EverMemBench: Benchmarking Long-Term Interactive Memory in Large Language Models

• URL: http://arxiv.org/abs/2602.01313v2
• Date: Tue, 03 Feb 2026 03:03:41 GMT
• Title: EverMemBench: Benchmarking Long-Term Interactive Memory in Large Language Models
• Authors: Chuanrui Hu, Tong Li, Xingze Gao, Hongda Chen, Yi Bai, Dannong Xu, Tianwei Lin, Xinda Zhao, Xiaohong Li, Yunyun Han, Jian Pei, Yafeng Deng,
• Abstract summary: We introduce EverMemBench, a benchmark featuring multi-party, multi-group conversations spanning over 1 million tokens.<n>EverMemBench evaluates memory systems across three dimensions through 1,000+ QA pairs.
• Score: 16.865998112859604
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long-term conversational memory is essential for LLM-based assistants, yet existing benchmarks focus on dyadic, single-topic dialogues that fail to capture real-world complexity. We introduce EverMemBench, a benchmark featuring multi-party, multi-group conversations spanning over 1 million tokens with temporally evolving information, cross-topic interleaving, and role-specific personas. EverMemBench evaluates memory systems across three dimensions through 1,000+ QA pairs: fine-grained recall, memory awareness, and user profile understanding. Our evaluation reveals critical limitations: (1) multi-hop reasoning collapses in multi-party settings, with even oracle models achieving only 26%; (2) temporal reasoning remains unsolved, requiring version semantics beyond timestamp matching; (3) memory awareness is bottlenecked by retrieval, where current similarity-based methods fail to bridge the semantic gap between queries and implicitly relevant memories. EverMemBench provides a challenging testbed for developing next-generation memory architectures.

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