arXiv:2606. 30801v1 Announce Type: cross Abstract: Personalization algorithms determine what content users encounter on online platforms.
arXiv:2608. 14692v1 Announce Type: cross Abstract: Personalized, generative AI systems increasingly adapt their behavior to individual users over time, fundamentally changing model behavior.
arXiv:2602. 02838v2 Announce Type: replace-cross Abstract: The detection of online influence operations -- coordinated campaigns by malicious actors to spread narratives -- has traditionally depended on content analysis or network features.
arXiv:2607. 10455v1 Announce Type: new Abstract: Autonomous CLI agents can now execute hundreds of actions across multi-hour sessions: writing code, executing shell commands, browsing the web, and managing cloud infrastructure, all with minimal human oversight.
arXiv:2606. 27936v1 Announce Type: cross Abstract: The widespread collection of fine-grained location data by commercial data brokers creates a re-identification risk that is not widely recognised by the public.
arXiv:2512. 05013v2 Announce Type: replace Abstract: Generative models augmented with external tools and update mechanisms (or \textit{agents}) have demonstrated capabilities beyond intelligent prompting of base models.
arXiv:2608. 16323v1 Announce Type: cross Abstract: The spread of misinformation on social networks poses a significant challenge to online communities and society at large.
arXiv:2606. 25836v2 Announce Type: replace Abstract: To better assist users with completing challenging tasks, AI agents mediate communications, access data, and interact with different APIs.
The World Wide Web was built on an assumption held for three decades: the primary consumer of web content is a human being. This permeates every layer; its access model presumes human visitors, its economics rest on human attention, and its content targets human perception.
arXiv:2607. 03162v1 Announce Type: new Abstract: LLM-powered agents struggle with personalization when users issue raw, underspecified queries.
arXiv:2603. 00829v2 Announce Type: replace-cross Abstract: Safe deployment of Large Language Model (LLM) agents in autonomous settings requires reliable oversight mechanisms.
arXiv:2606. 19116v1 Announce Type: new Abstract: The World Wide Web was built on an assumption held for three decades: the primary consumer of web content is a human being.
Personalizing large language models (LLMs) to individual users is essential for improving user experience, yet existing approaches typically rely on explicit preference supervision such as pairwise comparisons or demographic attributes, limiting their applicability in natural interaction settings. We propose IRIS, a framework that learns dynamic user personas directly from implicit interaction streams by extracting behavioral signals from everyday conversations and iteratively refining persona representations through a prediction-driven closed loop without requiring explicit feedback.