LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory

Recent large language model (LLM)-driven chat assistant systems have integrated memory components to track user-assistant chat histories, enabling more accurate and personalized responses. However, their long-term memory capabilities in sustained interactions remain underexplored. We introduce LONGMEMEVAL, a comprehensive benchmark designed to evaluate five core long-term memory abilities of chat assistants: information extraction, multi-session reasoning, temporal reasoning, knowledge updates, and abstention. With 500 meticulously curated questions embedded within freely scalable user-assistant chat histories, LONGMEMEVAL presents a significant challenge to existing long-term memory systems, with commercial chat assistants and long-context LLMs showing a 30% accuracy drop on memorizing information across sustained interactions. We then present a unified framework that breaks down the long-term memory design into three stages: indexing, retrieval, and reading. Built upon key experimental insights, we propose several memory design optimizations including session decomposition for value granularity, fact-augmented key expansion for indexing, and time-aware query expansion for refining the search scope. Extensive experiments show that these optimizations greatly improve both memory recall and downstream question answering on LONGMEMEVAL. Overall, our study provides valuable resources and guidance for advancing the long-term memory capabilities of LLM-based chat assistants, paving the way toward more personalized and reliable conversational AI. Our benchmark and code are publicly available at https://github.com/xiaowu0162/LongMemEval . INTRODUCTION Large language models (LLMs) have exhibited impressive capabilities in solving diverse tasks through natural language, leading to numerous successful chat assistant applications (OpenAI, 2022; Microsoft, 2023). Nevertheless, LLMs face limitations on tasks relying heavily on personal knowledge accumulated through long-term user-AI interactions, such as psychological counseling or secretarial duties (Zhong et al., 2024) . Failing to incorporate user background and preferences into responses can diminish the response's accuracy as well as user satisfaction. To personalize LLM-based assistants, long-term memory, the ability to memorize, recall, and reason with a long interaction history, is indispensable. Recently, several commercial (OpenAI, 2024; Coze, 2024) and open-source assistant systems with memory (Zhong et al., 2024; Zhang et al., 2024) have been introduced. These systems leverage techniques like compressing, indexing, and retrieving from chat histories to generate more accurate and personalized responses. Despite these advances, there has been limited progress in holistically evaluating the memory capability in long-term interactions. While several benchmarks evaluate LLMs on understanding long chat histories (