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StartupBench: Benchmarking General-Purpose Agents on Market-Validated End-to-End Workflows· StartupBench: 市场验证的端到端工作流 AI 代理基准测试

Recent advances in Large Language Models(LLMs) and agents have substantially improved the ability of AI systems to execute complex tasks. Yet existing benchmarks largely rely on researcher-selected tasks, leaving uncertain whether such progress extends to the work that real-world users actually demand from AI systems. We introduce \textbf{StartupBench}, an E2E agent benchmark grounded in market-validated AI startup products. Rather than defining tasks from pre-defined assumptions about useful agent capabilities, we systematically study AI products with demonstrated adoption, together with their product workflows and users, to identify real-world tasks for which AI has established practical demand across diverse professional domains. We translate these workflows into complete deliverable-or

AI 解读论文

通过市场验证的 AI 创业产品来评估 AI 代理在实际端到端工作流中的表现

核心方法
通过分析已经被市场验证的 AI 创业产品的实际工作流及用户需求,构建了一个全新的端到端 AI 代理基准测试 StartupBench
适合谁读
适合研究者和工程师阅读,特别是那些关注 AI 代理实际应用效果的人
要解决的问题
现有的 AI 代理基准测试多半基于研究者的假设,未曾针对实际用户需求进行评估
关键实验
未提供
主要贡献
提供了一个更加贴近实际应用场景的基准测试,帮助评估 AI 代理在不同专业领域中的实用能力
意义与局限
意义在于推进 AI 代理的实际应用,影响可能包括提高不同行业对 AI 代理的接受度,局限在于仅基于现有产品的工作流,可能忽略了新兴的需求
领域:cs.AI作者:Liya Zhu、Xin Ma、Tao Liu
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