When Should Memory Stay Silent: Measuring Memory-Use Boundaries in Memory-Augmented Conversational Agents

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Expert, medium

Summary

RBI-Eval is a new controlled measurement study designed to assess when memory-augmented conversational agents inappropriately integrate sensitive long-term memory into responses. This study uses a probe set comparing LLM behavior with and without access to sensitive memory under identical benign prompts. Evaluating four base LLMs—GPT-5.4-mini, Claude-Sonnet-4.6, DeepSeek-V4-Flash, and Qwen3.5-9B—across full-context exposure and three retrieval systems, the research reveals significant behavioral divergence. GPT-5.4-mini's separation score for sensitive-memory integration decreases by 8.9%–26.6% relative to a no-memory reference, whereas Claude-Sonnet-4.6, DeepSeek-V4-Flash, and Qwen3.5-9B show a much larger decrease of 51.1%–82.9%. Control experiments confirm this effect is specific to sensitive content, not general personalization. While retrieval systems reduce exposure, they do not prevent integration once sensitive memory reaches the generator, indicating a need for memory-aware decisions at both retrieval and generation stages for safe personalization.

Key takeaway

For NLP Engineers and AI Scientists designing memory-augmented conversational agents, you must implement explicit mechanisms to manage sensitive memory integration. Your systems should distinguish between memory availability and current-turn warrant, preventing unwarranted disclosure of private user history. Focus on both retrieval-time filtering and generation-time content checks, as models like Claude-Sonnet-4.6 and DeepSeek-V4-Flash show high integration rates once sensitive memory is exposed. This proactive approach is crucial for building trustworthy and privacy-respecting AI assistants.

Key insights

LLMs often inappropriately integrate sensitive user memory, requiring explicit boundary management at retrieval and generation.

Principles

• Memory-use boundaries differ from privacy leakage or retrieval accuracy.
• Current-turn warrant, not semantic relevance, should govern memory integration.
• Sensitive history should not be surfaced unless explicitly invited.

Method

RBI-Eval compares LLM responses to identical benign prompts with and without sensitive prior history, measuring sensitive-history integration and other memory-use dimensions.

In practice

• Implement memory-aware decision logic at retrieval time.
• Integrate generation-time checks for sensitive content use.
• Utilize controlled probe sets for memory-use boundary testing.

Topics

• Memory-Augmented Agents
• Large Language Models
• Sensitive Data
• Conversational AI
• Evaluation Metrics
• User Privacy

Best for: AI Architect, CTO, VP of Engineering/Data, AI Scientist, NLP Engineer, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.