NEOLAF, an LLM-powered neural-symbolic cognitive architecture
Richard Jiarui Tong, Cassie Chen Cao, Timothy Xueqian Lee, Guodong Zhao, Ray Wan, Feiyue Wang, Xiangen Hu, Robin Schmucker, Jinsheng Pan, Julian Quevedo, Yu Lu
This paper presents the Never Ending Open Learning Adaptive Framework (NEOLAF), an integrated neural-symbolic cognitive architecture that models and constructs intelligent agents. The NEOLAF framework is a superior approach to constructing intelligent agents than both the pure connectionist and pure symbolic approaches due to its explainability, incremental learning, efficiency, collaborative and distributed learning, human-in-the-loop enablement, and self-improvement. The paper further presents a compelling experiment where a NEOLAF agent, built as a problem-solving agent, is fed with complex math problems from the open-source MATH dataset. The results demonstrate NEOLAF's superior learning capability and its potential to revolutionize the field of cognitive architectures and self-improving adaptive instructional systems.