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English(EN) ByteDance's new HarnessDev benchmark tests whether LLMs can build their own agent harnesses - the code that surrounds a model to enable tool use and multi-step

字节跳动HarnessDev基准测试,评估LLM构建代理代码的能力

来自字节跳动Seed及其他机构的研究人员推出了HarnessDev,这是一个旨在评估LLM创建自身代理工具链能力的新基准。与固定工具链的传统基准不同,HarnessDev评估的是LLM自身生成的工具链代码的质量。初步结果表明,虽然LLM可以改进它们的工具链,但只有一部分改进能够泛化到不同的任务中,这表明完全自主改进的代理仍然是一个重大挑战。 AI

影响 该基准测试通过关注AI代理自我改进其自身操作代码的能力,有望推动开发更强大、更自主的AI代理。

排序理由 该集群描述了一个由学术研究人员提出的,用于评估LLM生成代理工具链的新基准和评估方法。

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字节跳动HarnessDev基准测试,评估LLM构建代理代码的能力

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该集群描述了一个由学术研究人员提出的,用于评估LLM生成代理工具链的新基准和评估方法。
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报道来源 [2]

  1. MarkTechPost TIER_1 English(EN) · Asif Razzaq ·

    大型语言模型能否自行构建其代理工具集?字节跳动Seed的HarnessDev表示64项变更中仅34项具有通用性

    <p>ByteDance Seed, SUTD, Georgia Tech, M-A-P, and TokenWave.AI introduce HarnessDev, a benchmark that scores the runnable harness a model builds rather than the answer it returns. Starting from a seed that scores 0, 6 creator LLMs construct harnesses across 5 benchmarks and 2,207…

  2. Mastodon — mastodon.social TIER_1 English(EN) · [email protected] ·

    字节跳动新HarnessDev基准测试LLM能否构建自己的代理Harness——围绕模型以实现工具使用和多步操作的代码

    ByteDance's new HarnessDev benchmark tests whether LLMs can build their own agent harnesses - the code that surrounds a model to enable tool use and multi-step reasoning. Only 34 of 64 evolution changes generalised across tasks, showing self-improving agents remain challenging to…