Resolving the dichotomy between the human-like yet constrained reasoning processes of cognitive architectures (CAs) and the broad but often noisy inference behavior of large language models (LLMs) remains a challenging yet exciting pursuit, aimed at enabling reliable machine reasoning capabilities in LLMs. Previous approaches that employ off-the-shelf LLMs in manufacturing decision-making face challenges in complex reasoning tasks, often exhibiting human-level yet unhuman-like behaviors due to insufficient grounding. This present article start to address this gap by asking whether LLMs can replicate cognition from CAs to make human-like decisions. We introduce cognitive LLMs , which are hybrid decision-making architectures comprised of a CA and an LLM through a knowledge transfer mechanism LLM-ACTR . Cognitive LLMs extract and embed knowledge of CA’s internal decision-making process as latent neural representations, inject this information into trainable LLM adapter layers, and fine-tune the LLMs for downstream prediction tasks. We find that, after knowledge transfer through LLM-ACTR , the cognitive LLMs offers better representations of human decision-making behaviors on a novel design for manufacturing problem, compared to an LLM-only model that employs chain-of-thought. Taken together, the results open up new research directions for equipping LLMs with the necessary knowledge to computationally model and replicate the internal mechanisms of human cognitive decision-making. We release the code and data samples at https://github.com/SiyuWu528/LLM-ACTR .
Wu et al. (2025) studied this question.