PROPER Agents: Proactivity Driven Personalized Agents for Advancing Knowledge Gap Navigation

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 (Response Generating Agent) integrates both explicit and implicit dimensions selectively to produce personalized, context-aware, and proactively informative responses. We evaluate PROPER across multiple domains using a structured, gap-aware rubric that measures coverage, initiative appropriateness, and intent alignment. PROPER improves on quality scores and win rates across all domains, achieving up to 84% gains in single-turn evaluation and consistent dominance in multi-turn interactions. All code for PROPER is available at: https://github.com/i-kiran/ProPer-Agent.