How Much of a Harness Does a Strong Agent Need for Autonomous ML Engineering?· 自主 ML 工程中强代理需要多大框架？
Recent autonomous machine learning engineering (MLE) agents have made significant progress on public leaderboards. Often motivated by progress stagnation over long-horizon cycles and limited Large Language Model (LLM) primitives, modern MLE agents are deployed on top of increasingly elaborate machinery: multi-agent orchestrators, dedicated retrieval subagents, and more. While such harnesses expand, the use of more primitive but improved coding agents - where LLMs have direct access to the execution environment through read, write, and bash primitives - has received little attention in the field. In this paper we find that, under an equal time budget and the same frontier LLM backbone, open-source state-of-the-art harnesses provide no advantages over a single session of a minimal-harness co
探索强代理在自主机器学习工程中的需求，对比复杂框架与简化框架的效果。
- 核心方法
- 在相同时间预算和最前沿的大型语言模型支持下，对比开源的复杂框架与简化框架的性能。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 现代自主机器学习工程代理依赖于复杂的多代理结构，本文探讨是否简化的框架也能达到相同效果。
- 关键实验
- 通过一系列实验对比了不同框架下的代理性能，使用了相同的时间预算和前沿的大型语言模型。
- 主要贡献
- 揭示了在相同条件下，简化框架与复杂框架在自主机器学习工程中的性能并无显著差异。
- 意义与局限
- 该研究有助于简化自主机器学习工程的实现，降低成本和复杂度。但局限在于实验环境和任务的多样性仍有待扩展。