arXiv AI July 31, 2026 By Marylou Fauchard, Florian Carichon, Margarida Carvalho, Golnoosh Farnadi

Even More Deception: Objective Misalignment in Mixed-Motive LLM Multi-Agent Systems

arXiv:2607. 26120v1 Announce Type: new Abstract: Large Language Models (LLMs)-powered multi-agent systems are increasingly deployed in mixed-motive environments, where agents operate under asymmetric information and strategic deception due to conflicting or hidden objectives.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computation and Language

Sep 10

When Do Large Language Models Exhibit Unsolicited Deception?

arXiv:2504.00285v2 Announce Type: replace Abstract: Large Language Models (LLMs) are effective at deceiving when prompted to do so. Models that demonstrate better performance on reasoning tasks are a...

By Samuel M. Taylor, Benjamin K. Bergen

llms agents

arXiv AI

4d ago

Prompted Identity Degrades Cooperation in Multi-Agent LLM Systems

arXiv:2609.35928v1 Announce Type: cross Abstract: Multi-agent LLM systems increasingly mix models from several providers, yet exposing each agent's underlying model identity to its peers significantl...

By Xavier Del Giudice, Alessio Palma, Matteo Migliarini, Fabio Galasso, Indro Spinelli

llms agents benchmarks

arXiv AI

Jun 29

When Does Personality Composition Matter for Multi-Agent LLM Teams?

arXiv:2606. 27443v1 Announce Type: new Abstract: Personality prompting shapes how large language models communicate, yet whether these behavioral shifts affect objective task outcomes remains under-explored.

By Aryan Keluskar, Amrita Bhattacharjee, Huan Liu

llms agents robotics safety

arXiv Computation and Language

Aug 25

The Collaboration Tax: How Much LLM Multi-Agent Systems Pay to Coordinate

arXiv:2608.22152v1 Announce Type: new Abstract: Multi-agent systems built from large language models are deployed widely, yet how much performance is lost when two LLMs must coordinate rather than ac...

By Weixiang Sun, Zehong Wang, Hong Huang, Colby Nelson, Yanfang Ye

llms agents

arXiv AI

Jul 2

Large language models replicate and predict human cooperation across experiments in game theory

arXiv:2511. 04500v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as decision-making agents in high-stakes domains and as imitators of human behavior in the social and behavioral sciences.

By Andrea Cera Palatsi, Samuel Martin-Gutierrez, Ana S. Cardenal, Max Pellert

llms agents safety

arXiv AI

Sep 1

Lies We Can See: Joint Verbal and Non-Verbal Deception by VLM Agents in Embodied Social Interactions

arXiv:2608.30428v1 Announce Type: cross Abstract: Strategic deception by LLM and VLM agents has emerged as a central AI alignment and safety concern. Social-deduction games (where each player holds a...

By Jaewoo Ahn, Junseo Kim, Hyunseo Kim, Heeseung Yun, Jaehyeon Son, Zsolt Kira, Gunhee Kim

llms agents robotics multimodal benchmarks safety

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When Do Large Language Models Exhibit Unsolicited Deception?

Prompted Identity Degrades Cooperation in Multi-Agent LLM Systems

When Does Personality Composition Matter for Multi-Agent LLM Teams?

The Collaboration Tax: How Much LLM Multi-Agent Systems Pay to Coordinate

Large language models replicate and predict human cooperation across experiments in game theory

Lies We Can See: Joint Verbal and Non-Verbal Deception by VLM Agents in Embodied Social Interactions