Extraneous Cognitive Load in Large Language Models

Cognitive Load Theory (CLT) shows that human problem-solving is affected by both intrinsic load, arising from task difficulty, and extraneous load, arising from how task information is presented. LLMs show analogous effects, where presentation alone can affect model performance, but these effects have not yet been systematically isolated from task difficulty. To address this gap, we introduce CogLoadBench, a controlled benchmark that varies five CLT-inspired presentation factors across three reasoning task families. Across models, we find that higher extraneous load conditions often reduce accuracy by either adding competition or complicating the reasoning path. To support diagnosis and mitigation of load-related effects, we propose the Extraneous Load Score (ELS), a prompt-time metric computed from activations that estimates a model's extraneous load, and show that it generalizes across reasoning task types and load levels. We further show that shifting model representations toward lower-ELS directions at inference time can significantly improve model performance without changing prompt content.