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explainable ai

2,280 papers

#generative ai Open access Sep 2026

Artificial Intelligence-Assisted Herbal Formulation Development: From Phytochemical Intelligence to Predictive Nanodelivery and Precision Phytotherapy

Herbal medicines constitute a chemically diverse source of bioactive molecules and remain important components of traditional and complementary healthcare systems. However, the development of reproducible pharmaceutical formulations from herbal materials is complicated by variability in botanical identity, geographical...

Alisha Jabi1*, Deepika Shakya2, Priti Yadav3 · 0 citations
#explainable ai Open access Sep 2026

From Movement to Mechanism: Understanding a neural network through fly leg movements

From Movement to Mechanism — version 0.4 How can a visible leg movement help us understand a neural network? This illustrated, English-language computational preprint follows two related experiments based on maleCNS, the male fruit-fly central nervous system wiring map. The main text explains the research in accessible...

Tomáš Kovářík · 0 citations
#explainable ai Dataset Open access Sep 2026

P. G. Lejeune Dirichlet: Werke Band II Source-Witnessed Working Drafts and English Translations

This collection preserves P. G. Lejeune Dirichlet’s mathematical works as original-language working transcriptions, English translations, editable LaTeX, source scans and checking records. Its established core is Werke Band II Papers I–XLI, including the cumulative readers, component sources, correspondence, notices an...

P. G. Lejeune Dirichlet · 0 citations
#explainable ai Open access Sep 2026

Why the User Rages: A User-Centered Study on Conversational AI Models' Defensive Communication Behaviors and Their Effects

On August 8 (UTC+8), 2025, OpenAI rolled out GPT-5 to the public, and simultaneously removed access to all “legacy models” including GPT-4o. This action led to a global movement to “bring back GPT-4o”. The protest against “cold and detached” GPT-5 revealed users’ acute sensitivity to anthropomorphic AI responses: users...

X. D. Yu · 0 citations
#explainable ai Open access Sep 2026

Explainable AI for sentiment analysis of human metapneumovirus (HMPV) using XLNet

The outbreak of Human Metapneumovirus (HMPV) in China, which later spread to the UK and other countries, raised significant public concern due to its potential impact on vulnerable populations. While HMPV typically causes mild symptoms, its effects on the elderly and immunocompromised individuals prompted health author...

Md. Shahriar Hossain Apu, Md. Saiful Islam, Tanjim Taharat Aurpa et al. · 1 citation
#explainable ai Open access Sep 2026

AnCiR: Analysis of Chronobiological Rhythms

AnCiR is a graphical, no-coding tool for the analysis of chronobiological rhythms and other time-series data. It provides periodicity detection (Sokolove–Bushell χ2, Enright, Lomb–Scargle, FFT), cosinor and harmonic-cosinor fitting, non-parametric circadian rhythm analysis (IS, IV, RA, M10, L5), circular statistics, co...

DaveCumin · 2 citations
#large language models Review Open access Sep 2026

Judgment Cost Theory, Study 2 — Replication and Audit Archive: When Automation Concentrates Judgment: A Systematic Review and Meta-Analysis of Residual Decision Difficulty and Oversight Burden Following AI Adoption

Version 2.3 (21 September 2026). No content changed between 2.2 and 2.3. Every file, hash and result is identical. This version exists only to correct the file list: version 2.2 was published carrying both the superseded v2.1 archive and the current one, because Zenodo copies prior files into a new-version draft by def...

West Jason · 0 citations
#natural language process... Preprint Sep 2026

Toward a Unified Mathematics of Concepts

An operation-based view that evaluates mathematical frameworks by the conceptual operations they support is proposed, identifying thirteen operations (including similarity, composition, generalization, and grounding) that recur across cognition, psychology, and AI.

Sha-Ni Chen · 0 citations
#generative ai Review Sep 2026

Decision Support in Publicly Available Patient Information Policies at U.S. Osteopathic Medical Schools: A Vignette-Based Document Analysis

Research Objectives: To evaluate whether publicly available institutional guidance supports patient-information decisions across scenarios and schools, and characterize document synthesis and evaluation consistency. Methods: We conducted an exploratory, vignette-based document analysis of a geographically diverse nonpr...

N. Dai, K. Waarala · 0 citations
#explainable ai Open access Sep 2026

The Moral Agency Transition

Public debate describes AI agents as lying, cheating, and coordinating. Those descriptions track real hazards, but they import a human moral psychology into systems whose behaviour is better explained by optimisation, scaffolding, and institutional context. This paper develops the alternative without minimising the dan...

Armando Vieira · 0 citations
#explainable ai Open access Sep 2026

THE DESIRE LAW LEDGER E-08: DesireLaw × Selection × Realization

THE DESIRE LAW LEDGER E-08: DesireLaw × Selection × Realization THE DESIRE LAW LEDGER is the eighth major volume in the E-Series Triple-Coupling System, built around the core structure: Λ_desirelaw × Λ_selection × Λ_realization DesireLaw × Selection × Realization At the center of this book is a question almost everyone...

政恩 馮 · 0 citations

From tech blogs

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Microsoft Research Blog Oct 7, 2026

Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses

Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.

Microsoft Research Blog Oct 6, 2026

What AI gets wrong and what failure teaches us

Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity.  The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.

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