让智能体从真实任务中学习可复用经验，提升复杂场景下的表现。
Transferable Expertise for Autonomous Agents via Real-World Case-Based Learning
- 将过往任务经验转化为可复用的知识资产，支持跨任务迁移。
- 在六类复杂任务中均优于基线，复杂任务上优势更明显。
- 不同智能体可共享实用知识，适合构建专业级自动化系统。
基于大模型的自主智能体在通用推理任务中表现良好，但在复杂真实场景中仍难以有效利用任务结构、关键约束和过往经验。本文提出一种基于案例的学习框架，将过去任务的经验转化为可重用的知识资产，使智能体能够将已有案例经验迁移到新任务中，实现更结构化的分析。与依赖预训练知识或静态提示的方法不同，本框架强调从真实案例中提取并复用任务相关知识、分析提示和操作技能。我们在涵盖六类复杂任务的统一基准上评估该方法，并与零样本、少样本、检查清单提示和规则记忆等基线对比。结果表明，该方法在所有任务中均表现出色，且在每项任务上均达到或超过最佳基线性能，尤其在复杂任务上提升显著。进一步分析显示，案例学习的优势随任务复杂度增加而增强，且一个智能体习得的实用知识可被其他智能体复用。这些发现表明，基于案例的学习为构建面向真实工作场景的专业级智能体提供了有前景的路径。
原文摘要 · Abstract (English)
LLM-based autonomous agents perform well on general reasoning tasks but still struggle to reliably use task structure, key constraints, and prior experience in complex real-world settings. We propose a case-based learning framework that converts experience from past tasks into reusable knowledge assets, allowing agents to transfer prior case experience to new tasks and perform more structured analysis. Unlike methods based mainly on pretrained knowledge or static prompts, our framework emphasizes extracting and reusing task-relevant knowledge, analytical prompts, and operational skills from real cases. We evaluate the method on a unified benchmark of six complex task categories and compare it with Zero-Shot, Few-Shot, Checklist Prompt, and Rule Memory baselines. Results show that our method achieves consistently strong performance across all tasks and matches or outperforms the best baseline in every case, with especially clear gains on more complex tasks. Further analysis shows that the advantage of case-based learning increases with task complexity, and that practical knowledge acquired by one agent can be reused by others. These findings suggest that case-based learning offers a promising path for building professional agents for real-world work.
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