Proactive Assistance
Overview
Proactive assistance examines how AI systems infer a person’s goals and context, then choose whether, when, and how to intervene rather than simply responding to requests. Approaches include multimodal perception, task and goal graphs, memory of user routines, and learned ask-or-act policies, applied to settings such as GUI use, procedural work, conversation, and human-robot collaboration. Benchmarks increasingly test long-horizon tasks, hidden preferences, intervention timing, and recovery, but current agents often struggle when intent is vague or useful information must be gathered across interactions. A central challenge is balancing useful initiative against interruption, misjudgment, and loss of user control, which makes real-world and closed-loop evaluation important.
Latest papers
46 papers6 papers in the last four weeks, down 40% on the four weeks before. 0.0% of all new papers.