cs.AIMar 23, 2026
SaveShaping Scientific Explanations to Expert Perspectives with Persona-Conditioned Reinforcement Learning
Organizations: LASIGE, Faculdade de Ciências da Universidade de Lisboa, Portugal.
Abstract
Explainable AI is increasingly important to scientific discovery. However, existing methods largely ignore that explanation quality is not universal: experts differ in how they assess evidence, prioritize mechanisms, and construct explanatory narratives. We introduce perspective-conditioned explanations, a framework for adapting explanation generation to epistemic variation in expert judgment. Using knowledge graph reasoning paths in drug discovery, we show that preferences organize into coherent epistemic perspectives that can be captured by agentic personas, representations of how experts evaluate explanations. Persona-aligned rewards then guide reinforcement learning-based explanation generation without large-scale expert supervision. Expert user studies show that perspective-conditioned explanations are preferred over general-purpose explanations and improve perceived relevance and validity. Moreover, they match or exceed state-of-the-art predictive performance and reduce expert feedback time by two orders of magnitude. Together, these findings demonstrate that explanation quality is perspective-dependent and that modeling this variation enables scalable and human-aligned explanation generation for scientific discovery.
Figures & tables
Figure 1: General-purpose and perspective explanation examples for Fenofibrate treats Coronary Artery Disease . (a) The general-purpose explanation (generated by REx) includes peripheral paths such as side effects and loosely related drug classes. (b) The perspective explanation, conditioned on a persona that prioritizes mechanistic directness, selects paths that focus on the core therapeutic mechanism and relevant biological targets.
Figure 2: Overview of the perspective explainability approach. Phase I creates agentic personas by embedding expert qualitative feedback, clustering responses to identify shared evaluative patterns, and synthesizing structured persona narratives via LLM summarization. Phase II integrates these personas into the reinforcement learning loop: as the RL agent generates candidate explanation paths over the knowledge graph, each persona scores them according to its epistemic perspective, producing a persona-conditioned reward signal that guides the agent toward explanations aligned with distinct expert reasoning strategies.
Figure 3: Persona discovery: quantitative evidence. a,b , Persona creation ratings by system and task (means SD on 1–5 scale), showing high variance reflecting interpretive diversity. c , Mean pairwise Spearman correlations ( ) between expert ratings and between expert and persona ratings across all drug repurposing and drug-target interaction tasks; all . d , Clustering of expert feedback embeddings projected via PCA, comparing K-Means, Agglomerative, and HDBSCAN algorithms. K-Means and Agglomerative converge on an identical two-cluster solution, while HDBSCAN does not resolve cluster structure.
Figure 4: Agentic persona profiles and assignment. a , Narrative profile and evidence summary for Elena , who prioritizes mechanistic rigor. b , Narrative profile and evidence summary for Leo , who prioritizes focused clarity. c , Three-question profiling instrument used to assign participants to personas for the persona evaluation study based on discriminative characteristics identified in expert feedback. For each question, the first response option (purple) aligns with Elena’s preferences, while the second option (green) aligns with Leo’s preferences. Response order was randomized to control for ordering effects.
Figure 5: Perspective explanation evaluation. a , Average ratings across evaluation criteria for general-purpose (REx) and persona-conditioned (Leo, Elena) explanations per hypothesis, with REx ratings reported as the shared baseline. b , Spearman correlation between persona ratings and their constituent participants’ ratings for drug repurposing in the persona creation study. c , Spearman correlation between persona ratings and their assigned participants’ ratings for drug repurposing in the persona evaluation study. d , Participant preferences between perspective and general-purpose explanations. e , Estimated feedback collection time under different supervision conditions; persona-conditioned training reduces expert time by two orders of magnitude. f , Link prediction performance on the drug repurposing task; perspective explanations exceed state-of-the-art methods across all metrics. Asterisks in b and c denote significance: , , .
Figure 6: Example explanations for the hypothesis Etidronic acid treats Paget’s disease of bone : a , general-purpose (REx); b , the Elena perspective (mechanistic rigour); c , the Leo perspective (focused clarity).
Appendix figures & tables6 assets
Supplementary material from the paper’s appendix.
