large language models

Towards Iterative End-to-End Software Development: A Feature-Driven Multi-agent Framework

Recent advances in large language model agents offer the promise of automating end-to-end software development from natural language requirements. However, existing approaches largely adopt linear, waterfall-style pipelines, which oversimplify the iterative nature of real-world development and struggle with complex, la...

Reproducibility package for traceable synthesis of antibiotic resistance gene burdens across wastewater treatment systems

Reproducibility package (data, human audit records, protocol and analysis scripts) for the article "What the evidence supports for surveillance of antibiotic resistance genes in treated wastewater: a human-verified, large language model-assisted synthesis". Version 1.3.0 contains the final datasets (v4) after the eligi...

VulFileHunter: leveraging description augmentation with large language models for vulnerability-relevant file uncovery

ASSOCIAÇÃO SEMÂNTICA ENTRE EDGV E OPENSTREETMAP: ESTUDOS PRELIMINARES COM VEGETAÇÃO APLICANDO ONTOLOGIA E LARGE LANGUAGE MODEL

Corrigendum to “Case reports unlocked: Harnessing large language models to advance research on child maltreatment” [Child Abuse Neglect Volume 160, 107202]

ToshLLM: local LLM inference on Intel Macs with AMD GPUs

A native SwiftUI application that runs large language models locally on Intel Macs with AMD GPUs, a configuration mainstream inference stacks leave unsupported or incorrect. Beyond packaging, it contributes original work to the Metal backend of llama.cpp: ToshGEMM, a manually tiled matrix multiply that replaces the sim...

The Context Collapse Paradox: Why Data Saturation Induces Cognitive Noise in Enterprise LLMs

Modern Large Language Models (LLMs) boast effective context windows spanning from 32,000to over 1,000,000 tokens. In production enterprise architectures, this expansion has driven a naivedesign pattern: piping uncurated historical databases, transcripts, and document revisions directlyinto the active prompt. In this wo...

Application of Retrieval-Augmented Generation Large Language Models in Stomatology: A Scoping Review

Large language models (LLMs) have been gradually applied to tasks such as specialized question answering, educational assistance, clinical decision support, and knowledge integration in stomatology. However, general-purpose LLMs still suffer from critical limitations, including outdated knowledge, hallucinatory generat...

Teachers’ use of generative artificial intelligence in K–12 education: a systematic review

The rapid advancement of generative artificial intelligence (GenAI) has created new opportunities and challenges for teachers, leading to a growing body of research on its educational applications. AI has become increasingly integrated into instructional processes, supporting teachers in tasks such as lesson planni...

ASSOCIAÇÃO SEMÂNTICA ENTRE EDGV E OPENSTREETMAP: ESTUDOS PRELIMINARES COM VEGETAÇÃO APLICANDO ONTOLOGIA E LARGE LANGUAGE MODEL

The Context Collapse Paradox: Why Data Saturation Induces Cognitive Noise in Enterprise LLMs

Modern Large Language Models (LLMs) boast effective context windows spanning from 32,000to over 1,000,000 tokens. In production enterprise architectures, this expansion has driven a naivedesign pattern: piping uncurated historical databases, transcripts, and document revisions directlyinto the active prompt. In this wo...

Reproducibility package for traceable synthesis of antibiotic resistance gene burdens across wastewater treatment systems

Reproducibility package (data, human audit records, protocol and analysis scripts) for the article "What the evidence supports for surveillance of antibiotic resistance genes in treated wastewater: a human-verified, large language model-assisted synthesis". Version 1.3.0 contains the final datasets (v4) after the eligi...