arXiv AI

Agentic Analysis for Agentic Infrastructure: An LLM-Powered Pipeline for Comparative Governance of DAO and Corporate AI Protocols

arXiv:2606. 26203v1 Announce Type: new Abstract: As AI agent protocols proliferate, the governance structures shaping their interoperability standards remain empirically underexamined.

arXiv AI
Jun 9

"So There's a Catch-22 Here": How Early Adopters Who Build Multi-Agent LLM Systems Conceptualize Transparency

arXiv:2606. 08323v1 Announce Type: cross Abstract: Multi-agent large language model (LLM) systems are rapidly emerging, yet transparency, a cornerstone of responsible AI, remains under-defined in these distributed architectures, which have complexities of inter-agent coordination and orchestration.

By Suchismita Naik, Samir Passi, Mihaela Vorvoreanu, Scott Saponas, Amanda Hall
arXiv AI
Jun 26

The Governance Inversion Hypothesis: Why More AI Regulation May Produce Less Organisational Control

arXiv:2606. 26117v1 Announce Type: cross Abstract: This paper introduces the Governance Inversion Hypothesis (GIH) to explain a growing paradox in artificial intelligence (AI) governance: under conditions of increasing regulatory expansion and technological complexity, organisations may become more formally governed while simultaneously experiencing a decline in operational control over AI systems.

By Victor Frimpong
arXiv AI
Aug 10

Agentic AI: User Empowerment or Enclosure?

arXiv:2608. 06510v1 Announce Type: cross Abstract: Agentic AI promises a more flexible form of digital agency: systems that can act on users' behalf, from filtering content to negotiating prices to selecting services.

By David Gamba, Daniel M. Romero, Grant Schoenebeck