MemoryBank: Enhancing Large Language Models with Long-Term Memory
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Cited by 308 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Revolutionary advancements in Large Language Models have drastically reshaped our interactions with artificial intelligence systems. Despite this, a notable hindrance remains-the deficiency of a long-term memory mechanism within these models. This shortfall becomes increasingly evident in situations demanding sustained interaction, such as personal companion systems and psychological counseling. Therefore, we propose MemoryBank, a novel memory mechanism tailored for LLMs. MemoryBank enables the models to summon relevant memories, continually evolve through continuous memory updates, comprehend, and adapt to a user personality by synthesizing information from past interactions. To mimic anthropomorphic behaviors and selectively preserve memory, MemoryBank incorporates a memory updating mechanism, inspired by the Ebbinghaus Forgetting Curve theory, which permits the AI to forget and reinforce memory based on time elapsed and the relative significance of the memory, thereby offering a human-like memory mechanism. MemoryBank is versatile in accommodating both closed-source models like ChatGPT and open-source models like ChatGLM. We exemplify application of MemoryBank through the creation of an LLM-based chatbot named SiliconFriend in a long-term AI Companion scenario. Further tuned with psychological dialogs, SiliconFriend displays heightened empathy in its interactions. Experiment involves both qualitative analysis with real-world user dialogs and quantitative analysis with simulated dialogs. In the latter, ChatGPT acts as users with diverse characteristics and generates long-term dialog contexts covering a wide array of topics. The results of our analysis reveal that SiliconFriend, equipped with MemoryBank, exhibits a strong capability for long-term companionship as it can provide emphatic response, recall relevant memories and understand user personality.
Key facts. Cited by 556 papers in the map (data through 14 Oct 2026). Builds on 14 of its 19 references that are in the map. First posted on arXiv 17 May 2023.
Builds on
14 of 19 references.
References · 14
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
- 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
- 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
- 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
- 2022Scaling Instruction-Finetuned Language Modelscited by 3,024
- 2023A Survey of Large Language Modelscited by 2,576
- 2017Billion-scale similarity search with GPUscited by 2,497
- 2020MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformerscited by 1,463
- 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 843
- 2014Neural Turing Machinescited by 644
2 more
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Citing papers per year: 2023: 19; 2024: 48; 2025: 129; 2026: 360.
556 papers.
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- 2023Qwen Technical Reportcited by 3,344
- 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
- 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,439
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- 2023A Comprehensive Overview of Large Language Modelscited by 648
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- 2025ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memorycited by 284
- 2025Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactionscited by 254
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- 2025MemEvolve: Meta-Evolution of Agent Memory Systemscited by 95
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- 2026Learning from Others, Acting for You: Cross-User Memory Sharing for LLM Agents26 Sep 2026
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- 2024Understanding the planning of LLM agents: A surveycited by 421
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- 2026HGP:An on-device personalized agent memory via hybrid graph storage7 Oct 2026
- 2026MINDSET: Energy-based Schema Evolution for Long Conversational Agent Memory6 Oct 2026
- 2026DSV-Mem: Evaluating Multimodal Memory in Professional Workflows for MLLM Agents6 Oct 2026
- 2026AECG: Asymmetric Experience Consolidation and Governance In Multi-Agent Systems4 Oct 2026
- 2026Memadapter: Counterfactual Adaptation Against Memory-induced Sycophancy4 Oct 2026
- 2026EVISKILL: Grounding Skill Evolution in Replayable Evidence4 Oct 2026
- 2026Principles that Guide, Actions that Inform: Agent Evolution via Knowledge Abstraction3 Oct 2026
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