mlsanityData to 14 Oct 2026

Strong Memory, Weak Control: An Empirical Study of Executive Functioning in LLMs

arXiv 2504.02789 ↗First posted 3 Apr 2025cs.CLcited by 6

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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

  1. 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenesscited by 3,673
  2. 2019Language Models as Knowledge Bases?cited by 1,936
  3. 2020Big Bird: Transformers for Longer Sequencescited by 1,684
  4. 2023Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formattingcited by 817
  5. 2022What learning algorithm is in-context learning? Investigations with linear modelscited by 684
  6. 2021Editing Factual Knowledge in Language Modelscited by 553
  7. 2019Are Transformers universal approximators of sequence-to-sequence functions?cited by 406
  8. 2024Repeat After Me: Transformers are Better than State Space Models at Copyingcited by 216
  9. 2024RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrievalcited by 64
  10. 2024CogBench: a large language model walks into a psychology labcited by 63
  11. 2023Working Memory Capacity of ChatGPT: An Empirical Studycited by 23
  12. 2024Do Language Models Exhibit Human-like Structural Priming Effects?cited by 21
4 more
  1. 2024Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformerscited by 15
  2. 2024Paraphrase Types Elicit Prompt Engineering Capabilitiescited by 15
  3. 2018A Dataset and Architecture for Visual Reasoning with a Working Memorycited by 12
  4. 2024On the Optimal Memorization Capacity of Transformerscited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

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  1. 2025Triangulating LLM Progress through Benchmarks, Games, and Cognitive Tests
  2. 2025Cognitive Mirrors: Exploring the Diverse Functional Roles of Attention Heads in LLM Reasoning
  3. 2023Empowering Working Memory for Large Language Model Agents
  4. 2025Visual Large Language Models Exhibit Human-Level Cognitive Flexibility in the Wisconsin Card Sorting Test
  5. 2025Large Language Models Show Signs of Alignment with Human Neurocognition During Abstract Reasoning
  6. 2026To Think or Not To Think, That is The Question for Large Reasoning Models in Theory of Mind Tasks
  7. 2024The Cognitive Capabilities of Generative AI: A Comparative Analysis with Human Benchmarks
  8. 2024Self-Attention Limits Working Memory Capacity of Transformer-Based Models