{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T12:08:10Z","timestamp":1779192490703,"version":"3.51.4"},"reference-count":46,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T00:00:00Z","timestamp":1777248000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100014440","name":"Espa\u00f1a Ministerio de Ciencia Innovaci\u00f3n y Universidades","doi-asserted-by":"publisher","award":["PID2023-150070NB-I00"],"award-info":[{"award-number":["PID2023-150070NB-I00"]}],"id":[{"id":"10.13039\/100014440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Fusion"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.inffus.2026.104389","type":"journal-article","created":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T23:27:02Z","timestamp":1776468422000},"page":"104389","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["TAXAL framework: Triadic fusion of cognitive, functional, and causal dimensions for explainability in agentic LLMs"],"prefix":"10.1016","volume":"134","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2604-2078","authenticated-orcid":false,"given":"David","family":"Herrera-Poyatos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7976-3139","authenticated-orcid":false,"given":"Carlos","family":"Pel\u00e1ez-Gonz\u00e1lez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cristina","family":"Zuheros","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Virilo","family":"Tejedor","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rosana","family":"Montes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francisco","family":"Herrera","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"9","key":"10.1016\/j.inffus.2026.104389_bib0001","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s10462-024-10896-y","article-title":"Towards trustworthy LLMs: a review on debiasing and dehallucinating in large language models","volume":"57","author":"Lin","year":"2024","journal-title":"Artif. Intell. Rev."},{"issue":"6","key":"10.1016\/j.inffus.2026.104389_bib0002","doi-asserted-by":"crossref","DOI":"10.1007\/s11704-024-40231-1","article-title":"A survey on large language model based autonomous agents","volume":"18","author":"Wang","year":"2024","journal-title":"Front. Comput. Sci."},{"issue":"4","key":"10.1016\/j.inffus.2026.104389_bib0003","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.1007\/s43681-023-00332-2","article-title":"Against the opacity, and for a qualitative understanding, of artificially intelligent technologies","volume":"4","author":"Khalili","year":"2024","journal-title":"AI Ethics"},{"key":"10.1016\/j.inffus.2026.104389_bib0004","unstructured":"F. Herrera, Making sense of the unsensible: reflection, survey, and challenges for XAI in large language models toward human-centered AI, (2025)."},{"key":"10.1016\/j.inffus.2026.104389_bib0005","unstructured":"H. Heyen, A. Widdicombe, N.Y. Siegel, M. Perez-Ortiz, P. Treleaven, The effect of model size on LLM post-hoc explainability via lime, (2024)."},{"key":"10.1016\/j.inffus.2026.104389_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103133","article-title":"Reflections and attentiveness on eXplainable artificial intelligence (XAI). the journey ahead from criticisms to human-AI collaboration","volume":"121","author":"Herrera","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.inffus.2026.104389_bib0007","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI","volume":"58","author":"Arrieta","year":"2020","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.inffus.2026.104389_bib0008","unstructured":"E. Cambria, L. Malandri, F. Mercorio, N. Nobani, A. Seveso, XAI meets llms: a survey of the relation between explainable AI and large language models, (2024)."},{"key":"10.1016\/j.inffus.2026.104389_bib0009","article-title":"Unveiling the black box: the significance of XAI in making LLMs transparent","author":"Carvalho Souza","year":"2025","journal-title":"Authorea Preprints"},{"key":"10.1016\/j.inffus.2026.104389_bib0010","unstructured":"F. Mumuni, A. Mumuni, Explainable artificial intelligence (XAI): from inherent explainability to large language models, (2025)."},{"key":"10.1016\/j.inffus.2026.104389_bib0011","unstructured":"F. Liu, Y. Feng, Z. Xu, L. Su, X. Ma, D. Yin, H. Liu, Jailjudge: a comprehensive jailbreak judge benchmark with multi-agent enhanced explanation evaluation framework, (2024)."},{"key":"10.1016\/j.inffus.2026.104389_bib0012","article-title":"An overview of model uncertainty and variability in LLM-based sentiment analysis: challenges, mitigation strategies, and the role of explainability","volume":"Volume 8 - 2025","author":"Herrera-Poyatos","year":"2025","journal-title":"Front. Artif. Intell."