Text2Mem: A Unified Memory Operation Language for Memory Operating System

ACL ARR 2026 January Submission4465 Authors

Keywords: memory, LLM, agent

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.

Paper Type: Long

Research Area: AI/LLM Agents

Research Area Keywords: human-AI interaction/cooperation, human-in-the-loop, human-centered evaluation

Contribution Types: NLP engineering experiment, Data resources

Languages Studied: English, Chinese

Submission Number: 4465