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长远终端基准 2.0：自动化多样化任务构建与持续代理进展测量Long-Horizon-Terminal-Bench 2.0: Automating Diverse Task Construction and Measuring Sustained Agent Progress

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以下内容为 AI 自动翻译，英文原文附后供参考。

摘要

长远终端基准 2.0 (LHTB 2.0) 被引入作为评估 AI 代理在扩展任务上表现的先进框架。此更新的基准通过将任务范围扩展到 77 个，涵盖多样化领域，解决了之前的局限性。LHTB 2.0 的一个关键特性是其关注持续进展和中间成就的测量，而不仅仅集中于最终结果，这为评估代理的能力提供了更全面的视角。

The Long-Horizon-Terminal-Bench 2.0 (LHTB 2.0) has been introduced as an advanced framework for evaluating the performance of AI agents on extended tasks. This updated benchmark addresses previous limitations by expanding the range of tasks to 77, covering diverse domains. A key feature of LHTB 2.0 is its focus on measuring sustained progress and intermediate achievements, rather than solely concentrating on final outcomes, which provides a more comprehensive assessment of an agent's capabilities over time.

要点

  1. LHTB 2.0 将任务范围扩展到 77 个，增强了评估的多样性。

    LHTB 2.0 expands the task range to 77, enhancing evaluation diversity.

  2. 该基准强调持续进展，提供了对 AI 性能的更细致视角。

    The benchmark emphasizes sustained progress, offering a more nuanced view of AI performance.

  3. 旨在纠正评估长远任务中的先前问题。

    It aims to correct previous issues in evaluating long-horizon tasks.

为何重要

该基准具有重要意义，因为它为评估 AI 代理设定了新标准，鼓励开发能够持续表现的系统，而不仅仅是实现最终目标。

This benchmark is significant as it sets a new standard for evaluating AI agents, encouraging the development of systems that can sustain performance over time rather than just achieving end goals.

按谁的角度看

产品经理

对于产品经理来说，了解 LHTB 2.0 可以为开发优先考虑持续用户参与和性能的 AI 产品提供信息，符合市场对可靠和有效 AI 解决方案的需求。

For product managers, understanding LHTB 2.0 can inform the development of AI products that prioritize sustained user engagement and performance, aligning with market needs for reliable and effective AI solutions.

开发者

开发人员和工程师可以利用 LHTB 2.0 设计在长任务中保持进展的 AI 系统，促进任务导向的 AI 应用创新。

Developers and engineers can leverage LHTB 2.0 to design AI systems that are evaluated on their ability to maintain progress over long tasks, fostering innovation in task-oriented AI applications.

学生与求职者

AI 领域的学生和求职者可以通过熟悉 LHTB 2.0 受益，因为它代表了一种前沿的性能评估方法，可能会影响未来的研究和就业机会。

Students and job-seekers in AI can benefit from familiarizing themselves with LHTB 2.0, as it represents a cutting-edge approach to performance evaluation that may influence future research and job opportunities in the field.

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