Welcome to Revise and Resubmit, the podcast where research meets reflection, and innovation takes shape one paper at a time. Today, we’re venturing into the exciting world of cognitive architectures and language agents. We’ll be reviewing the fascinating paper, "Cognitive Architectures for Language Agents," authored by Theodore R. Sumers, Shunyu Yao, Karthik Narasimhan, and Thomas L. Griffiths. Their work explores how large language models (LLMs) are evolving into autonomous language agents by drawing from cognitive science and symbolic AI.
Published in Transactions on Machine Learning Research and sponsored by some of the most cutting-edge institutions—Mila, Vector Institute, CIFAR, and Google DeepMind—this paper introduces the CoALA framework. CoALA reimagines language agents with modular memory systems and structured decision-making processes to help them interact better with both internal knowledge and the external world. The authors not only survey past advancements but also outline future directions, focusing on how these agents can integrate reasoning, retrieval, and adaptive learning to achieve greater capabilities.
At its heart, the research asks us to think beyond the current achievements of LLMs and reframe AI as a dynamic, cognitive system—one that grows, learns, and adapts. It’s a reminder that the path to general intelligence requires structure and insight, not just more data and parameters.
Special thanks to Theodore R. Sumers, Shunyu Yao, Karthik Narasimhan, Thomas L. Griffiths, and their sponsors for contributing this groundbreaking work to the field.
Here’s the question: Will the future of AI hinge on raw computational power, or will cognitive architectures become the secret key to unlocking true language-based intelligence?
Reference
Sumers, T., Yao, S., Narasimhan, K., & Griffiths, T. (2024). Cognitive Architectures for Language Agents. Transactions on Machine Learning Research. Retrieved from https://doi.org/10.48550/arXiv.2309.02427
