  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.
