The ability of large language models (LLMs) to engage in credible dialogues
with humans, taking into account the training data and the context of the
conversation, has raised discussions about their ability to exhibit intrinsic
motivations, agency, or even some degree of consciousness. We argue that the
internal architecture of LLMs and their finite and volatile state cannot
support any of these properties. By combining insights from complementary
learning systems, global neuronal workspace, and attention schema theories, we
propose to integrate LLMs and other deep learning systems into an architecture
for cognitive language agents able to exhibit properties akin to agency,
self-motivation, even some features of meta-cognition.

通过将大型语言模型与深度学习系统整合，提出能够展示类似于代理、自我激励甚至一些元认知特征的认知语言代理体系结构。

DeepThought：自主自律系统的架构

DeepThought: An Architecture for Autonomous Self-motivated Systems

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.

利用符号人工智能的代理设计历史，我们提出了一种新的认知语言代理的蓝图，即 Cognitive Architectures for Language Agents (CoALA) 框架，这个框架将大型语言模型与外部资源或内部控制流结合起来，以实现基于语言模型的推理、概念化、学习和决策。通过 CoALA 框架，我们强调了目前语言代理的不足，并提出了未来发展更强大的语言代理的具体方向。