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.
Abstract (translated)
AI解释方法通常预设静态用户模型，无论专家目标、推理策略或决策情境如何，均生成非自适应解释。基于知识图谱的解释方法尽管具备基于路径的具身推理能力，却也继承了这一局限。在科学发现等复杂领域中，该假设无法捕捉专家间认知策略与认知立场的多样性，导致解释无法促进深度理解与知情决策。然而，人类专家稀缺，限制了利用直接人类反馈生成自适应解释的可能。我们提出一种用于科学解释生成的强化学习方法，通过引入主体性角色——即对专家推理策略的结构化表征——引导解释智能体趋向特定认知偏好。在针对药物发现的知识图谱解释评估中，我们测试了两种源自专家反馈的不同认知立场角色。结果表明，角色驱动的解释在匹配最先进预测性能的同时，其角色偏好与对应专家的偏好高度一致。自适应解释持续受到青睐（n=22），且基于角色的训练将反馈需求降低了两个数量级。这些发现证明了主体性角色如何使AI系统在复杂高风险领域中实现可扩展的自适应可解释性。
URL
https://arxiv.org/abs/2603.21846