Appendix
| Study (n = 11) | |
| Age Range | 25–34: 9; 35–49: 2 |
| Primary Background | Life and Health Sciences: 4 CS/AI-heavy w/ Biomedical Experience: 5 Hybrid Computational Biology: 2 |
| Domain Knowledge | Knowledge Graphs: 4 Experts, 2 Competent, 3 Novice, 2 No Knowledge AI Systems: 1 Expert, 7 Competent, 1 Novice, 2 No Knowledge Molecular Biology: 2 Experts, 8 Competent, 1 No Knowledge |
Table 1: Persona creation study demographics, expertise, and domain knowledge ( ).
Figure 7: Clustering validation. a , Candidate solutions ( – ): Silhouette Score, Inertia, Davies–Bouldin Index, Calinski–Harabasz Index, Gaussian mixture model selection (BIC and AIC), and bootstrap cluster stability (Jaccard index, 200 resamples). Orange rings indicate the best for each metric. b , Subcluster analysis within the majority cluster (Elena, ): K-Means split ( , silhouette = 0.13), HDBSCAN (3 micro-clusters, 6 noise points), and per-sample silhouette coefficients for the base solution.
| Category | Distribution | |
| Age Range | 20–24: 2; 25–29: 12; 30–34: 4; 40–44: 3; 45–54: 1 | |
| Persona Assigned | Leo: 12; Elena: 10 | |
| Primary Background | Life and Health Sciences: 15 (68.2%) CS/AI-heavy with Biomedical Experience: 3 (13.6%) Hybrid Computational Biology: 4 (18.2%) | |
| Main Expertise Area | Biochemistry: 2 | Animal Cell Technology: 1 |
| Microbiology: 2 | Data Analytics: 1 | |
| Computer Science: 2 | Biochemistry and Biotechnology: 1 | |
Table 2: Persona evaluation study participant demographics, background categories, main expertise areas, and domain knowledge ( ).
| Contrast | SE | 95% CI | ||
| Relevance | 0.082 | |||
| Completeness † | 0.129 | |||
| Validity | 0.085 | |||
| Overall | 0.059 | |||
| Elena users | 0.081 | |||
| Leo users | 0.084 |
Table 3: Linear mixed-effects model of explanation ratings ( ; 1,320 observations) with crossed random intercepts for participant and hypothesis, fit by REML. Coefficients give the perspective-minus-general difference on the 1–5 scale.
| Comparison | Pairs | Persona paths/pair | REx paths/pair | Path length (hops) | Pairs sharing no path |
| Elena vs REx | 46 | 1.83 | 2.11 | 3.0 | 65% |
| Leo vs REx | 44 | 1.66 | 2.16 | 3.0 | 50% |
Table 4: Path properties of general-purpose (REx) and persona-conditioned explanations on the drug repurposing task.
| Question | Elena | Leo |
| Q1: Mechanistic Detail | “A detailed step-by-step explanation” Supporting statements: - “combine therapeutic, genetic, and adverse-effect details for depth” - “combine multiple mechanistic or genetic details to reinforce direct drug-disease links” | “A streamlined explanation with fewer steps” Supporting statements: - “offer multiple paths for completeness while limiting their number to avoid excessive complexity” - “concise explanations that link drugs by shared treatment lines” |
| Q2: Explanation Complexity | “I do not need high-level concepts, I am happy with seeing only a detailed explanation” Supporting statements: - “explicitly state direct, clinically established drug-disease links” - “avoid generic class or side-effect links, deliver a clear therapeutic rationale” | “I prefer to see high-level concepts that frame why things belong together” Supporting statements: - “use ontological expansion to clarify connections between claims” - “leverage ontology for non-obvious or indirect relations” |
| Q3: Decision-making Context | “Explanations should be based on specific relations such as ‘inhibits’ or ‘binds’ ” Supporting statements: - “prefer ‘binds’ to ‘causes’ for drug-target interactions” - “eliminate vague predicates like ‘causes,’ ‘includes,’ or ‘participates’ ” | “Explanations can also include less specific relations such as ‘treats’ or ‘causes’ ” Supporting statements: - “relations…already covered by ‘causes’ or ‘treats’ ” (accepting them) - “ontological expansion specifically to indirect relations” |
Table 5: Mapping of profiling questions to persona characteristics based on expert feedback analysis.
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