},{"issue":"2","key":"10.1016\/j.inffus.2026.104389_bib0013","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3639372","article-title":"Explainability for large language models: a survey","volume":"15","author":"Zhao","year":"2024","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"10.1016\/j.inffus.2026.104389_bib0014","series-title":"Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 5: Tutorial Abstracts)","first-page":"19","article-title":"Explanation in the era of large language models","author":"Zhu","year":"2024"},{"key":"10.1016\/j.inffus.2026.104389_bib0015","unstructured":"X. Shui, Z. Ru, Bridging the gap between explainability and large language models, Hal-05011844 (2025)."},{"key":"10.1016\/j.inffus.2026.104389_bib0016","unstructured":"S. Chaduvula, J. Ho, K. Kim, A. Narayanan, M. Alinoori, M. Garg, D. Ramachandram, S. Raza, From features to actions: explainability in traditional and agentic AI systems, (2026)."},{"key":"10.1016\/j.inffus.2026.104389_bib0017","unstructured":"A. Mittal, R. Arike, C2-Faith: benchmarking LLM judges for causal and coverage faithfulness in chain-of-thought reasoning, (2026)."},{"key":"10.1016\/j.inffus.2026.104389_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.techsoc.2026.103302","article-title":"Opacity as a feature, not a flaw: Role-sensitive explainability, institutional trust, and the LoBOX ethics governance framework for AI","author":"Herrera","year":"2026","journal-title":"Technol. Soc."},{"key":"10.1016\/j.inffus.2026.104389_bib0019","unstructured":"R. Luss, E. Miehling, A. Dhurandhar, Cell your model: contrastive explanations for large language models, (2025)."},{"key":"10.1016\/j.inffus.2026.104389_bib0020","doi-asserted-by":"crossref","unstructured":"M.A. Mersha, M.G. Yigezu, S. Byun, J. Kalita, et al., A unified framework with novel metrics for evaluating the effectiveness of XAI techniques in LLMs, (2025).","DOI":"10.1016\/j.knosys.2025.113042"},{"issue":"3","key":"10.1016\/j.inffus.2026.104389_bib0021","doi-asserted-by":"crossref","first-page":"69","DOI":"10.3390\/make8030069","article-title":"Co-explainers: a position on interactive XAI for human\u2013AI collaboration as a harm-mitigation infrastructure","volume":"8","author":"Herrera","year":"2026","journal-title":"Mach. Learn. Knowl. Extrac."},{"key":"10.1016\/j.inffus.2026.104389_bib0022","series-title":"Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems","first-page":"1","article-title":"On selective, mutable and dialogic XAI: a review of what users say about different types of interactive explanations","author":"Bertrand","year":"2023"},{"key":"10.1016\/j.inffus.2026.104389_bib0023","series-title":"Proceedings of the 30th International Conference on Intelligent User Interfaces","first-page":"907","article-title":"Is conversational XAI all you need? human-AI decision making with a conversational XAI assistant","author":"He","year":"2025"},{"key":"10.1016\/j.inffus.2026.104389_bib0024","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.artint.2018.07.007","article-title":"Explanation in artificial intelligence: insights from the social sciences","volume":"267","author":"Miller","year":"2019","journal-title":"Artif. Intell."},{"key":"10.1016\/j.inffus.2026.104389_bib0025","unstructured":"P. Mavrepis, G. Makridis, G. Fatouros, V. Koukos, M.M. Separdani, D. Kyriazis, XAI for all: can large language models simplify explainable AI?, (2024)."},{"issue":"4","key":"10.1016\/j.inffus.2026.104389_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2024.103732","article-title":"The rationality of explanation or human capacity? understanding the impact of explainable artificial intelligence on human-AI trust and decision performance","volume":"61","author":"Wang","year":"2024","journal-title":"Inf. Process. Manag."},{"key":"10.1016\/j.inffus.2026.104389_bib0027","unstructured":"X. Wu, H. Zhao, Y. Zhu, Y. Shi, F. Yang, T. Liu, X. Zhai, W. Yao, J. Li, M. Du, et al., Usable XAI: 10 strategies towards exploiting explainability in the LLM era, (2024)."},{"key":"10.1016\/j.inffus.2026.104389_bib0028","unstructured":"J. Schneider, Generative to agentic AI: survey, conceptualization, and challenges, (2025)."},{"key":"10.1016\/j.inffus.2026.104389_bib0029","series-title":"2025 1st International Conference on Artificial Intelligence and Computing","article-title":"JAILJUDGE: a comprehensive JAILBREAK judge benchmark with multi-agent enhanced explaination evaluation framework","author":"Liu","year":"2025"},{"issue":"14","key":"10.1016\/j.inffus.2026.104389_bib0030","doi-asserted-by":"crossref","DOI":"10.3390\/app11146421","article-title":"What disease does this patient have? a large-scale open domain question answering dataset from medical exams","volume":"11","author":"Jin","year":"2021","journal-title":"Appl. Sci."