LLM Agent Memory: A Survey from a Unified Representation--Management Perspective

ACL ARR 2026 January Submission4974 Authors

Keywords: LLM Agent, Memory, LLM Inference

Abstract: Large language models (LLMs) face significant challenges in sustaining long-term memory for agentic applications due to limited context windows. To address this limitation, many work has proposed diverse memory mechanisms to support long-term, multi-turn interactions, leveraging different approaches tailored to distinct memory storage objects, such as KV caches. In this survey, we present a unified taxonomy that organizes memory systems for long-context scenarios by decoupling memory abstractions from model-specific inference and training methods. We categorize LLM memory into three primary paradigms: natural language tokens, intermediate representations and parameters. For each paradigm, we organize existing methods by three management stages, including memory construction, update, and query, so that long-context memory mechanisms can be described in a consistent way across system designs, with their implementation choices and constraints made explicit. Finally, we outline key research directions for long-context memory system design.

Paper Type: Long

Research Area: AI/LLM Agents

Research Area Keywords: LLM/AI agents, Language Modes, multi-agent systems, LLM-based controllers, other LLM agent topics

Contribution Types: Surveys

Languages Studied: English, Chinese

Submission Number: 4974