large language models

A Review of the Integrated Application of Artificial Intelligence and Building Information Modeling

Under the background of digital transformation in the construction industry, Building Information Modeling (BIM) and artificial intelligence (AI) have become an important technical path to promote the intelligent upgrading of the construction industry. AI-BIM integration improves design efficiency, project control qual...

Prompt Engineering and Architectural Ablations for Domain-Adaptive Resume Generation

The Ability of Generative Artificial Intelligence to Evaluate the Quality of Scholarly Output

Large language models (LLMs) are increasingly being considered as tools to support the peer-review process. However, we currently have a limited understanding of their capabilities and biases in this area. In a set of three studies, we examined how out-of-the box LLMs evaluate scientific quality when presented with con...

tokenwatt: ground-truth energy measurement for LLM inference across heterogeneous CPU power islands

A RAPL-based measurement harness for large language model inference on heterogeneous CPUs. Separates prefill from decode, records KV cache growth at every decode step, subtracts a verified idle baseline, and degrades to timing-only measurement rather than estimating energy when the counters are unreadable. Includes the...

Multilingual Psychological_Disorders_Detection

Multilingual detection of Pshycological Disordes using PHQ-9 feature extraction via NLP & Large Language Model

LangExtract v1.7.1

LangExtract is a Python library that uses Large Language Models (LLMs) to extract structured information from unstructured text documents based on user-defined instructions. It processes materials such as clinical notes or reports, identifying and organizing key details while ensuring the extracted data corresponds to...

Third Intelligence

Friends and colleagues, The Third Intelligence is a first attempt to strike a match in a dark room. Someone has probably been here before. Or perhaps not? Strike a match and look around. The book’s central question is simple: what happens when a human being and AI arrive together at a thought that neither had before th...

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...