Deployment-Time Memorization in Foundation-Model Agents
Quick Answer
This paper shows that Foundation-model agents, such as Gemma 3 12B and GPT-4o-mini, utilize deployment-time memorization, balancing personalization recall and extraction risk.
Quick Take
Key findings reveal that summarization reduces adversarial extraction by up to 76%, but deletion fidelity suffers, with 20% recoverability of deleted data unless comprehensive purging is applied.
Key Points
- Summarization reduces adversarial extraction by 76% on Gemma 3 12B.
- Personalization recall remains nearly intact despite aggressive summarization.
- Increasing retrieval breadth (k) post-compression does not restore leakage.
- 20% of deleted information remains recoverable without full-pipeline purge.
- Forgetting Residue Score (FRS) quantifies recoverability of deleted data.
Paper Resources
Source Excerpt
arXiv:2606. 10062v1 Announce Type: new Abstract: Foundation-model agents are increasingly long-lived systems that remember users across interactions, making memorization an explicit deployment-time function rather than solely a property of model weights. Existing work addresses parametric memorization or audits fixed memory configurations, but does not characterize how memory-design choices jointly shape personalization utility, extraction risk, and deletion fidelity. …
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