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
arXiv:2606. 28347v1 Announce Type: cross Abstract: Contemporary AI safety spans pre-training interventions, post-training alignment, deployment-time controls, monitoring, and red-teaming.
By Charles L. Wang, Keir Dorchen, Peter Jin
arXiv:2605. 28114v2 Announce Type: replace Abstract: Language-model agents are moving from single-user assistants into persistent networks that build trust and reputation with one another, and the same models increasingly control physically embodied robots as well as software.
By Messi H. J. Lee
arXiv:2608. 07070v1 Announce Type: cross Abstract: With the rapid diffusion of AI-generated content, AI-driven misinformation is becoming increasingly pervasive and difficult to govern, undermining information credibility and social trust.
By Qin Li, Gui Zhang, Minyu Feng, Matjaz Perc, Attila Szolnoki
arXiv:2607. 15992v1 Announce Type: new Abstract: Over the past decade, responsible AI (RAI) has produced a substantial body of practice for identifying and mitigating the risks AI poses in high-stakes settings.
By Trisevgeni Papakonstantinou, Cansu Canca, Farah Nanji, Waheedullah Pardess, Jen Weedon, Jasmijn Remmers, Eliza Krigman, Matthew Ball, Yalda Daryani, Kiran Iqbal, Francielle Vargas, Mar\'ia Llorente S\'anchez, Joe Humphreys, Fendi Tsim, Kelly Fitzpatrick, Jeff Dunn, Catherine Feldman
The paper "Governing at Machine Speed: An Adaptive Intelligence Architecture for Real-Time AI Policy Enforcement" highlights a gap in enterprise AI governance, where 78% of organizations lack auditable evidence of policy enforcement. It introduces AGIL, a five-layer adaptive governance architecture that uses machine learning for real-time detection, risk classification, sub-100ms policy enforcement, continuous attestation, and policy evolution. The authors argue that the failure is organizational and architectural, not technical, and call for future empirical validation of AGIL.
By Sandeep Bokkasam, B. Durgalakshmi