Images co-edited with AI are often labeled to disclose AI use. However, creators feel these labels do not sufficiently describe their contributions to the editing process. Editing histories preserve requests, suggestions, and revisions, but how do creators use these logs to judge contribution? We investigated this with Roop, a mixed-initiative design probe that links image regions to editing histories (spatial provenance) and summarizes interactions in a heat map. Twenty participants edited images, annotated human and AI contributions, then reviewed the spatial provenance heat map. Participants credited themselves for directing changes while crediting AI for determining visual details. They judged contribution partly by whether their interactions achieved their intentions. Reviewing provenance helped participants remember interactions without always changing whom they credited. These findings motivate provenance systems that preserve enough context for creators to explain how interactions shaped their work, alongside the record of what happened.
Ziyi Wang, Yuxuan Lu, Wenbo Li, Amirali Amini Northeastern University and Sharif University of Technology, Bo Sun Northeastern University, Yakov Bart Northeastern University, Weimin Lyu Amazon, Jiri Gesi Amazon Science 等 16 位作者
Can Large Language models (LLMs) accurately simulate the next web action of a specific user? While LLMs have shown promising capabilities in generating believable human behaviors, evaluating their ability to mimic real user behaviors remains an open challenge, largely due to the lack of high-quality, publicly available datasets that capture both the observable actions and the internal reasoning of an actual human user. To address this gap, we introduce OPeRA, a novel dataset of Observation, Persona, Rationale, and Action collected from real human participants during online shopping sessions. OPeRA is the first public dataset that comprehensively captures: user personas, browser observations, fine-grained web actions, and self-reported just-in-time rationales. We developed both an online questionnaire and a custom browser plugin to gather this dataset with high fidelity. Using OPeRA, we establish the first benchmark to evaluate how well current LLMs can predict a specific user’s next action and rationale with a given persona and history. This dataset lays the groundwork for future research into LLM agents that aim to act as personalized digital twins for human.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表