Princeton University: Cognitive Architectures for Language Agents
Summary of Read Full Report
This research paper proposes a framework called CoALA (Cognitive Architectures for Language Agents) for building more sophisticated language agents.
CoALA draws parallels between Large Language Models (LLMs) and production systems from symbolic AI, suggesting that control flow mechanisms used in cognitive architectures can be applied to LLMs to improve reasoning, grounding, learning, and decision-making.
The authors present CoALA as a blueprint for organizing existing methods and guiding future development of more capable language agents, highlighting key components like memory modules and various action types.
The paper examines several existing language agents through the lens of CoALA and proposes actionable directions for future research. Finally, the authors address some conceptual questions regarding the boundaries of agents and their environments.