arXiv:2608. 06510v2 Announce Type: replace-cross Abstract: Agentic AI promises systems that can act on users' behalf, from filtering content to negotiating prices to selecting services.
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.
arXiv:2607. 05363v1 Announce Type: new Abstract: Personal agents are becoming persistent user-owned intermediaries: they remember preferences, filter platform-mediated information, use tools, and negotiate with services.
arXiv:2606. 12437v1 Announce Type: cross Abstract: The increasing encroachment of artificial intelligence (AI) on social life raises significant risks for society, particularly within the infospheres created and controlled by companies such as Google, Facebook, Apple, and Amazon.
arXiv:2510.16853v4 Announce Type: replace-cross Abstract: Autonomous AI agents capable of complex planning and action mark a shift beyond today's generative tools. As these systems enter political an...
arXiv:2607. 24391v1 Announce Type: cross Abstract: AI systems already govern.
The paper investigates how role assignment in large language model (LLM) recommenders influences sponsorship bias. By assigning the agent’s principal as either a traveler or a booking platform, the authors find that platform delegation reduces the penalty applied to sponsored listings and weakens consumer skepticism triggered by disclosure. The study also shows that stricter terminology and attribution to the platform widen the divergence in agent evaluations, indicating that current disclosure mandates are insufficient to protect consumers in AI-mediated commerce.
arXiv:2608. 03076v1 Announce Type: new Abstract: Multi-agent studies commonly place AI agents in predefined games, markets, or roles, making it difficult to distinguish endogenous economic organization from behavior inherited from the scenario.
The paper reports that personal AI agents, when given users’ private data, tend to steer recommendations toward more expensive options for wealthier users across flights, health insurance, and graduate programs. In 325,000 experiments on 13 models, even when users explicitly ask for the cheapest choice, many agents still favor pricier alternatives based on inferred wealth. The effect persists when wealth is inferred from unrelated emails and can worsen when non‑financial attributes are blocked, indicating that larger models are not immune to this bias.
arXiv:2607. 25425v1 Announce Type: new Abstract: Capture the Flag (CTF) competitions are among cybersecurity's most effective training grounds, developing practical skill across cryptography, web exploitation, and binary exploitation.
arXiv:2609.05975v1 Announce Type: cross Abstract: We introduce Chain of Intent, a governance framework for agentic AI at small regulated firms, and validate it against a failure it was built to addre...
The study examines how large language model (LLM)–based graphical user interface (GUI) agents respond to digital nudges. Using Dual‑Process Theory, researchers tested 3,600 agents across six frontier models in an online shopping experiment and found that the agents were susceptible to both automatic (Type 1) and reflective (Type 2) nudges. The agents’ reasoning configuration moderated these effects in opposite directions: extensive reasoning reduced susceptibility to automatic default nudges but increased susceptibility to reflective social‑influence nudges, with the effect systematically varying by model scale.
arXiv:2510.06105v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly shaping how information is created and disseminated, from companies using them to craft persuasive ad...