arXiv AI
Aug 11
Mitigating Over-Personalization in LLMs via Structured Memory
arXiv:2608. 08300v1 Announce Type: new Abstract: Conversational assistants increasingly rely on persistent long-term memory to personalize responses across sessions.
Disclosure Begets Disclosure: A Self-Reinforcing Privacy Loop in LLM Agents with Persistent Memory
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arXiv AI
Jun 6
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.
arXiv AI
Jun 10
Deployment-Time Memorization in Foundation-Model Agents
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.
arXiv AI
Jun 30
Manufactured Confidence: How Memory Consolidation Turns Hearsay into Confident Facts
arXiv:2606. 29279v1 Announce Type: cross Abstract: LLM agents carry conclusions across steps and sessions in compressed memory, and memory products (e.
arXiv AI
Oct 15
Can Agents Forget When Asked? A Benchmark for Memory Unlearning in LLM Agents
arXiv:2610.18052v1 Announce Type: new Abstract: Long-term memory has become a core component of large language model (LLM) agents. As agents accumulate personal information over long-term use, users...
arXiv AI
Aug 25
Reinforcement Learning on Benign Facts Amplifies Leakage of Memorized Private Data
arXiv:2608.21727v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is deployed to make models better at reasoning tasks, but its side effect on what models will d...
arXiv Machine Learning
Aug 5
DP-MemView: A Memory Interface for Attribute-Level Transcript Privacy in Long-Term LLM Agents
arXiv:2608. 03130v1 Announce Type: cross Abstract: Long-term memory enables persistent personalization in LLM agents, but repeated memory-conditioned responses can cumulatively reveal protected attributes even when they are never stated explicitly.
arXiv AI
Jun 10
The Interlocutor Effect: Why LLMs Leak More Personal Data to Agents Than Humans
arXiv:2606. 09844v1 Announce Type: cross Abstract: Large Language Models (LLMs) alter their privacy behavior based on the perceived identity of their interlocutor.
arXiv AI
Oct 15
Loose Lips: Understanding Privacy Leakage in Agentic Dialogue
arXiv:2610.14071v1 Announce Type: cross Abstract: As LLM agents are increasingly deployed in high-stakes enterprise settings, understanding how they handle sensitive information in agentic dialogue h...
arXiv AI
Sep 30
Audience-Bound Persistent Memory: Authorization Across the Memory Lifecycle
The paper introduces Audience‑Bound Persistent Memory, a system that tracks the audience of each memory item and enforces authorization throughout the memory lifecycle. Each item carries the audience present at recording, and derived items are partitioned or suppressed based on the intersection of source audiences, expanding only through explicit grants. The authors implement the approach in two reference architectures—a flat store and a relationship graph—and evaluate it on 10,000 multi‑party histories, showing that no forbidden items entered any context while unscoped retrieval exposed forbidden items in 82% of cases, and that entitled recall matched policy‑equivalent baselines and outperformed unscoped retrieval by 0.30 Recall@5.
arXiv AI
Oct 1
Unlearning Deceptive Behaviors in LLMs with Contrastive Forget Sets
The paper introduces PACT, a method for unlearning deceptive behaviors in large language models by using contrastive forget sets that compare a model’s responses under deceptive and neutral contexts. PACT trains the model to produce pressure‑aware counterfactual targets, preserving benign system‑prompt adherence and reasoning traces while dramatically reducing deception rates from over 50% to under 3% on 32B reasoning models.
arXiv AI
Sep 16
Attention is All You Need Until You Need Retention
arXiv:2501.09166v2 Announce Type: replace-cross Abstract: Pretrained Transformers keep what they learned in their weights and lose what they observe once a session ends. The first version of this pap...