{"ID":2849456,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.22998","arxiv_id":"2510.22998","title":"ProfileXAI: User-Adaptive Explainable AI","abstract":"ProfileXAI is a model- and domain-agnostic framework that couples post-hoc explainers (SHAP, LIME, Anchor) with retrieval - augmented LLMs to produce explanations for different types of users. The system indexes a multimodal knowledge base, selects an explainer per instance via quantitative criteria, and generates grounded narratives with chat-enabled prompting. On Heart Disease and Thyroid Cancer datasets, we evaluate fidelity, robustness, parsimony, token use, and perceived quality. No explainer dominates: LIME achieves the best fidelity-robustness trade-off (Infidelity $\\le 0.30$, $L\u003c0.7$ on Heart Disease); Anchor yields the sparsest, low-token rules; SHAP attains the highest satisfaction ($\\bar{x}=4.1$). Profile conditioning stabilizes tokens ($σ\\le 13\\%$) and maintains positive ratings across profiles ($\\bar{x}\\ge 3.7$, with domain experts at $3.77$), enabling efficient and trustworthy explanations.","short_abstract":"ProfileXAI is a model- and domain-agnostic framework that couples post-hoc explainers (SHAP, LIME, Anchor) with retrieval - augmented LLMs to produce explanations for different types of users. The system indexes a multimodal knowledge base, selects an explainer per instance via quantitative criteria, and generates grou...","url_abs":"https://arxiv.org/abs/2510.22998","url_pdf":"https://arxiv.org/pdf/2510.22998v1","authors":"[\"Gilber A. Corrales\",\"Carlos Andrés Ferro Sánchez\",\"Reinel Tabares-Soto\",\"Jesús Alfonso López Sotelo\",\"Gonzalo A. Ruz\",\"Johan Sebastian Piña Durán\"]","published":"2025-10-27T04:34:02Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Large Language Model\"]","has_code":false}