Task-CoEvolve: Efficient Harness Optimization via Adaptive Validation Task Selection
Score
74提出将验证任务动态选择与框架优化协同演化的新思路，填补了该领域空白。
方法设计严谨，理论动机清晰，实验对比充分，论文结构完整。
能减少80%评估次数且保持性能，直接降低智能体开发成本，开源代码便于落地。
当前LLM智能体框架优化方兴未艾，该研究切中大规模部署中的效率痛点。
来自arXiv顶尖学术平台，作者机构为知名大学，并公开代码仓库。
Base 82 × T2 Source weight ×0.9 = 74
Curation line 70Selected
Why selected
It offers an innovative solution to a practical pain point, backed by solid experiments and open-source code, making it highly reproducible and practically valuable.
AI commentary
「This paper deserves attention from engineers working on agent system optimization. It solves the heavy-evaluation pain point with a lightweight method that is ready to use. If your team is iterating on harness code repeatedly, this approach could save you several-fold compute.」
Quick overview
A team from the University of Tokyo proposes Task-CoEvolve for efficient optimization of LLM agent harnesses. Traditional methods evaluate a full fixed validation set at every iteration, incurring high costs. Task-CoEvolve uses variance-weighted sampling to focus on tasks near the agent's capability frontier and estimates full-set scores from partial evaluations by accounting for sampling probabilities. On online text classification and Terminal-Bench 2.1, it outperforms fixed-subset baselines and matches full-set search while reducing evaluation counts by 80%. Code is open-sourced.
- Proposes Task-CoEvolve, which co-evolves validation tasks with the agent harness to dynamically select informative tasks.
- Uses variance-weighted sampling based on disagreement among candidate harnesses to focus on frontier tasks, with sampling-probability-adjusted full-set score estimation.
- Matches full-set search's final performance on benchmarks while reducing evaluation counts by 80%, significantly cutting optimization costs.
Full text
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