AutoSaddler：基于智能体执行轨迹的持久化更新的自动框架优化

摘要

大语言模型智能体在长时程任务上仍不可靠，因为细微的局部失败会在长时间交互中不断累积，最终导致整体任务失败。尽管外部框架能显著提升鲁棒性，但框架设计仍是一个手工且昂贵的过程，需要在庞大的提示词、工具配置和控制逻辑空间中进行搜索。我们提出AutoSaddler，一个自动框架优化系统，将框架改进形式化为离线学习问题，并利用小批量的失败信号迭代更新框架。AutoSaddler结合了失败轨迹诊断、将框架视为代码的结构化补丁生成，以及基于验证的更新选择。在GAIA2、SWE-Bench Pro和Terminal-Bench 2.0上的实验表明，AutoSaddler相较于对应的基础框架大幅提升了智能体性能，分别取得了9.0、9.6和10.0个百分点的增益。消融研究进一步表明，有效的框架优化得益于三个要素：深度调试而非浅层反思、针对性修改而非无约束编辑，以及泛化感知的选择而非针对特定轨迹的修复。综上所述，这些结果表明自动框架优化是通往更高效、更可靠的智能体系统的可行路径。

English

LLM agents remain unreliable on long-horizon tasks, where small local failures can compound over extended interactions and lead to overall task failure. Although external harnesses can substantially improve robustness, harness design remains a manual and expensive process that requires searching over a large space of prompts, tool configurations, and control logic. We propose AutoSaddler, an automatic harness optimization framework that formulates harness improvement as an offline learning problem and iteratively updates the harness using failure signals from mini-batches. AutoSaddler combines failure-trace diagnosis, structured patch generation that treats the harness as code, and validation-based update selection. Experiments on GAIA2, SWE-Bench Pro, and Terminal-Bench 2.0 show that AutoSaddler substantially improves agent performance over the corresponding base harnesses, achieving gains of 9.0, 9.6, and 10.0 percentage points, respectively. Ablation studies further suggest that effective harness optimization benefits from three ingredients: deep debugging rather than shallow reflection, targeted modifications rather than unconstrained editing, and generalization-aware selection rather than trajectory-specific repair. Together, these results suggest that automatic harness optimization is a promising path toward more performant and reliable agent systems.