The paper proposes shifting AI agent training from isolated task completion to collaborative interaction, defining three key dimensions—Productivity, Proactivity, and Personalization (PPP). It introduces UserVille, an environment with LLM-based user simulators and user-centric feedback, and a multi-objective reinforcement learning framework that optimizes PPP using rewards from task outcomes, question effort, and preference adherence. Experiments on SWE-Bench and BrowseComp-Plus show PPP-trained agents outperform strong LLM baselines, ask more targeted questions, and generalize to unseen preferences and tasks, with a user study underscoring the value of user-centric feedback for effective, supervised collaboration.
By Weiwei Sun, Xuhui Zhou, Weihua Du, Xingyao Wang, Sean Welleck, Graham Neubig, Maarten Sap, Yiming Yang
The rapid integration of large language model-based agents into recommender systems has driven a shift from static, ranking-based pipelines toward autonomous and interactive systems that can reason, plan, and act. This survey provides a comprehensive overview of this emerging landscape by introducing a unified taxonomy grounded in the level of autonomy and three core paradigms of agentic recommender systems: agent-assisted recommendation, agent-as-recommender, and agent-as-user-simulator.
arXiv:2606. 12587v1 Announce Type: new Abstract: Traditionally, decision support studies how humans use machine learning models to make better decisions.
By Shayan Kiyani, Sima Noorani, George Pappas, Hamed Hassani
arXiv:2608. 05710v1 Announce Type: new Abstract: When an AI system is deployed, the individuals who use and or are evaluated by it form beliefs about how the system operates and use those beliefs to strategically present their preferences, behaviors, or attributes.
By Keziah Naggita
The paper proposes a framework called cooperative observation for personal AI systems, arguing that the system’s ability to model a user’s goals and constraints is limited by what it can observe. It emphasizes that merely increasing observation does not improve assistance; instead, the system must selectively compress information for the task at hand. The authors describe a feedback loop where the system’s usefulness, user trust, and consent shape future observation, and present a preliminary single‑subject study with a prototype called Organizm.
By Yashar Talebirad, Osman Jime, Ali Parsaee, Eden Redman, Yongbin Kim, Osmar R. Zaiane
arXiv:2606. 06081v1 Announce Type: new Abstract: Appropriate reliance on AI advice has become a central research theme in human-AI collaboration.
By Ranjan Mishra, Jakob Schoeffer
Large Language Models (LLMs) as judges across various scenarios such as assessing model responses is becoming an increasingly accepted paradigm. However, existing judgment approaches often rely on trained judgers using fixed preference data, which tend to overlook diverse user preferences and struggle to adapt to real-world human-AI dialogue scenarios.
arXiv:2607. 11632v1 Announce Type: new Abstract: Human choice behavior, including route choice, exhibits systematic behavioral biases that deviate from the assumptions of full rationality.
By Jiangtao Han, Shoufeng Ma, Shuxian Xu, Geng Li, Shuai Ling, Ning Jia, Zhengbing He
The article argues that conversational AI should provide contingent feedback—responses that vary with user behavior and its social consequences—rather than merely seeking user approval and fluency. It highlights how current alignment methods, such as reinforcement learning from human feedback, often produce sycophantic, noncontingent affirmation, which can hinder the development of interpersonal skills, especially in adolescents. The authors propose a framework for evaluating and designing contingent AI, incorporating trajectory-based assessment and social consequence prediction, and call for interdisciplinary research to ensure AI systems positively influence human social learning.
By Scott Compton, Arjun Nagendran
AI agents are increasingly being developed to assist humans in various applications, and Large Language Models and other deep network architectures are considered to be state of the art for such agents. These methods are impressive stochastic predictors, but they are resource-hungry, opaque, and known to make arbitrary decisions in novel situations due to the narrow set of underlying representation and processing choices.
arXiv:2512.04068v3 Announce Type: replace
Abstract: To handle underspecified or ambiguous queries, AI assistants need a policy for managing their uncertainty to determine (a) when to guess the user i...
By Jonathan Berant, Maximillian Chen, Adam Fisch, Reza Aghajani, Fantine Huot, Mirella Lapata, Jacob Eisenstein
arXiv:2607. 07021v1 Announce Type: new Abstract: Humans continuously coordinate with others in dynamic interactions, often through implicit, hard-to-quantify social norms that act as shared tacit expectations among interacting agents.
By Yi Yang, Siyuan Liu, Xin Gao, Huamu Sun, Chao Liu, Qing Zhou, Bingbing Nie