@inproceedings{liu-etal-2026-evalmem,
title = "{E}val{M}em: An Operation-Level Diagnostic Framework for Long-Term Memory Systems",
author = "Liu, Zeyu and
Zhong, Jian and
Han, Rongduo and
Wu, Ziyang and
Tang, Shunye and
He, Chenghao and
Yang, Yaxuan and
Qiu, Yihang and
Wang, Ailing and
Liang, Xiao and
Xie, Guohuan and
Xue, Xiaokang and
Li, Gongchen and
Zhang, Haining and
Wang, Wei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1134/",
pages = "24002--24034",
ISBN = "979-8-89176-462-0",
abstract = "Long-horizon interactions with LLM-based assistants require memory systems that preserve and update user states, preferences, and interaction histories. Existing evaluations report end-to-end QA accuracy and cannot determine whether errors arise from encoding, retrieval, or generation. We introduce **EvalMem**, an operation-level diagnostic framework with three parallel Examiners. For each query, the Encoding Examiner checks whether the target fact is stored, the Retrieval Examiner assesses whether the native retriever returns usable evidence, and the Generation Examiner tests whether the model can answer from oracle evidence. Their outputs form fine-grained multi-label defect codes. To improve store-level diagnosis, we adapt agentic RAG with a recall-first strategy that searches using both the query and source evidence, increasing recall of present evidence on LoCoMo from 70.2{\%} to 95.6{\%}. Evaluations of seven memory systems on LoCoMo, LongMemEval-S, and dynamic DynaMem-Bench identify retrieval as the most frequently attributed failure layer; in default LoCoMo, retrieval defects reach 22.1{\%}, compared with 7.7{\%} for encoding and 6.5{\%} for generation. Guided by this diagnosis, **MemWiki**, a search-friendly auxiliary structure built from each system{'}s memory export, improves mean accuracy by 2.5 and 2.3 percentage points on LoCoMo and LongMemEval-S, respectively."
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<abstract>Long-horizon interactions with LLM-based assistants require memory systems that preserve and update user states, preferences, and interaction histories. Existing evaluations report end-to-end QA accuracy and cannot determine whether errors arise from encoding, retrieval, or generation. We introduce **EvalMem**, an operation-level diagnostic framework with three parallel Examiners. For each query, the Encoding Examiner checks whether the target fact is stored, the Retrieval Examiner assesses whether the native retriever returns usable evidence, and the Generation Examiner tests whether the model can answer from oracle evidence. Their outputs form fine-grained multi-label defect codes. To improve store-level diagnosis, we adapt agentic RAG with a recall-first strategy that searches using both the query and source evidence, increasing recall of present evidence on LoCoMo from 70.2% to 95.6%. Evaluations of seven memory systems on LoCoMo, LongMemEval-S, and dynamic DynaMem-Bench identify retrieval as the most frequently attributed failure layer; in default LoCoMo, retrieval defects reach 22.1%, compared with 7.7% for encoding and 6.5% for generation. Guided by this diagnosis, **MemWiki**, a search-friendly auxiliary structure built from each system’s memory export, improves mean accuracy by 2.5 and 2.3 percentage points on LoCoMo and LongMemEval-S, respectively.</abstract>
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%0 Conference Proceedings
%T EvalMem: An Operation-Level Diagnostic Framework for Long-Term Memory Systems
%A Liu, Zeyu
%A Zhong, Jian
%A Han, Rongduo
%A Wu, Ziyang
%A Tang, Shunye
%A He, Chenghao
%A Yang, Yaxuan
%A Qiu, Yihang
%A Wang, Ailing
%A Liang, Xiao
%A Xie, Guohuan
%A Xue, Xiaokang
%A Li, Gongchen
%A Zhang, Haining
%A Wang, Wei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F liu-etal-2026-evalmem
%X Long-horizon interactions with LLM-based assistants require memory systems that preserve and update user states, preferences, and interaction histories. Existing evaluations report end-to-end QA accuracy and cannot determine whether errors arise from encoding, retrieval, or generation. We introduce **EvalMem**, an operation-level diagnostic framework with three parallel Examiners. For each query, the Encoding Examiner checks whether the target fact is stored, the Retrieval Examiner assesses whether the native retriever returns usable evidence, and the Generation Examiner tests whether the model can answer from oracle evidence. Their outputs form fine-grained multi-label defect codes. To improve store-level diagnosis, we adapt agentic RAG with a recall-first strategy that searches using both the query and source evidence, increasing recall of present evidence on LoCoMo from 70.2% to 95.6%. Evaluations of seven memory systems on LoCoMo, LongMemEval-S, and dynamic DynaMem-Bench identify retrieval as the most frequently attributed failure layer; in default LoCoMo, retrieval defects reach 22.1%, compared with 7.7% for encoding and 6.5% for generation. Guided by this diagnosis, **MemWiki**, a search-friendly auxiliary structure built from each system’s memory export, improves mean accuracy by 2.5 and 2.3 percentage points on LoCoMo and LongMemEval-S, respectively.
%U https://aclanthology.org/2026.findings-emnlp.1134/
%P 24002-24034
Markdown (Informal)
[EvalMem: An Operation-Level Diagnostic Framework for Long-Term Memory Systems](https://aclanthology.org/2026.findings-emnlp.1134/) (Liu et al., Findings 2026)
ACL
- Zeyu Liu, Jian Zhong, Rongduo Han, Ziyang Wu, Shunye Tang, Chenghao He, Yaxuan Yang, Yihang Qiu, Ailing Wang, Xiao Liang, Guohuan Xie, Xiaokang Xue, Gongchen Li, Haining Zhang, and Wei Wang. 2026. EvalMem: An Operation-Level Diagnostic Framework for Long-Term Memory Systems. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 24002–24034, Budapest, Hungary. Association for Computational Linguistics.