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cs.HC2026

Stayin' Aligned Over Time: Towards Longitudinal Human-LLM Alignment via Contextual Reflection and Privacy-Preserving Behavioral Data

Simret Araya Gebreegziabher, Allison E Sproul, Yinuo Yang +3

Current human-AI alignment and evaluation methods for large language models (LLMs) often rely on preference signals collected immediately after an interaction. This practice implic…

cs.HC2026

Comparing Human Oversight Strategies for Computer-Use Agents

Chaoran Chen, Zhiping Zhang, Zeya Chen +9

LLM-powered computer-use agents (CUAs) are shifting users from direct manipulation to supervisory coordination. Existing oversight mechanisms, however, have largely been studied as…

cs.HC2026

Through the Lens of Human-Human Collaboration: A Configurable Research Platform for Exploring Human-Agent Collaboration

Bingsheng Yao, Jiaju Chen, Chaoran Chen +3

Intelligent systems have traditionally been designed as tools rather than collaborators, often lacking critical characteristics that collaboration partnerships require. Recent adva…

cs.HC2026

From Human-Human Collaboration to Human-Agent Collaboration: A Vision, Design Philosophy, and an Empirical Framework for Achieving Successful Partnerships Between Humans and LLM Agents

Bingsheng Yao, Chaoran Chen, April Yi Wang +3

The emergence of Large Language Model (LLM) agents enables us to build agent-based intelligent systems that move beyond the role of a "tool" to become genuine collaborators with hu…

cs.HC2026

The Behavioral Fabric of LLM-Powered GUI Agents: Human Values and Interaction Outcomes

Simret Araya Gebreegziabher, Yukun Yang, Charles Chiang +7

Large Language Model (LLM)-powered web GUI agents are increasingly automating everyday online tasks. Despite their popularity, little is known about how users' preferences and valu…

cs.HC2025

Beyond Permissions: Investigating Mobile Personalization with Simulated Personas

Ibrahim Khalilov, Chaoran Chen, Ziang Xiao +3

Mobile applications increasingly rely on sensor data to infer user context and deliver personalized experiences. Yet the mechanisms behind this personalization remain opaque to use…