EnvOpt：面向智能体环境的自动优化方法
EnvOpt: Automated Optimization for Agent Environments
一篇很实用的论文：不改模型不改 harness，只优化智能体环境就能把 Haiku 4.5 成本砍七成，做智能体的都该看看
EnvOpt 是一种针对智能体运行环境的端到端自动优化方法，不需要评分器或参考答案。在 AutomationBench 上，它把 Claude Haiku 4.5 的成本降低 72.7%，pass rate 提升 14.34%；同一优化后的环境迁移到 Opus 5，成本再降 68.1%。叠加在 Meta-Harness 优化过的 harness 上时，还能把 Haiku 4.5 的成本进一步降低 83.4%，准确率提升 11.17%。该论文在 5 个基准上验证了成本下降和 pass rate 保持或提升，并分析了自动优化中被采纳和被拒绝的环境优化策略。
EnvOpt: Automated Optimization for Agent Environments
An LLM agent's cost and success depend on its model, its harness, and its environment. In addition to the widely studied directions of parameter tuning and harness optimization, we propose the third direction: optimization over stationary environments. Agents are often deployed in rich environments shared by many tasks, such as a company's Slack workspace or a complex collection of data sources, documents or tickets. We call these environments stationary environments. We introduce EnvOpt, a method for automated end-to-end optimization of stationary environments for any given model and harness, using real or synthetic tasks without needing any graders or reference answers. On AutomationBench, EnvOpt reduces the cost of Claude Haiku 4.5 by 72.7% and raises its pass rate by 14.34%. The same environment transfers to Opus 5, cutting its cost by 68.1% with a 1.34% gain. Added on top of Meta-Harness's optimized harness for Haiku 4.5, it lowers cost by a further 83.4% and adds 11.17% accuracy over Meta-Harness alone. Across 5 benchmarks, EnvOpt significantly reduces the cost of agents while improving or maintaining pass rate, and is transferable to different models and different harnesses. We also discuss common successful and rejected environment optimization strategies during automated optimization. More broadly, our results show that how information is represented to an agent shapes both its cost and its success, making the environment worth optimizing in addition to the parameters and the harness.