# PersoPilot: An Adaptive AI-Copilot for Transparent Contextualized Persona Classification and Personalized Response Generation

arXiv: https://arxiv.org/abs/2602.04540 · HTML: https://mlsanity.com/paper/2602.04540/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 8 references that are in the map.
- First posted on arXiv 4 Feb 2026.

## Builds on (most cited first, 6 of 6)

- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profiles](https://mlsanity.com/paper/2108.09355/) (2021)
- [PersoBench: Benchmarking Personalized Response Generation in Large Language Models](https://mlsanity.com/paper/2410.03198/) (2024)
- [Towards Explainable Conversational Recommender Systems](https://mlsanity.com/paper/2305.18363/) (2023)
- [Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy](https://mlsanity.com/paper/2505.21907/) (2025)
- [ExBigBang: A Dynamic Approach for Explainable Persona Classification through Contextualized Hybrid Transformer Analysis](https://mlsanity.com/paper/2508.15364/) (2025)

## Built on by (most cited first, 1 of 1)

- [SETU: An Agentic Ecosystem for Multilingual, Persona-Aware Communication Coaching](https://mlsanity.com/paper/2608.27524/) (2026)

## Abstract (arXiv)

Understanding and classifying user personas is critical for delivering effective personalization. While persona information offers valuable insights, its full potential is realized only when contextualized, linking user characteristics with situational context to enable more precise and meaningful service provision. Existing systems often treat persona and context as separate inputs, limiting their ability to generate nuanced, adaptive interactions. To address this gap, we present PersoPilot, an agentic AI-Copilot that integrates persona understanding with contextual analysis to support both end users and analysts. End users interact through a transparent, explainable chat interface, where they can express preferences in natural language, request recommendations, and receive information tailored to their immediate task. On the analyst side, PersoPilot delivers a transparent, reasoning-powered labeling assistant, integrated with an active learning-driven classification process that adapts over time with new labeled data. This feedback loop enables targeted service recommendations and adaptive personalization, bridging the gap between raw persona data and actionable, context-aware insights. As an adaptable framework, PersoPilot is applicable to a broad range of service personalization scenarios.