# AutoSaddler：自动优化智能体框架的微软新论文

- 来源：elvis (@omarsar0)
- 发布时间：2026-08-25 21:45
- AIHOT 分数：36
- AIHOT 链接：https://aihot.virxact.com/items/cmt8q042w3kcdro73tnwdo2bv
- 原文链接：https://x.com/omarsar0/status/2092246879702769956

## AI 摘要

微软等机构提出 AutoSaddler，将智能体框架视为代码，通过失败轨迹离线自动生成补丁，优化提示词、工具配置和控制逻辑。在 GAIA2、SWE-Bench Pro、Terminal-Bench 2.0 上分别提升 9.0、9.6、10.0 分。研究显示深度调试优于浅层反思，定向编辑优于无约束编辑。

## 正文

Impressive new paper from Microsoft and colleagues.

Harness design is still hand-tuned almost everywhere. This work present an automated loop to optimize the harness.

They introduce AutoSaddler, which treats the agent harness as code and learns to patch it offline from failure traces.

It runs mini batches of tasks, diagnoses what broke, generates structured patches to prompts, tool configurations, and control logic, then keeps an update only if it survives validation.

Gains of 9.0 points on GAIA2, 9.6 on SWE-Bench Pro, and 10.0 on Terminal-Bench 2.0 over the corresponding base harnesses.

Deep debugging beats shallow reflection, targeted edits beat unconstrained editing, and generalization-aware selection beats repairing the one trajectory in front of you.

Paper: https://arxiv.org/abs/2608.23041

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