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

arXiv: https://arxiv.org/abs/2504.02789 · HTML: https://mlsanity.com/paper/2504.02789/

## 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 (most cited first, 16 of 16)

- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting](https://mlsanity.com/paper/2310.11324/) (2023)
- [What learning algorithm is in-context learning? Investigations with linear models](https://mlsanity.com/paper/2211.15661/) (2022)
- [Editing Factual Knowledge in Language Models](https://mlsanity.com/paper/2104.08164/) (2021)
- [Are Transformers universal approximators of sequence-to-sequence functions?](https://mlsanity.com/paper/1912.10077/) (2019)
- [Repeat After Me: Transformers are Better than State Space Models at Copying](https://mlsanity.com/paper/2402.01032/) (2024)
- [RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrieval](https://mlsanity.com/paper/2402.18510/) (2024)
- [CogBench: a large language model walks into a psychology lab](https://mlsanity.com/paper/2402.18225/) (2024)
- [Working Memory Capacity of ChatGPT: An Empirical Study](https://mlsanity.com/paper/2305.03731/) (2023)
- [Do Language Models Exhibit Human-like Structural Priming Effects?](https://mlsanity.com/paper/2406.04847/) (2024)
- [Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers](https://mlsanity.com/paper/2406.18400/) (2024)
- [Paraphrase Types Elicit Prompt Engineering Capabilities](https://mlsanity.com/paper/2406.19898/) (2024)
- [A Dataset and Architecture for Visual Reasoning with a Working Memory](https://mlsanity.com/paper/1803.06092/) (2018)
- [On the Optimal Memorization Capacity of Transformers](https://mlsanity.com/paper/2409.17677/) (2024)

## Built on by (most cited first, 6 of 6)

- [WMF-AM: Probing LLM Working Memory via Depth-Parameterized Cumulative State Tracking](https://mlsanity.com/paper/2603.27343/) (2026)
- [How LLMs Comprehend Temporal Meaning in Narratives: A Case Study in Cognitive Evaluation of LLMs](https://mlsanity.com/paper/2507.14307/) (2025)
- [Red Teaming Large Reasoning Models](https://mlsanity.com/paper/2512.00412/) (2025)
- [Simulating Human Memory with Language Models](https://mlsanity.com/paper/2605.25680/) (2026)
- [In-context superposition: human-like working memory interference in large language models](https://mlsanity.com/paper/2604.09670/) (2026)
- [CogArena: A Multimethod Evaluation of Cognitive Ability Structure in Large Language Models](https://mlsanity.com/paper/2607.24999/) (2026)

## 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.