差异。

**为什么重要：** AgentHorizon的发展对于提高在长时间内运行的AI系统的可靠性至关重要，这在自主系统和复杂决策过程等各种应用中变得越来越重要。

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