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    <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 multiturn interactions. All code for PROPER is available at: https://github.com/i-kiran/ProPer-Agent.</abstract>
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%0 Conference Proceedings
%T PROPER Agents: Proactivity Driven Personalized Agents for Advancing Knowledge Gap Navigation
%A Kaur, Kirandeep
%A Gupta, Vinayak
%A Gupta, Aditya
%A Shah, Chirag
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F kaur-etal-2026-proper
%X 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 multiturn interactions. All code for PROPER is available at: https://github.com/i-kiran/ProPer-Agent.
%U https://aclanthology.org/2026.findings-acl.2082/
%P 41951-41975

Markdown (Informal)

[PROPER Agents: Proactivity Driven Personalized Agents for Advancing Knowledge Gap Navigation](https://aclanthology.org/2026.findings-acl.2082/) (Kaur et al., Findings 2026)

• PROPER Agents: Proactivity Driven Personalized Agents for Advancing Knowledge Gap Navigation (Kaur et al., Findings 2026)

ACL

• Kirandeep Kaur, Vinayak Gupta, Aditya Gupta, and Chirag Shah. 2026. PROPER Agents: Proactivity Driven Personalized Agents for Advancing Knowledge Gap Navigation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 41951–41975, San Diego, California, United States. Association for Computational Linguistics.