Review of Case-Based Reasoning for LLM Agents: Theoretical Foundations, Architectural Components, and Cognitive Integration

TL;DR

The paper addresses the limitations of LLM agents in reasoning with structured knowledge, persistent memory, and accountable decision-making by proposing a Case-Based Reasoning (CBR) framework for augmentation. It develops a formal theoretical basis, including a structured case definition, retrieval, adaptation, and learning processes, and presents an architectural blueprint that integrates CBR with LLM reasoning. A key contribution is the incorporation of cognitive dimensions and Goal-Driven Autonomy (GDA) through a CBR-GDA framework, enabling dynamic goal formulation and continuous learning. Comparative analyses indicate that CBR-augmented LLM agents provide improved explainability, domain adaptation, and solution quality over Chain-of-Thought and vanilla Retrieval-Augmented Generation, highlighting a promising neuro-symbolic direction for robust autonomous reasoning in real-world tasks.

Abstract

Agents powered by Large Language Models (LLMs) have recently demonstrated impressive capabilities in various tasks. Still, they face limitations in tasks requiring specific, structured knowledge, flexibility, or accountable decision-making. While agents are capable of perceiving their environments, forming inferences, planning, and executing actions towards goals, they often face issues such as hallucinations and lack of contextual memory across interactions. This paper explores how Case-Based Reasoning (CBR), a strategy that solves new problems by referencing past experiences, can be integrated into LLM agent frameworks. This integration allows LLMs to leverage explicit knowledge, enhancing their effectiveness. We systematically review the theoretical foundations of these enhanced agents, identify critical framework components, and formulate a mathematical model for the CBR processes of case retrieval, adaptation, and learning. We also evaluate CBR-enhanced agents against other methods like Chain-of-Thought reasoning and standard Retrieval-Augmented Generation, analyzing their relative strengths. Moreover, we explore how leveraging CBR's cognitive dimensions (including self-reflection, introspection, and curiosity) via goal-driven autonomy mechanisms can further enhance the LLM agent capabilities. Contributing to the ongoing research on neuro-symbolic hybrid systems, this work posits CBR as a viable technique for enhancing the reasoning skills and cognitive aspects of autonomous LLM agents.