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explainable ai

2,183 papers

#reinforcement learning Review Sep 2026

Innovation in Energy Transition: Lessons from Digital Twin and Industry 4.0 Deployments

Digital twin (DT) technologies, tightly coupled with Industry 4.0 methodologies, are emerging as fundamental enablers for innovation and optimization in renewable and low-carbon energy systems. Unlike traditional offline simulation, DTs support a live, bidirectional connection to physical assets and networks, enablin...

Mahmoud AlGaiar, M. Y. Alklih · 0 citations
#generative ai Open access Sep 2026

Generative Resonance as a Macrodynamic Theory of Growth and Stagnation ー Population, Productivity, Participation, and the Endogenous Cooling of Economic Development

Short Description This paper develops Generative Resonance Theory as a macrodynamic explanation of growth and stagnation. It introduces generative resonance intensity, (G=\Phi R), as a state variable describing a society's capacity to connect unrealized possibilities with relational participation. The framework explain...

Kazunori Ohumi · 0 citations
#generative ai Sep 2026

Agentic AI for Autonomous Fault Diagnosis and Root Cause Analysis in Industrial Maintenance

Results demonstrate that the agent successfully identifies multiple concurrent root causes, produces actionable maintenance steps, and generalizes to synthetic failures, establishing a scalable, explainable framework for intelligent industrial maintenance.

Sarafudheen M. Tharayil, Dalia A. Albuqytah, Fuad Shalaan et al. · 0 citations
#explainable ai Open access Sep 2026

Topological and Geometric Native Coding for Higher Dimensional Programming

Abstract This record contains the comprehensive monograph, "Topological and Geometric Native Coding for Higher Dimensional Programming" by Elias Oulad Brahim (September 2026). The research introduces ToposLang and the novel paradigm of Geometric Native Coding (GNC), which challenges the structural limitations of the cl...

Elias Oulad Brahim · 0 citations
#explainable ai Open access Sep 2026

Condensation Happens Without Any Attraction: What Decides Is the Wavelength Overtaking the Spacing ── the condensation temperature of an ideal Bose gas contains only constants, the mass and the number density, with no interparticle potential at all ── the separator is whether switching off the interaction makes it disappear ── [Paper 663]

It is natural to think that a phase changes because particles attract one another. But the condensation of bosons happens without any attraction at all. The condensation temperature of an ideal Bose gas is computed in three cases, and what fixes that temperature is examined. No new theorem or law is claimed. Scope of t...

Yuuki Yamagishi · 0 citations
#explainable ai Open access Sep 2026

Topological and Geometric Native Coding for Higher Dimensional Programming

Abstract This record contains the comprehensive monograph, "Topological and Geometric Native Coding for Higher Dimensional Programming" by Elias Oulad Brahim (September 2026). The research introduces ToposLang and the novel paradigm of Geometric Native Coding (GNC), which challenges the structural limitations of the cl...

Elias Oulad Brahim · 0 citations

Predicting Fines Migration Threshold in Sandstone Reservoir: A Machine Learning Framework for Predicting Critical Salt Concentration

Fines migration is a well-documented phenomenon in sandstones, which impairs permeability and causes productivity and injectivity issues. Critical salt concentration (CSC) defines the lowest brine salinity below which fines detach from sand surface due to increased repulsive forces between fines and sand. While prior...

S. Belkhir, R. Muneer, Y. Alblooshi et al. · 0 citations
#explainable ai Review Open access Oct 2026

Modeling artificial intelligence readiness and semiotic override in Indonesian geometry education

Findings show that hardware exposure alone is insufficient for AI-oriented learning transformation, andometry teachers should diagnose symbolic overreliance and strengthen visualization-based scaffolding, while school leaders and policymakers should balance infrastructure procurement with adaptive pedagogy, teacher sup...

Andi Kaharuddin, A. Rinawati, Ahmad Syamsuadi et al. · 0 citations
#explainable ai Open access Sep 2026

ACI 318-25 Concrete Beam Checking Assistant - A Claude AI Skill for Structural Engineering Code Compliance (Version 3.0, Imperial Units)

The ACI 318-25 Concrete Beam Checking Assistant is a reusable Claude AI Skill for checking reinforced concrete beam designs per ACI 318-25 and gravity loads per ASCE 7-22, using US customary (imperial) units exclusively. The skill performs 10 sequential code checks, cites the specific ACI 318-25 section governing every...

Rinaz Riyaz Mohamed, Rahul Anand, Jigarbhai Mansukhbhai Sonani · 0 citations
#large language models Open access Sep 2026

The Four Planck Quantities Are Four Instances of One Rule ── 3rd ed.: in the Yamagishi Complete Planck Unit Unpacking Theory, the mass dimension fixes the power of 4π, and l_p/t_p=c holds in every unit system ── [Paper 40]

The Four Planck Quantities Are Four Instances of One Rule: Mass Dimension Fixes the Power of 4pi, and l_p/t_p = c Holds in Every Unit System. Third edition. This paper carried a table showing that under the Yamagishi patch G = 4pi, hbar = 1/(4pi) the four Planck quantities read l_p = c^(-3/2), t_p = c^(-5/2), m_p = sqr...

Yuuki Yamagishi · 0 citations
#large language models Open access Sep 2026

From Boundary Energy to Volume Energy ── The Einstein–Yamagishi Volume Restoration Equation and the Overclock Pythagoras Equation ── [Paper 12]

What sits at the centre of this paper is not a computation but a re-reading. Substituting the geometric mass parameter m=4π obtained from the n=2 uniqueness theorem into Einstein's relation gives E = mc² = 4π c², and the right-hand side has the same form as the surface area of a sphere of radius c. Read in this form, E...

Yuuki Yamagishi · 0 citations

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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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