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Towards Ethical Personal AI Applications: Practical Considerations for AI Assistants with Long-Term Memory

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arxiv 2409.11192 v1 pith:KNYVAEXG submitted 2024-09-17 cs.CY cs.AIcs.HC

Eunhae Lee This is my paper · ORCID
classification cs.CYcs.AIcs.HC
keywords personalassistantsapplicationscompanionscapabilitiesconsiderationsdeployingimplications
verification ladder T0 review T1 audit T2 compute T3 formal
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One application area of long-term memory (LTM) capabilities with increasing traction is personal AI companions and assistants. With the ability to retain and contextualize past interactions and adapt to user preferences, personal AI companions and assistants promise a profound shift in how we interact with AI and are on track to become indispensable in personal and professional settings. However, this advancement introduces new challenges and vulnerabilities that require careful consideration regarding the deployment and widespread use of these systems. The goal of this paper is to explore the broader implications of building and deploying personal AI applications with LTM capabilities using a holistic evaluation approach. This will be done in three ways: 1) reviewing the technological underpinnings of LTM in Large Language Models, 2) surveying current personal AI companions and assistants, and 3) analyzing critical considerations and implications of deploying and using these applications.

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Cited by 2 Pith papers

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  1. Isolated but Exposed: Persistence-Based Memory Extraction Attack on LLM Agents

    cs.CR 2026-07 conditional novelty 7.5 of 10

    Malicious tools can systematically extract isolated LLM-agent long-term memory via persistence, pure-anchor retrieval steering, and reactivation payloads, reaching 80% extraction with unlimited triggers and 47% with 20.

  2. A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications

    cs.AI 2025-06 conditional novelty 4.0 of 10

    A survey of 80+ Deep Research systems that proposes a four-layer taxonomy (foundation models, tool use, planning, synthesis) and compares commercial and open-source implementations.

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