large language models

Evaluating Autonomous LLM Agents Across Molecular Prediction and Optimization Benchmarks

Large language model (LLM) agents are increasingly capable of carrying out autonomous computational research, but it remains unclear whether they can develop molecular modeling methods that compete with strong human-developed approaches. Here, we evaluate autonomous method development across four settings: Therapeutics...

Guideline adherence and expert-rated potential safety concerns in AI-generated dietary advice for patients with cirrhosis: a comparative evaluation of large language models

Background Patients with cirrhosis require complication-specific nutritional counseling. Large language models are increasingly used for health information seeking, but the guideline adherence and potential safety concerns of their dietary advice for cirrhosis remain uncertain. Methods Thirty-six standardized simulated...

Natural Language Query Hybrid Architecture for Cross-Sectoral Analytics Dashboards on Public Statistical Data

Background: Public statistical data are essential for evidence-based policymaking, yet the complexity of cross-sectoral indicators often limits accessibility for nontechnical users. This challenge is particularly relevant in East Java, where regional development disparities require integrated analytical tools. Objectiv...

Asymmetric Cascade Fusion Visual-RAG Large Model for Wind Turbine Blade Defect Recognition

This repository provides the official implementation of the VAGF-RAG Cascade framework for industrial defect recognition. It integrates YOLOv8 (Spatial Perception), Qwen2-VL (Cognitive Review), and CLIP-RAG (Multimodal Evidence Retrieval) to decouple the precision-recall trade-off in industrial anomaly detection. The a...

Self-refining structures in large-language generative AI models for the generation of cybersecurity detection capabilities using machine-based and natural languages

The cybersecurity threat landscape evolves rapidly placing increasing pressure on detectionapproaches that rely on static rules, manually curated signatures, or models trained on fixeddatasets. While modern security environments collect large volumes of telemetry data, theprocesses used to generate and maintain detecti...

A reasoning large language model for rare disease diagnosis: a randomized AI physician-assistance trial

The dataset contains individual-level data from participants in the randomized controlled trial, including demographic information, diagnostic accuracy, helpfulness scores, confidence ratings, and task duration, all recorded in an Excel file.

A novel privacy-preserving large language model integrating trust-weighted and ethical gradient masking

Large language models for invasive urodynamic interpretation: a blinded comparison with experienced urologists

Abstract Background and objective Large language models (LLMs) are increasingly explored as clinical decision-support tools, but their performance in invasive urodynamic interpretation remains poorly characterized. This study compared the diagnostic agreement of four LLM configurations with two blinded experienced urol...

Supporting Multi-Step Workflows with LLM-Based Web Automation using Agentic Context Engineering and Selective Multimodal Reasoning

[Background.] Multi-step web workflows, such as comparing offers across portals, cost people much time. Scripts must be written for each site and break when pages change; agents built on large language models can pursue a goal stated in natural language on unseen sites. Current agents depend on site-specific interactio...

Nosing Around: Dowsing the Data Landscape with an AI Divining Rod AI as a Search Instrument in the Preliminary Phase of Qualitative Analysis

This article introduces the "AI divining rod," a procedure that uses large language models (LLMs) exclusively in the preliminary phase of qualitative analysis. By analogy with the "nosing around" of the Chicago School, the researching subject explores the interview material by roaming through it. The AI first identifie...

My (22m) Girlfriend (23f) Comes Home and Does Nothing”– Gendered Perceptions of Paid and Unpaid Work in Reddit Relationship Discussions over Time

The COVID-19 pandemic reignited longstanding debates around gender inequalities in paid and unpaid work. While survey research has advanced our understanding of these disparities, it typically relies on predefined categories and is susceptible to social desirability and recall bias. Online postings, by contrast, captur...

Human, AI, and Hybrid Ensembles for Detection of Adaptive, RL-based Social Bots

The use of reinforcement learning to dynamically adapt and evade detection is now well-documented in several cybersecurity settings including Covert Social Influence Operations (CSIOs), in which bots try to spread disinformation. While AI bot detectors have improved greatly, they are largely limited to detecting static...