Recent efforts have incorporated large language models (LLMs) with external resources (e.g., the Internet) or internal control flows (e.g., prompt chaining) for tasks requiring grounding or reasoning. However, these efforts have largely been piecemeal, lacking a systematic framework for constructing a fully-fledged language agent. To address this challenge, we draw on the rich history of agent design in symbolic artificial intelligence to develop a blueprint for a new wave of cognitive language agents. We first show that LLMs have many of the same properties as production systems, and recent efforts to improve their grounding or reasoning mirror the development of cognitive architectures built around production systems. We then propose Cognitive Architectures for Language Agents (CoALA), a conceptual framework to systematize diverse methods for LLM-based reasoning, grounding, learning, and decision making as instantiations of language agents in the framework. Finally, we use the CoALA framework to highlight gaps and propose actionable directions toward more capable language agents in the future.

翻译：近期研究尝试将大语言模型（LLMs）与外部资源（如互联网）或内部控制流（如提示链）结合，以完成需要具身性或推理的任务。然而，这些尝试大多零散无序，缺乏构建完整语言代理的系统性框架。为应对这一挑战，我们借鉴符号人工智能中代理设计的丰富历史，为新一代认知语言代理制定蓝图。我们首先阐明大语言模型与产生式系统具有诸多相似属性，且近期提升其具身性或推理能力的探索，实则是对围绕产生式系统构建的认知架构发展的镜像映射。继而提出语言代理认知架构（CoALA）——一个概念性框架，将基于LLM的推理、具身化、学习与决策的多样化方法系统化为该框架中语言代理的实例化形式。最后，我们运用CoALA框架揭示当前研究缺口，并针对未来构建能力更强的语言代理提出可行方向。