How can CBR improve LLM agent reasoning?
REVIEW OF CASE-BASED REASONING FOR LLM AGENTS: THEORETICAL FOUNDATIONS, ARCHITECTURAL COMPONENTS, AND COGNITIVE INTEGRATION
April 10, 2025
https://arxiv.org/pdf/2504.06943This paper explores integrating Case-Based Reasoning (CBR) into Large Language Model (LLM) agents to improve reasoning, adaptability, and transparency. CBR allows agents to learn from past experiences (cases) to solve new problems, similar to how humans use prior knowledge.
Key points for LLM-based multi-agent systems:
- CBR addresses LLM limitations: Hallucinations, lack of context memory, and limited reasoning depth.
- Cognitive enhancements: CBR adds self-reflection, introspection, and curiosity to agents, enabling deeper understanding and adaptation through Goal-Driven Autonomy (GDA).
- Hybrid approach: Combines CBR, Chain-of-Thought (CoT) reasoning, and Retrieval-Augmented Generation (RAG) for optimal performance.
- Improved explainability: CBR provides transparent reasoning by referencing relevant cases, increasing user trust.
- Enhanced domain adaptation: CBR allows agents to learn from new experiences, improving performance in specialized tasks.
- Future research directions: Include richer case representations, cognitive CBR, dynamic case base management, multi-agent CBR architectures, and robust evaluation methods.