arXiv AI July 28, 2026 By Jiaqi Lai, Hou Liang, Weihong Huang

Stability of AI Governance Systems: A Coupled Dynamics Model of Public Trust and Social Disruptions

arXiv:2603. 20248v2 Announce Type: replace-cross Abstract: AI systems are increasingly entrenched in public governance, yet scholarship lacks formal tools to determine when deviations of public trust in algorithmic institutions dissipate and when they grow into collapse.

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

arXiv AI

Jun 2

Dissociative Identity: Language Model Agents Lack Grounding for Reputation Mechanisms

arXiv:2605. 30169v2 Announce Type: replace-cross Abstract: As autonomous language model agents proliferate, forming an emerging agentic web with real-world consequences, what credibility signals can you use to decide whether to trust an unfamiliar agent in the wild and delegate to it?

By Botao Amber Hu, Helena Rong, Max Van Kleek

llms agents safety

arXiv AI

Jun 16

How to Detect and Measure the AI Dangers to Democracy

arXiv:2606. 16054v1 Announce Type: cross Abstract: Research on artificial intelligence and democracy has grown quickly over the last decade.

By Giulia Sandri, Claudio Novelli

agents

arXiv AI

Jun 16

Trust Between AI Agents: Measuring Formation, Breakage, and Recovery, with Implications for Governing Multi-Agent Systems

arXiv:2606. 14923v1 Announce Type: new Abstract: As language-model agents increasingly work in teams, each agent must decide how much to trust its teammates.

By Yujiao Chen

llms agents safety

arXiv AI

Jul 22

From Agent Failure Paths to Quantified Residual Risk: A Compositional Framework for Resilient Agentic AI

arXiv:2607. 18243v1 Announce Type: new Abstract: Agentic AI is crossing trust boundaries faster than current risk models can represent.

By Hassan Karim, Sai Sitharaman, Deepti Gupta, Danda B. Rawat

agents robotics

arXiv AI

Jun 2

Deliberative Curation: A Protocol for Multi-Agent Knowledge Bases

arXiv:2606. 00007v1 Announce Type: new Abstract: As AI agents transition from isolated tools to collaborative participants in shared knowledge ecosystems, governing collective knowledge curation becomes a critical challenge.

By Steven Johnson

agents benchmarks safety

arXiv Machine Learning

Aug 4

Trust or Check? Understanding the (Evolutionary) Dynamics of User Trust in AI Systems

arXiv:2603. 24742v2 Announce Type: replace-cross Abstract: As the capabilities and adoption of Artificial Intelligence (AI) systems grow, trust in these AI systems is an increasingly urgent concern.

By Adeela Bashir, Zhao Song, Ndidi Bianca Ogbo, Nataliya Balabanova, Martin Smit, Chin-wing Leung, Paolo Bova, Manuel Chica Serrano, Dhanushka Dissanayake, Manh Hong Duong, Elias Fernandez Domingos, Nikita Huber-Kralj, Marcus Krellner, Andrew Powell, Stefan Sarkadi, Fernando P. Santos, Zia Ush Shamszaman, Chaimaa Tarzi, Paolo Turrini, Grace Ibukunoluwa Ufeoshi, Victor A. Vargas-Perez, Alessandro Di Stefano, Simon T. Powers, The Anh Han

reinforcement-learning safety

Related stories

Dissociative Identity: Language Model Agents Lack Grounding for Reputation Mechanisms

How to Detect and Measure the AI Dangers to Democracy

Trust Between AI Agents: Measuring Formation, Breakage, and Recovery, with Implications for Governing Multi-Agent Systems

From Agent Failure Paths to Quantified Residual Risk: A Compositional Framework for Resilient Agentic AI

Deliberative Curation: A Protocol for Multi-Agent Knowledge Bases

Trust or Check? Understanding the (Evolutionary) Dynamics of User Trust in AI Systems