Core Concepts
Proposing a framework, CoALA, to organize language agents using memory, actions, and decision-making processes.
Abstract
Recent efforts have combined large language models with external resources or internal control flows to create language agents. CoALA proposes a framework organizing agents based on memory components, action spaces, and decision-making processes. Language agents leverage LLMs for reasoning, planning, and managing memory. The paper draws parallels between production systems and LLMs to propose CoALA as a conceptual framework for designing general-purpose language agents.
The paper discusses the history of production systems and cognitive architectures in AI research. It introduces the concept of CoALA as a way to structure language agents with modular memory components, structured action spaces, and decision-making procedures. The proposed framework aims to organize existing work on language agents and guide future developments towards more capable agents.
CoALA organizes language agents along three key dimensions: information storage (working and long-term memories), action space (internal and external actions), and decision-making procedure (interactive loop with planning and execution). The framework aims to express existing agents coherently while identifying unexplored directions for developing new ones.
Stats
Recent efforts have augmented large language models with external resources or internal control flows for tasks requiring grounding or reasoning.
Language agents leverage commonsense priors present in LLMs to adapt to novel tasks.
Production systems generate outcomes by applying rules iteratively.
Cognitive architectures specify control flow for selecting, applying, and generating new productions.
CoALA proposes a conceptual framework to characterize and design general-purpose language agents.