做智能体工程化的团队会发现，你的 Harness 工作比想象中更可迁移——不用反复调模型，改接口就能显著提升性能，值得点开看具体实现。
一篇新论文提出一种不修改模型、只调整运行时接口（Harness）的方法，将重复交互失败转化为可复用的干预措施。在 7 个确定性环境、126 个模型-环境设置和 18 个基座模型上，该方法平均相对提升 88.5%。从一个模型轨迹学到的 Harness 可泛化到其他 17 个基座模型，说明它捕获的是环境结构而非模型特定模式。这对生产环境中部署智能体的团队有直接参考价值。
// Adapt the Interface, Not the Model // I am fascinated by the results across my cheap-model-plus-...
// Adapt the Interface, Not the Model // I am fascinated by the results across my cheap-model-plus-good-harness builds. This new paper also shows good signs of the code-as-agent-harness thesis. The idea is really simple. Do not touch the model. Instead, modify the runtime interface that wraps the frozen LLM. Then convert recurring interaction failures into reusable interventions on the harness side. The paper reports an average relative improvement 88.5% across 7 deterministic environments, 126 model-environment settings, and 18 backbones. A harness learned from one model trajectory generalizes to 17 other backbones. That tells you the harness is capturing environment structure, not model-specific patterns. If you ship agents in production, your harness work is more portable than you might assume. Paper: arxiv.org/abs/2605.22166 Learn to build effective AI agents in our academy: academy.dair.ai 💬 1 🔄 1 ❤️ 5 👀 330 📊 3 ⚡