@inproceedings{wang-etal-2026-text2mem,
title = "{T}ext2{M}em: A Unified Memory Operation Language for Memory Operating System",
author = "Wang, Leo and
Yang, Lihai and
Chen, Boyu and
Xu, Kerun and
Zou, Gongyi and
Tang, Bo and
Xiong, Feiyu and
Chen, Siheng and
li, Zhiyu",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.100/",
pages = "2105--2119",
ISBN = "979-8-89176-395-1",
abstract = "Large language model agents increasingly rely on memory to support long-horizon interaction, yet existing frameworks expose only a small set of low-level primitives and lack a formal, executable specification for memory control. As a result, higher-order operations such as promotion, consolidation, or lifecycle governance are missing or inconsistently implemented, leading to unpredictable behavior across systems. We introduce Text2Mem, a unified memory operation language that standardizes the translation of natural-language instructions into reliable execution. Text2Mem defines a compact and expressive operation set spanning encoding, storage, and retrieval, and represents each instruction as a schema-based contract with explicit fields and semantic invariants. Validated schemas are parsed into typed operation objects and executed through a unified pipeline that supports both a SQL reference backend and real memory frameworks, enabling safe, deterministic, and portable behavior across heterogeneous systems. We further outline the Text2Mem Benchmark, which decouples schema generation from backend execution to systematically evaluate planning accuracy and execution fidelity. Together, Text2Mem and its benchmark establish a standardized foundation for controllable and reproducible memory management in LLM-based agents."
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<abstract>Large language model agents increasingly rely on memory to support long-horizon interaction, yet existing frameworks expose only a small set of low-level primitives and lack a formal, executable specification for memory control. As a result, higher-order operations such as promotion, consolidation, or lifecycle governance are missing or inconsistently implemented, leading to unpredictable behavior across systems. We introduce Text2Mem, a unified memory operation language that standardizes the translation of natural-language instructions into reliable execution. Text2Mem defines a compact and expressive operation set spanning encoding, storage, and retrieval, and represents each instruction as a schema-based contract with explicit fields and semantic invariants. Validated schemas are parsed into typed operation objects and executed through a unified pipeline that supports both a SQL reference backend and real memory frameworks, enabling safe, deterministic, and portable behavior across heterogeneous systems. We further outline the Text2Mem Benchmark, which decouples schema generation from backend execution to systematically evaluate planning accuracy and execution fidelity. Together, Text2Mem and its benchmark establish a standardized foundation for controllable and reproducible memory management in LLM-based agents.</abstract>
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%0 Conference Proceedings
%T Text2Mem: A Unified Memory Operation Language for Memory Operating System
%A Wang, Leo
%A Yang, Lihai
%A Chen, Boyu
%A Xu, Kerun
%A Zou, Gongyi
%A Tang, Bo
%A Xiong, Feiyu
%A Chen, Siheng
%A li, Zhiyu
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F wang-etal-2026-text2mem
%X Large language model agents increasingly rely on memory to support long-horizon interaction, yet existing frameworks expose only a small set of low-level primitives and lack a formal, executable specification for memory control. As a result, higher-order operations such as promotion, consolidation, or lifecycle governance are missing or inconsistently implemented, leading to unpredictable behavior across systems. We introduce Text2Mem, a unified memory operation language that standardizes the translation of natural-language instructions into reliable execution. Text2Mem defines a compact and expressive operation set spanning encoding, storage, and retrieval, and represents each instruction as a schema-based contract with explicit fields and semantic invariants. Validated schemas are parsed into typed operation objects and executed through a unified pipeline that supports both a SQL reference backend and real memory frameworks, enabling safe, deterministic, and portable behavior across heterogeneous systems. We further outline the Text2Mem Benchmark, which decouples schema generation from backend execution to systematically evaluate planning accuracy and execution fidelity. Together, Text2Mem and its benchmark establish a standardized foundation for controllable and reproducible memory management in LLM-based agents.
%U https://aclanthology.org/2026.findings-acl.100/
%P 2105-2119
Markdown (Informal)
[Text2Mem: A Unified Memory Operation Language for Memory Operating System](https://aclanthology.org/2026.findings-acl.100/) (Wang et al., Findings 2026)
ACL
- Leo Wang, Lihai Yang, Boyu Chen, Kerun Xu, Gongyi Zou, Bo Tang, Feiyu Xiong, Siheng Chen, and Zhiyu li. 2026. Text2Mem: A Unified Memory Operation Language for Memory Operating System. In Findings of the Association for Computational Linguistics: ACL 2026, pages 2105–2119, San Diego, California, United States. Association for Computational Linguistics.