Knowing Isn't Understanding: Re-grounding Generative Proactivity with Epistemic and Behavioral Insight
arXiv:2602.15259v2 Announce Type: replace-cross Abstract: Generative AI agents equate understanding with resolving explicit queries, an assumption that confines interaction to what users can articulate. This assumption breaks down when users themselves lack awareness of what is missing, risky, or worth considering. In such conditions, proactivity is not merely an efficiency enhancement, but an epistemic necessity. We refer to this condition as epistemic incompleteness: where progress depends on engaging with unknown unknowns for effective partnership. Existing approaches to proactivity remain
This paper addresses a critical limitation of current generative AI as it becomes more integrated into complex workflows, highlighting a fundamental issue with its 'understanding' of user needs beyond explicit queries.
A strategic reader should care because this research points towards the next evolutionary step for AI agents, moving them from reactive tools to proactive partners, essential for collapsing white-collar workflows effectively.
AI systems will evolve from merely answering explicit questions to anticipating user needs and identifying unknown unknowns, fundamentally changing how humans interact with and rely on artificial intelligence.
- · AI Agent Developers
- · Knowledge Workers
- · Software-as-a-Service Providers
- · Enterprises Adopting Advanced AI
- · Companies with Reactive AI Systems
- · Manual Workflow Providers
Generative AI models will be redesigned to incorporate epistemic and behavioral insights, leading to more sophisticated and autonomous agents.
This enhanced proactivity will accelerate the automation of complex decision-making processes currently requiring human intuition.
The development of truly proactive AI could lead to new ethical and regulatory challenges regarding agency, responsibility, and systemic unknown unknowns.
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Read at arXiv cs.LG