Huang et al. (2025)

Find It

DOI

Abstract

While Large Language Models (LLMs) exhibit remarkable reasoning abilities, we demonstrate that they fundamentally lack a core aspect of human cognition: working memory. Human working memory is an active cognitive system that enables not only the temporary storage of information but also its processing and utilization. Without working memory, individuals may produce unrealistic conversations, exhibit self-contradictions, and struggle with tasks that require mental reasoning. Existing evaluations using N-back or context-dependent tasks fail as they allow LLMs to exploit external context rather than retaining the reasoning process in the latent space. We introduce three novel tasks—(1) Number Guessing, (2) Yes-No Deduction, and (3) Math Magic—that isolate internal representation from external context. Across seventeen frontier models spanning four major model families, we consistently observe irrational or contradictory behaviors, highlighting LLMs' inability to retain and manipulate latent information. Our work establishes a new benchmark for evaluating working memory in LLMs and identifies this deficit as a critical obstacle to artificial general intelligence. Code and prompts will be made publicly available upon publication.

Reference