large language models

Efficient and Effective Test Generation and Type Inference for Python Applications

Python has become one of the most widely used programming languages, valued for its flexibility and ease of use. However, its dynamic nature provides only limited static guarantees, making programs more difficult to write reliably: errors are more likely to remain undetected until runtime. Tests and type checking can m...

Compact Reinforcement Learning: Resource-Efficient Formulations for Large-Scale Decision Making

Reinforcement learning (RL) offers a general framework for addressing problems involving sequential decision-making. However, modern practical applications of RL involve prohibitively large state and action sets: such as those in slate recommendations, image-based control, and large language-modeling tasks. Standard fo...

MAOMAO: an ontology-guided FAIR resource for harmonized peptide toxicity data

Therapeutic peptides have emerged as a rapidly expanding class of biomolecules with applications spanning antimicrobial, antiviral, anticancer, immunomodulatory, metabolic, and diagnostic settings (8). Their high target specificity, favorable safety profiles, and growing clinical success have positioned them as attract...

Behavioral Analysis of AI-Assisted User Interaction in a Digital Art Repository

Recent developments in artificial intelligence, including the emergence of generative AI and large language models, have brought changes to user interaction habits as well as interface design. These emerging technologies can be integrated into digital art repositories to reshape interaction paradigms through content en...

Cloud‐Native and Distributed Systems for Efficient and Scalable Large Language Models—A Research Agenda

ABSTRACT Objective Large Language Models (LLMs) are rapidly transforming artificial intelligence applications, but their unprecedented computational, memory, communication, and data‐management demands pose significant challenges to existing computing infrastructures. Traditional cloud and distributed systems were not d...

Protocol: From Pilots to Practice: A Scoping Review of Artificial Intelligence Adoption in the Healthcare Sector of the GCC Countries and Egypt

Protocol (version 0.6, 17 October 2026) for a scoping review mapping the adoption of artificial intelligence across five healthcare sectors in Saudi Arabia, the United Arab Emirates, Qatar, Kuwait, Bahrain, Oman and Egypt (2015–2026): hospitals and providers; pharmaceutical companies and manufacturers; pharmacies and d...

Automated surveillance of primary outcome reporting in clinical trials: Development and validation of a scalable screening pipeline.

Background: Detecting discrepancies between registered and published trial outcomes currently requires manual expert review, limiting surveillance to small samples. No scalable automated system exists for continuous monitoring of outcome reporting integrity across the clinical trial literature. We aimed to develop and...

Large language models in STEM education: Opportunities, pedagogical shifts, ethical concerns, and future research directions

From Fabricated Citations to Unverified Journal Matches: Problems and Solutions in LLM Assisted Manuscript Generation

Examining scopus-jurnal, an open-source Python multi-agent pipeline that retrieves literature, drafts IMRaD sections, validates citations, reviews manuscripts, and recommends journals, shows that although the pipeline abstained when evidence was absent, weaknesses emerged between LLM outputs and deterministic code.

Adapting large language models for urban trip purpose inference: A domain-informed textual representation and fine-tuning framework

VirtuAI-Safe: Personalized Situated Safety Learning through Integration of VR and LLMs

Construction safety outcomes are strongly shaped by human factors, including how individuals perceive hazards, interpret contextual cues, and make safety-related decisions in dynamic work environments. Safety education therefore plays a critical role in preparing future construction professionals to reason about risk...

Mind the mismatch: mapping stakeholder-interest divergence in urban e-mobility across major western anglophone arenas using large language models