Episodic memory in AI agents poses risks that should be studied and mitigated
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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.
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50 most cited of 68 All 68 (JSON)
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Citing papers per year: 2025: 5; 2026: 6.
11 papers.
- 2025AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challengescited by 167
- 2025Memory Injection Attacks on LLM Agents via Query-Only Interactioncited by 127
- 2025Agentic AI Frameworks: Architectures, Protocols, and Design Challengescited by 16
- 2026Trajectory-Informed Memory Generation for Self-Improving Agent Systemscited by 16
- 2026On Safety Risks in Experience-Driven Self-Evolving Agentscited by 12
- 2026Agentic Unlearning: When LLM Agent Meets Machine Unlearningcited by 6
- 2025Safety Must Precede the Deployment of Open-Ended AIcited by 3
- 2026Stateless Yet Not Forgetful: Implicit Memory as a Hidden Channel in LLMscited by 3
- 2025Fire360: A Benchmark for Robust Perception and Episodic Memory in Degraded 360-Degree Firefighting Videoscited by 2
- 2026Evolving Afferent Architectures: Biologically-inspired Models for Damage-Avoidance Learningcited by 0
- 2026Eternal Sunshine of the Spotless Mind: Systematically Erasing LLM's Memoriescited by 0
- 2026Eternal Sunshine of the Spotless Mind: Systematically Erasing LLM's Memories29 Sep 2026
- 2026On Safety Risks in Experience-Driven Self-Evolving Agents18 Apr 2026
- 2026Trajectory-Informed Memory Generation for Self-Improving Agent Systems11 Mar 2026
- 2026Stateless Yet Not Forgetful: Implicit Memory as a Hidden Channel in LLMs9 Feb 2026
- 2026Agentic Unlearning: When LLM Agent Meets Machine Unlearning6 Feb 2026
- 2026Evolving Afferent Architectures: Biologically-inspired Models for Damage-Avoidance Learning4 Feb 2026
- 2025Agentic AI Frameworks: Architectures, Protocols, and Design Challenges13 Aug 2025
- 2025Fire360: A Benchmark for Robust Perception and Episodic Memory in Degraded 360-Degree Firefighting Videos2 Jun 2025
- 2025AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges15 May 2025
- 2025Memory Injection Attacks on LLM Agents via Query-Only Interaction5 Mar 2025
- 2025Safety Must Precede the Deployment of Open-Ended AI6 Feb 2025