arXiv Machine Learning October 21, 2026 By Masahiro Kaneko, Hiromi Arai, Timothy Baldwin

Disclosure Begets Disclosure: A Self-Reinforcing Privacy Loop in LLM Agents with Persistent Memory

Read the original on arXiv Machine Learning →

The Flow has not summarised this story yet — read it at arXiv Machine Learning.

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.

By Hakeem Hannoon, Andrew Zhao, Mihir Narayan, Sharvin Goyal, Ivaxi Sheth

llms

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.

By Lingxiang Xu, Jiaoyun Yang, Min Hu, Hongtu Chen, Ning An

llms agents

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.

By Lei (Rachel), Chen, Guilin Zhang, Kai Zhao, Dalmo Cirne, Andy Olsen, Xu Chu, Zeke Miller, Alet Blanken, Amine Anoun, Jerry Ting

llms agents nlp safety

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.

By Alex Kwon

llms agents

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...

By Bofan Xie, Chaokun Wang, Guanchen Ge, Songyao Wang, Fang Niu, Hang Zhang, Wei Wang

llms agents benchmarks

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...

By Renfei Zhang, Niloofar Mireshghallah

reinforcement-learning fine-tuning safety

One email a morning, machine-written

Related stories

Mitigating Over-Personalization in LLMs via Structured Memory

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

Deployment-Time Memorization in Foundation-Model Agents

Manufactured Confidence: How Memory Consolidation Turns Hearsay into Confident Facts

Can Agents Forget When Asked? A Benchmark for Memory Unlearning in LLM Agents

Reinforcement Learning on Benign Facts Amplifies Leakage of Memorized Private Data