Transferable Expertise for Autonomous Agents via Real-World Case-Based Learning
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 compar...
| Authors | Zhenyu Ma, Yuyang Song, Chunyi Yang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 14 April 2026 |
| Last updated at source | 14 April 2026 |
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