Transferable Expertise for Autonomous Agents via Real-World Case-Based Learning
Summary: arXiv:2604.12717v1 Announce Type: new
Abstract: 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.
Introduction
Autonomous agents powered by large language models (LLMs) are increasingly being integrated into various sectors, demonstrating impressive capabilities in general reasoning. However, these agents often encounter challenges when faced with complex tasks that demand a nuanced understanding of task structure and the ability to apply prior experiences effectively. This limitation has underscored the need for innovative approaches to enhance the learning and performance of these agents.
Proposed Framework
In response to the challenges faced by LLM-based agents, we introduce a case-based learning framework designed to facilitate the transfer of knowledge from past tasks to new, similar tasks. This framework encompasses several key components:
- Experience Conversion: Transforming past task experiences into structured knowledge assets that can be reused.
- Task-Relevant Knowledge Extraction: Identifying and utilizing knowledge pertinent to specific tasks.
- Analytical Prompts: Crafting prompts that guide agents toward a more structured analysis of new tasks.
- Operational Skills Reuse: Allowing agents to apply operational skills learned from previous experiences.
Evaluation and Results
We rigorously tested our framework against a unified benchmark comprising six categories of complex tasks. The evaluation included comparisons with several baseline methods, such as Zero-Shot, Few-Shot, Checklist Prompt, and Rule Memory. The results indicated that our case-based learning framework achieved consistently superior performance across all tasks.
Notably, our method not only matched but often outperformed the best baseline in every tested case, particularly excelling in more intricate task scenarios. This performance trend suggests that as task complexity increases, the advantages of case-based learning become even more pronounced.
Conclusion and Future Work
This study demonstrates that case-based learning provides a viable pathway for enhancing the capabilities of autonomous agents in real-world applications. The ability for an agent to reuse practical knowledge gleaned from one case to assist in another showcases the potential for collaborative learning among agents. Future research will focus on expanding the framework’s applicability across more diverse task categories and refining the extraction of relevant knowledge to further improve agent performance.
In conclusion, our findings highlight the promise of case-based learning as a transformative approach for developing professional-grade autonomous agents capable of navigating the complexities of real-world tasks.