自主机器学习工程中智能体需要多复杂的辅助架构？
How Much of a Harness Does a Strong Agent Need for Autonomous ML Engineering?
Apple 研究团队通过对比实验发现，在同等时间预算和模型基座下，现有的开源复杂智能体架构在机器学习工程任务中并未表现出比极简编码智能体基准更高的性能。研究认为，在当前编码智能体设置下，多智能体编排等辅助层往往是冗余的，模型基座性能才是决定性因素。
AuthorsKirill Brilliantov†‡, Alejandro Hernández-Cano†‡**, Emmanuel Abbé†
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 coding agent baseline, pointing to the backbone as the primary driver for performance. Via a series of large-scale systematic ablation studies, we argue that the machinery layers become redundant in the coding agent setting. We conclude that the effort spent elaborating hand-crafted harnesses around strong models yields poor returns for current MLE benchmarks.
- † EPFL
- ‡ Equal contribution
- ** Work done while at Apple
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来源：Apple Machine Learning Research · machinelearning.apple.com