artificial intelligence

OpenFrontiers: A Collection of Open Research Problems and a Benchmark for Scientific Rediscovery

Evaluating seven LLM agents on the HQ-50 subset under a standardized budget, it is found that closed-book rediscovery is rare, at most 7 of 40 resolved problems, whereas live-web success mostly reflects retrieval of published solutions.

ThinkJev: One Thought, Every Budget for Calibrated Typed Decisions

ThinkJev builds a fast and a slow system from one open hybrid-thinking model that can answer directly or after a thought, and chooses when to think from System 1's confidence alone saves less, because the decisions System 1 is least sure of are often not the ones thinking fixes.

Amortized Localized Posterior Inference for Model Calibration with Application to an Ocean Turbulence Closure

An application to the k-$\epsilon$ turbulence closure in a single-column ocean model, with atmospheric surface forcing as context, shows that localized posterior calibration reduces temperature and zonal-velocity errors relative to deterministic and global-posterior calibration.

LRMoE: Demand-Side Load Re-Calibration for Efficient MoE Training on Memory-Constrained Nodes

LRMoE is presented, a memory-aware MoE training system that jointly recalibrates backward demand and balances the retained workload, and reduces cumulative MoE-layer training time to fixed-loss targets by 21.2-44.8% relative to SYMI.

Artificial Intelligence: Competencies for design in emerging creative environments

The study aims to determine the competencies linked to the use of Artificial Intelligence in the training of designers, as well as to examine its impact on the professional environment.

Reskilling for AI Adoption: Predicting Workforce Readiness Using Learning Analytics

It is argued that reskilling should be managed on practice and application rather than completion, that readiness predictions should direct support toward employees near the threshold, and that learning analytics should be governed as employee data with developmental purpose.

Explainable Artificial Intelligence for Improving Transparency and Reliability in Automated Decision-Making Systems

This narrative review examines how explainable artificial intelligence (XAI) may improve transparency and reliability by clarifying model behavior, revealing data dependencies, supporting error detection, and enabling informed human oversight.

AI, Neuro-Inclusion and Inclusive Development in the Global South: A Governance Framework for Equitable Participation

An integrated framework for AI-enabled neuro-inclusion is proposed that places neurodiversity, accessibility, human autonomy, transparency, privacy, and participatory design at the center of responsible AI development.

Integrating artificial intelligence into nuclear reactor safety: ML-augmented CFD, AI-enabled PSA, and cybersecurity

Toward a Trustworthy AI-Based Decision Support for Bridge Asset Management

Compared with traditional reactive, condition-based decision policies, the proposed AI-based policies are found to offer more proactive and adaptive decisions and to demonstrate superior performance in minimizing the life cycle costs.

Artificial intelligence-driven prediction of biogas production from co-digestion of food waste and pongamia pinnata seed shells

Artificial intelligence and machine learning in agri-food systems: A comprehensive review of technological frontiers, socio-ethical barriers, and future roadmaps

How operational efficiency and decision-making are enhanced across all phases of any agricultural production system, such as planting, irrigation, pest control, harvesting, aquaculture, food safety, and post-harvest handling is highlighted.