Learning Next Action Predictors from Human-Computer Interaction
Truly proactive AI systems must anticipate what we will do next. This foresight demands far richer information than the sparse signals we type into our prompts -- it demands reasoning over the entire context of what we see and do. We formalize this as next action prediction (NAP): given a sequence of a user's multimodal interactions with a computer (screenshots, clicks, sensor data), predict that user's next action. Progress on this task requires both new data and modeling approaches. To scale data, we annotate longitudinal, naturalistic computer use with vision-language models. We release an open-source pipeline for performing this labeling on private infrastructure, and label over 360K ...
| Authors | Omar Shaikh, Valentin Teutschbein, Kanishk Gandhi et al. |
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| Primary category | cs.CL |
| Published | 6 March 2026 |
| Last updated at source | 6 March 2026 |
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