CARA: A Hybrid Framework Integrating Swarm AI Agents and Knowledge Graphs for Advanced LLM Reasoning

Language models excel in linguistic processing but often face challenges with complex reasoning tasks that require real-world interaction and multi-step logic. This paper presents the Cognitive Adaptive Reasoning Architecture (CARA), a framework that enhances reasoning by integrating a modular swarm architecture with knowledge graphs (KGs) and specialized agents. These agents, equipped with diverse tools, dynamically adapt to various tasks, actively engaging with real-world data. CARA's continuous memory update cycle ensures each reasoning step is based on up-to-date, contextual KG knowledge, enhancing decision-making. Incorporating human insights further aligns outcomes with nuanced cognition. Experimental results demonstrate CARA's superior performance over larger models, advancing language models' reasoning capabilities for more context-aware, informed decision-making in dynamic environments.