LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory

TL;DR

This paper introduces LongMemEval, a benchmark for evaluating long-term memory in chat assistants, revealing significant performance gaps and proposing optimized memory design strategies to enhance recall and reasoning over sustained interactions.

Contribution

We present LongMemEval, a comprehensive benchmark for long-term memory in chat assistants, and propose a unified framework with design optimizations to improve memory performance.

Findings

Commercial chat assistants drop 30% accuracy on long-term memory tasks.

Memory optimizations significantly improve recall and question answering.

The benchmark provides a challenging testbed for future memory system improvements.

Abstract

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.