---
title: "LongMemEval: Benchmarking Long-Term Interactive Memory (Raw Archive)"
created: 2026-06-25
updated: 2026-06-25
type: raw
tags: ["memory-benchmark", "chat-assistant", "long-term-memory"]
source: "https://arxiv.org/abs/2410.10813"
---

# LongMemEval — Raw Archive

## Metadata

- **Title**: LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory
- **Authors**: Di Wu (UCLA), Hongwei Wang, Wenhao Yu (Tencent AI Lab Seattle), Yuwei Zhang (UC San Diego), Kai-Wei Chang (UCLA), Dong Yu (Tencent AI Lab Seattle)
- **Venue**: ICLR 2025
- **arXiv**: 2410.10813
- **Date**: 2024-10-14 (v1), 2025-03-04 (v2)
- **Category**: cs.CL
- **Code**: https://github.com/xiaowu0162/LongMemEval

## 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.

## Key Contributions

1. First comprehensive memory benchmark featuring 5 core abilities + abstention
2. Unified three-stage memory framework (indexing → retrieval → reading) with four control points
3. Empirically validated design optimizations: round granularity, fact-augmented keys, time-aware query expansion
4. Two standard settings: S (~115k tokens) and M (~1.5M tokens)
