elvis@omarsar0 · X一项新研究提出通过改进包裹冻结LLM的运行时接口来优化AI代理性能，而非修改模型本身。该方法将反复出现的交互失败转化为对运行时层的可复用干预，在7个确定性环境、126个设置中取得平均88.5%的相对性能提升。关键发现是，从单一模型轨迹中学习到的运行时方法可成功迁移至18个不同模型骨架，证明其捕捉的是环境结构而非模型特异性模式。这为生产环境中部署AI代理提供了更高可移植性的解决方案。
// 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.