Automated Discovery Has No Universally Superior Harness
Score
81首次大规模系统比较自动化发现框架组件，揭示无通用最优框架，挑战主流做法。
方法论严谨，实验设计完整，统计指标明确，但仅覆盖有限模型和问题类型。
为自动化发现框架选择提供数据驱动指导，并提出可落地的自适应分配策略。
发表于2025年7月，前沿话题，与当前LLM自动化研究高度相关。
论文来自arXiv AI/CL/LG，作者信息透明，实验数据开放，但尚未经过同行评审。
Base 89.5 × T2 Source weight ×0.9 = 81
Curation line 70Selected
Why selected
Large-scale experiments (3.1M rollouts), rigorous statistics (repeated trials), and practically actionable conclusions, though limited to specific LLMs and problem sets.
AI commentary
「This paper is highly recommended for teams working on AutoML, agent search, and LLM-driven automation. It provides strong empirical evidence against blindly adopting existing harnesses and advocates for task-specific adaptation. The adaptive allocation strategy offers a plug-and-play improvement, and the released statistical infrastructure can accelerate future benchmarking.」
Quick overview
This paper systematically decomposes the components of automated discovery harnesses like OpenEvolve and TTT-Discover, and evaluates 30 budget-matched harnesses across 12 model-problem pairs using over 3.1 million LLM rollouts with repeated-trial statistical analysis. The results show no fixed harness is universally superior, and OpenEvolve variants generally underperform simpler alternatives. The authors suggest treating harness choice as a hyperparameter rather than a universal recipe, and propose an adaptive allocation strategy based on early discovery progress. All run pools and baseline null distributions are released as reusable statistical infrastructure.
- Systematically compared 30 discovery harnesses across 12 model-problem pairs using over 3.1 million LLM rollouts and repeated-trial statistics.
- No fixed harness is reliably superior across all settings, and OpenEvolve variants generally underperform simpler alternatives.
- Early discovery progress predicts final performance, enabling an adaptive allocation strategy that outperforms fixed harness and non-adaptive ensemble.
Full text
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