},{"key":"10.1016\/j.inffus.2026.104389_bib0031","series-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems","article-title":"Judging LLM-as-a-judge with MT-bench and chatbot arena","author":"Zheng","year":"2023"},{"key":"10.1016\/j.inffus.2026.104389_bib0032","series-title":"Technical Report","article-title":"Derivation of New Readability Formulas (Automated Readability Index, Fog Count and Flesch Reading Ease formula) for Navy Enlisted Personnel","author":"Kincaid","year":"1975"},{"key":"10.1016\/j.inffus.2026.104389_bib0033","series-title":"Proceedings of the 18th BioNLP Workshop and Shared Task","first-page":"319","article-title":"Scispacy: fast and robust models for biomedical natural language processing","author":"Neumann","year":"2019"},{"issue":"suppl_1","key":"10.1016\/j.inffus.2026.104389_bib0034","doi-asserted-by":"crossref","first-page":"D267","DOI":"10.1093\/nar\/gkh061","article-title":"The unified medical language system (UMLS): integrating biomedical terminology","volume":"32","author":"Bodenreider","year":"2004","journal-title":"Nucleic Acids Res."},{"issue":"3","key":"10.1016\/j.inffus.2026.104389_bib0035","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.pec.2014.05.027","article-title":"Development of the patient education materials assessment tool (PEMAT): a new measure of understandability and actionability for print and audiovisual patient information","volume":"96","author":"Shoemaker","year":"2014","journal-title":"Patient Educ. Couns."},{"issue":"3","key":"10.1016\/j.inffus.2026.104389_bib0036","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/S0271-5309(01)00005-2","article-title":"The linguistic assumptions underlying readability formulae: a critique","volume":"21","author":"Bailin","year":"2001","journal-title":"Lang. commun."},{"key":"10.1016\/j.inffus.2026.104389_bib0037","series-title":"Proceedings of the Third Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)","first-page":"205","article-title":"Flesch or fumble? evaluating readability standard alignment of instruction-tuned language models","author":"Imperial","year":"2023"},{"key":"10.1016\/j.inffus.2026.104389_bib0038","unstructured":"K. Gruteke Klein, S. Frenkel, O. Shubi, Y. Berzak, Surprisal takes it all: eye tracking based cognitive evaluation of text readability measures, (2025)."},{"key":"10.1016\/j.inffus.2026.104389_bib0039","series-title":"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing","first-page":"2511","article-title":"G-eval: NLG evaluation using gpt-4 with better human alignment","author":"Liu","year":"2023"},{"key":"10.1016\/j.inffus.2026.104389_bib0040","series-title":"The Twelfth International Conference on Learning Representations","article-title":"Prometheus: inducing fine-grained evaluation capability in language models","author":"Kim","year":"2023"},{"key":"10.1016\/j.inffus.2026.104389_bib0041","series-title":"Proceedings of the 41st International Conference on Machine Learning","article-title":"MLLM-as-a-Judge: assessing multimodal LLM-as-a-Judge with vision-language benchmark","author":"Chen","year":"2024"},{"issue":"2","key":"10.1016\/j.inffus.2026.104389_bib0042","doi-asserted-by":"crossref","first-page":"491","DOI":"10.3758\/s13428-022-01802-x","article-title":"A large-scaled corpus for assessing text readability","volume":"55","author":"Crossley","year":"2023","journal-title":"Behav. Res. Methods"},{"issue":"5","key":"10.1016\/j.inffus.2026.104389_bib0043","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1037\/0033-295X.85.5.363","article-title":"Toward a model of text comprehension and production","volume":"85","author":"Kintsch","year":"1978","journal-title":"Psychol. Rev."},{"key":"10.1016\/j.inffus.2026.104389_bib0044","first-page":"24824","article-title":"Chain-of-thought prompting elicits reasoning in large language models","volume":"35","author":"Wei","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"8","key":"10.1016\/j.inffus.2026.104389_bib0045","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1038\/s42256-023-00692-8","article-title":"Explaining machine learning models with interactive natural language conversations using talktomodel","volume":"5","author":"Slack","year":"2023","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.inffus.2026.104389_bib0046","series-title":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","first-page":"2660","article-title":"Editing factual knowledge and explanatory ability of medical large language models","author":"Xu","year":"2024"}],"container-title":["Information Fusion"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S156625352600268X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S156625352600268X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T11:40:17Z","timestamp":1779190817000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S156625352600268X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":46,"alternative-id":["S156625352600268X"],"URL":"https:\/\/doi.org\/10.1016\/j.inffus.2026.104389","relation":{},"ISSN":["1566-2535"],"issn-type":[{"value":"1566-2535","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"TAXAL framework: Triadic fusion of cognitive, functional, and causal dimensions for explainability in agentic LLMs","name":"articletitle","label":"Article Title"},{"value":"Information Fusion","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.inffus.2026.104389","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"104389"}}