@inproceedings{tang-etal-2026-mnemis,
title = "Mnemis: Dual-Route Retrieval on Hierarchical Graphs for Long-Term {LLM} Memory",
author = "Tang, Zihao and
Yu, Xin and
Xiao, Ziyu and
Wen, Zengxuan and
Li, Zelin and
Zhou, Jiaxi and
Wang, Hualei and
Wang, Haohua and
Huang, Haizhen and
Deng, Weiwei and
Sun, Feng and
Zhang, Qi",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1096/",
pages = "23914--23928",
ISBN = "979-8-89176-390-6",
abstract = "AI Memory, specifically how models organizes and retrieves historical messages, becomes increasingly valuable to Large Language Models (LLMs), yet existing methods (RAG and Graph-RAG) primarily retrieve memory through similarity-based mechanisms. While efficient, such System-1-style retrieval struggles with scenarios that require global reasoning or comprehensive coverage of all relevant information. In this work, We propose Mnemis, a novel memory framework that integrates System-1 similarity search with a complementary System-2 mechanism, termed Global Selection. Mnemis organizes memory into a base graph for similarity retrieval and a hierarchical graph that enables top-down, deliberate traversal over semantic hierarchies. By combining the complementary strength from both retrieval routes, Mnemis retrieves memory items that are both semantically and structurally relevant. Mnemis achieves state-of-the-art performance across all compared methods on long-term memory benchmarks, scoring 93.9 on LoCoMo and 91.6 on LongMemEval-S using GPT-4.1-mini."
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%0 Conference Proceedings
%T Mnemis: Dual-Route Retrieval on Hierarchical Graphs for Long-Term LLM Memory
%A Tang, Zihao
%A Yu, Xin
%A Xiao, Ziyu
%A Wen, Zengxuan
%A Li, Zelin
%A Zhou, Jiaxi
%A Wang, Hualei
%A Wang, Haohua
%A Huang, Haizhen
%A Deng, Weiwei
%A Sun, Feng
%A Zhang, Qi
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F tang-etal-2026-mnemis
%X AI Memory, specifically how models organizes and retrieves historical messages, becomes increasingly valuable to Large Language Models (LLMs), yet existing methods (RAG and Graph-RAG) primarily retrieve memory through similarity-based mechanisms. While efficient, such System-1-style retrieval struggles with scenarios that require global reasoning or comprehensive coverage of all relevant information. In this work, We propose Mnemis, a novel memory framework that integrates System-1 similarity search with a complementary System-2 mechanism, termed Global Selection. Mnemis organizes memory into a base graph for similarity retrieval and a hierarchical graph that enables top-down, deliberate traversal over semantic hierarchies. By combining the complementary strength from both retrieval routes, Mnemis retrieves memory items that are both semantically and structurally relevant. Mnemis achieves state-of-the-art performance across all compared methods on long-term memory benchmarks, scoring 93.9 on LoCoMo and 91.6 on LongMemEval-S using GPT-4.1-mini.
%U https://aclanthology.org/2026.acl-long.1096/
%P 23914-23928
Markdown (Informal)
[Mnemis: Dual-Route Retrieval on Hierarchical Graphs for Long-Term LLM Memory](https://aclanthology.org/2026.acl-long.1096/) (Tang et al., ACL 2026)
ACL
- Zihao Tang, Xin Yu, Ziyu Xiao, Zengxuan Wen, Zelin Li, Jiaxi Zhou, Hualei Wang, Haohua Wang, Haizhen Huang, Weiwei Deng, Feng Sun, and Qi Zhang. 2026. Mnemis: Dual-Route Retrieval on Hierarchical Graphs for Long-Term LLM Memory. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 23914–23928, San Diego, California, United States. Association for Computational Linguistics.