Strong Memory, Weak Control: An Empirical Study of Executive Functioning in LLMs
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Abstract (arXiv)
Working memory, or the ability to hold and manipulate information in the mind, is a critical component of human intelligence and executive functioning. It is correlated with performance on various cognitive tasks, including measures of fluid intelligence, which encompasses reasoning and problem solving. We use a comprehensive set of classic working memory tasks to estimate the working memory capacity of large language models (LLMs). We find that in most cases, LLMs exceed normative human scores. However, we do not find that the increased capacity of working memory is associated with higher performance on other executive functioning tasks or problem solving benchmarks. These results suggest that LLMs may have deficits in attentional control and cognitive flexibility, which result in difficulties with inhibiting automatic responses and adapting to shifting information. Our findings suggest that current reasoning models have mixed results in compensating for these deficits.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 16 of its 53 references that are in the map. First posted on arXiv 3 Apr 2025.
Builds on
16 of 53 references.
References · 16
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4 more
- 2024Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformerscited by 15
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Built on by
Citing papers per year: 2025: 2; 2026: 4.
6 papers.
- 2026WMF-AM: Probing LLM Working Memory via Depth-Parameterized Cumulative State Trackingcited by 4
- 2025How LLMs Comprehend Temporal Meaning in Narratives: A Case Study in Cognitive Evaluation of LLMscited by 3
- 2025Red Teaming Large Reasoning Modelscited by 2
- 2026Simulating Human Memory with Language Modelscited by 2
- 2026In-context superposition: human-like working memory interference in large language modelscited by 1
- 2026CogArena: A Multimethod Evaluation of Cognitive Ability Structure in Large Language Modelscited by 0
- 2026CogArena: A Multimethod Evaluation of Cognitive Ability Structure in Large Language Models27 Jul 2026
- 2026Simulating Human Memory with Language Models25 May 2026
- 2026In-context superposition: human-like working memory interference in large language models1 Apr 2026
- 2026WMF-AM: Probing LLM Working Memory via Depth-Parameterized Cumulative State Tracking28 Mar 2026
- 2025Red Teaming Large Reasoning Models29 Nov 2025
- 2025How LLMs Comprehend Temporal Meaning in Narratives: A Case Study in Cognitive Evaluation of LLMs18 Jul 2025
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- 2025Triangulating LLM Progress through Benchmarks, Games, and Cognitive Tests
- 2025Cognitive Mirrors: Exploring the Diverse Functional Roles of Attention Heads in LLM Reasoning
- 2023Empowering Working Memory for Large Language Model Agents
- 2025Visual Large Language Models Exhibit Human-Level Cognitive Flexibility in the Wisconsin Card Sorting Test
- 2025Large Language Models Show Signs of Alignment with Human Neurocognition During Abstract Reasoning
- 2026To Think or Not To Think, That is The Question for Large Reasoning Models in Theory of Mind Tasks
- 2024The Cognitive Capabilities of Generative AI: A Comparative Analysis with Human Benchmarks
- 2024Self-Attention Limits Working Memory Capacity of Transformer-Based Models