mlsanityData to 7 Oct 2026

Episodic memory in AI agents poses risks that should be studied and mitigated

arXiv 2501.11739 ↗First posted 20 Jan 2025cs.AI cs.CYcited by 11

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Abstract (arXiv)

Most current AI models have little ability to store and later retrieve a record or representation of what they do. In human cognition, episodic memories play an important role in both recall of the past as well as planning for the future. The ability to form and use episodic memories would similarly enable a broad range of improved capabilities in an AI agent that interacts with and takes actions in the world. Researchers have begun directing more attention to developing memory abilities in AI models. It is therefore likely that models with such capability will be become widespread in the near future. This could in some ways contribute to making such AI agents safer by enabling users to better monitor, understand, and control their actions. However, as a new capability with wide applications, we argue that it will also introduce significant new risks that researchers should begin to study and address. We outline these risks and benefits and propose four principles to guide the development of episodic memory capabilities so that these will enhance, rather than undermine, the effort to keep AI safe and trustworthy.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 68 of its 129 references that are in the map. First posted on arXiv 20 Jan 2025.

Builds on

68 of 129 references.

References · 68

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  5. 2016Deep Learning with Differential Privacycited by 2,682
  6. 2022Locating and Editing Factual Associations in GPTcited by 2,609
  7. 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,302
  8. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  9. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
  10. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  11. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
  12. 2016Concrete Problems in AI Safetycited by 1,582
38 more
  1. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
  2. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,295
  3. 2015Prioritized Experience Replaycited by 1,070
  4. 2022Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agentscited by 1,029
  5. 2019Machine Unlearningcited by 1,020
  6. 2022Mass-Editing Memory in a Transformercited by 976
  7. 2021Ethical and social risks of harm from Language Modelscited by 957
  8. 2023Progress measures for grokking via mechanistic interpretabilitycited by 822
  9. 2022Discovering Language Model Behaviors with Model-Written Evaluationscited by 805
  10. 2022Discovering Latent Knowledge in Language Models Without Supervisioncited by 733
  11. 2020How Much Knowledge Can You Pack Into the Parameters of a Language Model?cited by 713
  12. 2014Neural Turing Machinescited by 630
  13. 2024A Survey on the Memory Mechanism of Large Language Model based Agentscited by 622
  14. 2023MemoryBank: Enhancing Large Language Models with Long-Term Memorycited by 539
  15. 2023Identifying the Risks of LM Agents with an LM-Emulated Sandboxcited by 514
  16. 2023Active Retrieval Augmented Generationcited by 506
  17. 2021Fast Model Editing at Scalecited by 491
  18. 2024The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearningcited by 488
  19. 2020Hopfield Networks is All You Needcited by 461
  20. 2023Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrationscited by 401
  21. 2023On the Planning Abilities of Large Language Models : A Critical Investigationcited by 308
  22. 2021Unsolved Problems in ML Safetycited by 291
  23. 2022The Alignment Problem from a Deep Learning Perspectivecited by 246
  24. 2023Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Drivingcited by 231
  25. 2024Foundational Challenges in Assuring Alignment and Safety of Large Language Modelscited by 193
  26. 2019Search on the Replay Buffer: Bridging Planning and Reinforcement Learningcited by 188
  27. 2022In-context Reinforcement Learning with Algorithm Distillationcited by 186
  28. 2023Augmenting Language Models with Long-Term Memorycited by 186
  29. 2023AI Deception: A Survey of Examples, Risks, and Potential Solutionscited by 185
  30. 2021The Values Encoded in Machine Learning Researchcited by 172
  31. 2023Model evaluation for extreme riskscited by 167
  32. 2024The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Surveycited by 164
  33. 2023Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attackscited by 156
  34. 2024Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Modelscited by 150
  35. 2023Large Language Models can Strategically Deceive their Users when Put Under Pressurecited by 132
  36. 2024Empowering Biomedical Discovery with AI Agentscited by 117
  37. 2024An Adversarial Perspective on Machine Unlearning for AI Safetycited by 113
  38. 2016Model-Free Episodic Controlcited by 100

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 6.

11 papers.