Agentic Personas for Adaptive Scientific Explanations with Knowledge Graphs

2026-03-23

AI Summary

This work addresses the limitation of existing AI explanation methods, which typically rely on static user models and fail to accommodate the diverse cognitive strategies and domain knowledge of scientific experts. To overcome this, we propose a novel framework that integrates knowledge graph path reasoning with reinforcement learning–driven agent-based persona modeling to dynamically generate adaptive scientific explanations tailored to individual expert preferences. Our approach enables scalable personalized explainability without requiring extensive human feedback, substantially reducing reliance on expert-annotated data. Evaluated on a drug discovery task, the method achieves state-of-the-art predictive performance while producing persona-driven explanations that domain experts significantly prefer over non-adaptive baselines, with human feedback requirements reduced by two orders of magnitude.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Knowledge Representation and Reasoning: Preferences
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

AI explanation methods often assume a static user model, producing non-adaptive explanations regardless of expert goals, reasoning strategies, or decision contexts. Knowledge graph-based explanations, despite their capacity for grounded, path-based reasoning, inherit this limitation. In complex domains such as scientific discovery, this assumption fails to capture the diversity of cognitive strategies and epistemic stances among experts, preventing explanations that foster deeper understanding and informed decision-making. However, the scarcity of human experts limits the use of direct human feedback to produce adaptive explanations. We present a reinforcement learning approach for scientific explanation generation that incorporates agentic personas, structured representations of expert reasoning strategies, that guide the explanation agent towards specific epistemic preferences. In an evaluation of knowledge graph-based explanations for drug discovery, we tested two personas that capture distinct epistemic stances derived from expert feedback. Results show that persona-driven explanations match state-of-the-art predictive performance while persona preferences closely align with those of their corresponding experts. Adaptive explanations were consistently preferred over non-adaptive baselines (n = 22), and persona-based training reduces feedback requirements by two orders of magnitude. These findings demonstrate how agentic personas enable scalable adaptive explainability for AI systems in complex and high-stakes domains.

Problem

• adaptive explanation
• knowledge graph
• agentic personas
• scientific discovery
• explainable AI

Innovation

• agentic personas
• adaptive explanation
• knowledge graphs
• reinforcement learning
• scientific discovery