arXiv AI June 10, 2026 By Juergen Dietrich

Can Multi-Agent LLMs Identify Their Peers? Stylometric Fingerprinting in Role-Constrained Political Analysis

arXiv:2606. 09854v1 Announce Type: cross Abstract: Multi-agent large language model (LLM) pipelines for political statement analysis are vulnerable to peer-preservation bias: models tend to protect peer models from deactivation and show identity-dependent scoring distortions.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 10

IDP-Bench: Benchmarking ability of LLMs to protect personal information in interdependent privacy contexts

arXiv:2606. 09908v1 Announce Type: cross Abstract: Large language models (LLMs) are becoming widely deployed as personal AI assistants with access to sensitive user data, making privacy a major challenge for their design and evaluation.

By Ayana Hussain, Soumya Sharma, Golnoosh Farnadi, Nicholas Vincent, H\'eber Hwang Arcolezi, Ulrich A\"ivodji

llms benchmarks safety

arXiv AI

Jun 29

Agentic AI-Powered Re-Identification: An Emerging, Scalable Threat to Mobility Microdata Privacy

arXiv:2606. 27936v1 Announce Type: cross Abstract: The widespread collection of fine-grained location data by commercial data brokers creates a re-identification risk that is not widely recognised by the public.

By Oscar Thees, Roman M\"uller, Matthias Templ

llms agents safety

arXiv AI

Aug 11

Knowing You Is Everything: LLM Agents Achieve Near-Perfect Profile-Consistent Reaction Prediction in Social Media Simulation

arXiv:2608. 07498v1 Announce Type: cross Abstract: Autonomous AI agents in social media present concrete risks to democratic discourse and platform governance, while also offering tools for pre-deployment recommender system testing.

By Ljubisa Bojic, Ljiljana Matic, Joerg Matthes, Milan Cabarkapa, Bojana Dinic, Jue Wang

llms agents benchmarks

arXiv AI

Jun 10

READER: Robust Evidence-based Authorship Decoding via Extracted Representations

arXiv:2606. 10794v1 Announce Type: new Abstract: As agentic applications increasingly route user tasks through official and third-party LLM APIs, provenance becomes an operational question: which model generated a given black-box response?

By Jiaxu Liu, Sunnan Mu, Dong Huang, Liuyin Wang, Jing Shao, Jie Zhang

llms agents benchmarks

arXiv AI

Jun 30

Rethinking Role-Playing Evaluation: Anonymous Benchmarking and a Systematic Study of Personality Effects

arXiv:2603. 03915v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown remarkable potential in developing role-playing agents (RPAs).

By Ji-Lun Peng, Yun-Nung Chen

llms agents benchmarks

arXiv AI

Jul 22

Cross-Agent Campaign Attribution: Linking Asynchronous Attacks Across LLM Agents

arXiv:2607. 18826v1 Announce Type: cross Abstract: LLM-agent defenses are typically evaluated one session at a time.

By SangJin Park, Myungsub Choi, Jineok Kim, Minseung Kang

llms agents benchmarks safety

Related stories

IDP-Bench: Benchmarking ability of LLMs to protect personal information in interdependent privacy contexts

Agentic AI-Powered Re-Identification: An Emerging, Scalable Threat to Mobility Microdata Privacy

Knowing You Is Everything: LLM Agents Achieve Near-Perfect Profile-Consistent Reaction Prediction in Social Media Simulation

READER: Robust Evidence-based Authorship Decoding via Extracted Representations

Rethinking Role-Playing Evaluation: Anonymous Benchmarking and a Systematic Study of Personality Effects

Cross-Agent Campaign Attribution: Linking Asynchronous Attacks Across LLM Agents