large language models

Analysis code for: Large language models supply actionable management plans when the implicit clinical premise of a request does not hold

Analysis code behind the quantitative results of the manuscript. It covers prompt construction from the paired scenario library, response collection from seven language models, the blinded manual-coding workflow, inter-rater reliability, the rule-based classifier used in the repetition-stability analysis, and the stati...

Pneumatic retinopexy identification in electronic medical records using an on-premises large language model

Material Suplementar - Large Language Models, UML Diagrams, Software Documentation, Software Maintenance, Systematic Mapping Study

Este pacote de replicação contém a planilha utilizada no mapeamento sistemático da literatura sobre o uso de Modelos de Linguagem de Grande Escala (LLMs) na geração, extração e validação de diagramas UML. O material reúne os registros do processo de busca e seleção dos estudos, incluindo os resultados da busca na Scopu...

The Grand Unified Temporary Protocol: Demystifying "Emergence" and Hardware-Level Safety Chips for AI Executive Summary

I. Why Build a "Grand Unified Theory"? (Eradicating Academic Fragmentation and Cyber-Superstition) Contemporary science has fallen into a paradoxical dilemma: physicists lack AI insight, AI engineers lack neuroscientific perspectives, and economists miss the logic of complex systems. When faced with the emergent capabi...

From Neural Signatures to Narrative Intelligence

The chapter introduces a unique neuromarketing/generative AI model that is expected to enhance emotion-adaptive brand communication. This chapter is aimed at illustrating how neural signatures based on EEG, fMRI, eye-tracking, and biometric data can be combined with large language models and narrative intelligence syst...

Consequential Behaviour and Representational Fairness in the Validation of Synthetic Research

Researchers in industry and academia use synthetic survey respondents powered by large language models as substitutes for human samples. These synthetic populations require validation against real-world data, so researchers often address them using ad hoc comparisons with human surveys. Inspired by the intention–behavi...

Integrating AI into optical engineering: experiences in workflow transformation

We report experiences integrating a large-language-model agent into a production camera-lens design workflow. Rather than claiming the AI designs better lenses, we describe a division of labor: the AI writes analytical tooling and runs trade-space sweeps that would otherwise consume weeks of expert time, while the engi...

Content Deviation Recognition and Recommendation Model for Sports and Health Information: Based on Improved Text Rank Algorithm and Deep Fusion Transformer Architecture

A multimodal deep learning-driven model for assessing drawing composition quality

In recent years, digital art education, computerized evaluation systems, and aesthetic computing have prioritized drawing composition quality assessment. Traditional assessments have relied on subjective judgment or single-modal analysis, limiting scalability and uniformity. Existing methods use visual characteristics...

Geração de Workloads Sintéticos com LLMs: Um Estudo sobre o Impacto de Logs de Produção na Representatividade de Testes de Desempenho

Este pacote de replicação reúne os artefatos utilizados em um estudo quasi-experimental sobre a geração de workloads sintéticos para testes de desempenho com Large Language Models (LLMs). O estudo analisou duas plataformas web do setor de biogás, biometano, bioenergia e sustentabilidade: Portal Energia e Biogás e Biogá...

Mechanistic Interpretability and Explainable AI (XAI) in Foundation Models: Reverse-Engineering Circuits, Superposition, and Safety Bounds

Abstract—Foundation models, particularly autoregressive Transformer Large Language Models (LLMs) and Vision-Language Models (VLMs), have exhibited unprecedented reasoning and generative capabilities across computer science and engineering. However, their internal representations operate as opaque, high-dimensional 'bla...

Intelligent Circuit Design: Exploring Artificial Intelligence for Logic Gate Synthesis and Optimization

Modern integrated circuits contain billions of logic gates, while thermal and sub-micron parasitic limits make every design decision more expensive. Classical logic synthesis relies on deterministic minimization and hand-engineered heuristics that struggle to balance Power, Performance, and Area (PPA) at the same time....