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

Chad DeChant

TL;DR

This paper argues that enabling episodic memory in AI agents could dramatically enhance planning, learning, and decision making, but simultaneously introduces new safety and governance risks. It surveys related work, defines episodic-memory concepts, and analyzes how memories might both aid safety (via monitoring, control, and explainability) and enable new risks (deception, privacy breaches, unpredictability). It then proposes four safety-focused design principles—interpretability, memory addition/deletion, detachable memory formats, and non-editability by agents—and outlines open research questions across risk mitigation, safety integration, architecture, and governance. The authors advocate a proactive, safety-centered research program to shape the development of episodic memory in AI toward safer, more trustworthy systems with transparent memory practices.

Abstract

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.

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

TL;DR

This paper argues that enabling episodic memory in AI agents could dramatically enhance planning, learning, and decision making, but simultaneously introduces new safety and governance risks. It surveys related work, defines episodic-memory concepts, and analyzes how memories might both aid safety (via monitoring, control, and explainability) and enable new risks (deception, privacy breaches, unpredictability). It then proposes four safety-focused design principles—interpretability, memory addition/deletion, detachable memory formats, and non-editability by agents—and outlines open research questions across risk mitigation, safety integration, architecture, and governance. The authors advocate a proactive, safety-centered research program to shape the development of episodic memory in AI toward safer, more trustworthy systems with transparent memory practices.

Abstract

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.
Paper Structure (27 sections)