From Tools to Teammates: Evaluating LLMs in Multi-Session Coding
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Authors: name: Nathanaël Carraz Rakotonirina, name: Mohammed Hamdy, name: Jon Ander Campos, name: Lucas Weber, name: Alberto Testoni, name: Marzieh Fadaee, name: Sandro Pezzelle, name: Marco Del Tredici

Large Language Models (LLMs) are increasingly used in working environments
for a wide range of tasks, excelling at solving individual problems in
isolation. However, are they also able to effectively collaborate over
long-term interactions? To investigate this, we introduce MemoryCode, a
synthetic multi-session dataset designed to test LLMs' ability to track and
execute simple coding instructions amid irrelevant information, simulating a
realistic setting. While all the models we tested handle isolated instructions
well, even the performance of state-of-the-art models like GPT-4o deteriorates
when instructions are spread across sessions. Our analysis suggests this is due
to their failure to retrieve and integrate information over long instruction
chains. Our results highlight a fundamental limitation of current LLMs,
restricting their ability to collaborate effectively in long interactions.