PulseAugur
实时 19:01:12
English(EN) As AI agents move from demos to production, the key question is: how do you know if an agent is any good? Perplexity scores tell you little about real-world cap

SWE-bench测试AI代理的现实世界能力,显示80%的解决率

随着AI代理从实验阶段过渡到生产环境,评估其现实世界性能变得至关重要。困惑度分数等传统指标不足以评估代理的有效性。SWE-bench等基准测试(测试实际GitHub问题的解决情况)显示出显著进展,顶级模型现在的成功率达到80%,而去年仅为2%。 AI

影响 新的基准测试正在涌现,以更好地评估AI代理在现实世界任务中的性能,超越了简单的困惑度分数。

排序理由 该集群讨论了AI代理的基准测试和评估指标,属于研究范畴。

在 Mastodon — sigmoid.social 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

SWE-bench测试AI代理的现实世界能力,显示80%的解决率

报道来源 [1]

  1. Mastodon — sigmoid.social TIER_1 English(EN) · [email protected] ·

    As AI agents move from demos to production, the key question is: how do you know if an agent is any good? Perplexity scores tell you little about real-world cap

    As AI agents move from demos to production, the key question is: how do you know if an agent is any good? Perplexity scores tell you little about real-world capability. SWE-bench tests real GitHub issue resolution - top models now hit 80% vs just 2% in 2023. https://www. marktech…