Oct 2026· Zenodo (CERN European Organization for Nuclear Research)
Islamic Thought and Society Studies
Abstract
This working paper examines whether the classical methodology of ʿilal al-Ḥadīth—the detection of hidden defects in apparently sound reports—can provide a useful conceptual framework for evaluating artificial-intelligence outputs in specialist knowledge environments. It develops the concept of the “epistemic hidden defect”: a weakness in provenance, attribution, inference, context, evidential hierarchy, or scholarly status that may remain concealed beneath a fluent and apparently authoritative AI-generated response. Drawing on classical Hadith-critical methodology and contemporary AI research, the paper distinguishes computational capability from scholarly authority. It develops the concepts of the Authority Ceiling, Digital Isnād, competence-specific human oversight, and the No-Autonomous-Grading Principle as safeguards for AI-assisted scholarship. The paper argues for an AI-assisted rather than AI-autonomous model of specialist research, in which computational systems widen the evidential field while consequential scholarly judgement remains traceable, reviewable, and accountable.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9
