TL;DR
PROPER is a framework that models user knowledge gaps explicitly to generate personalized, proactive assistance, improving response quality and interaction success across multiple domains.
Contribution
It introduces a structured approach with explicit and implicit dimensions to enhance proactive, personalized user assistance beyond existing methods.
Findings
PROPER achieves up to 84% gains in single-turn evaluations.
It outperforms baselines in coverage, initiative, and intent alignment.
Code is publicly available at https://github.com/i-kiran/ProPer-Agent.
Abstract
Current approaches to proactive assistance move beyond the ask-and-respond paradigm by anticipating user needs. In practice, they either burden users with clarifying questions or rely on context-based extrapolation, often leading to unnecessary or mistimed interventions. Such systems lack explicit mechanisms to model users' knowledge gaps, resulting in incomplete or suboptimal task outcomes. To address this, we propose PROPER, a framework that explicitly models user-specific knowledge gaps in a controlled manner. Central to our approach is the notion of dimensions: structured, task-relevant factors that define the considerations required for effective task completion. Given a user query, the DGA (Dimension Generating Agent) identifies explicit dimensions (from the user's query) and generates a set of candidate implicit dimensions capturing unarticulated aspects of the task. The RGA…
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