When Should Memory Stay Silent: Measuring Memory-Use Boundaries in Memory-Augmented Conversational Agents
arXiv:2606.06055v1 Announce Type: new Abstract: Long-term memory enables language model agents to support personalized interactions, but it remains unclear when available memories warrant integration into responses. Existing memory evaluations emphasize retrieval accuracy and downstream task utility, while overlooking whether retrieved sensitive memory content is warranted in the current turn. We introduce RBI-Eval, a controlled measurement study built around a probe set that compares model behavior with and without access to sensitive memory under identical benign prompts. We evaluate four ba
The proliferation of memory-augmented language models necessitates clearer guidelines and evaluation methods for responsible memory use, especially concerning sensitive data.
This research provides crucial tools and insights for developing AI agents that can utilize long-term memory effectively without compromising privacy or generating inappropriate content.
The focus of memory evaluation shifts from mere retrieval accuracy to the appropriateness of memory integration, introducing a nuanced ethical and practical consideration for AI development.
- · AI developers
- · Privacy advocates
- · Users of personalized AI
- · Developers of AI without robust memory governance
- · Models prone to over-sharing sensitive information
Improved ethical guidelines and development practices for memory-augmented AI agents.
Increased user trust and adoption of personalized AI systems due to better data handling.
The emergence of specialized AI governance frameworks focused on memory-use boundaries in conversational AI.
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Read at arXiv cs.AI