Results 141 to 150 of about 22,074,380 (291)
Automating AI Discovery for Biomedicine Through Knowledge Graphs and Large Language Models Agents
Advanced Intelligent Discovery, EarlyView.This work proposes a novel framework that automates biomedical discovery by integrating knowledge graphs with multiagent large language models. A biologically aligned graph exploration strategy identifies hidden pathways between biomedical entities, and specialized agents use this pathway to iteratively design AI predictors and wet‐lab validation ...
Naafey Aamer +3 more
wiley +1 more source
LLM‐Based Scientific Assistants for Knowledge Extraction: Which Design Choices Matter?
Advanced Intelligent Discovery, EarlyView.A comprehensive framework for optimizing Large Language Models in domain‐specific applications is introduced. The LLM Playground integrates Prompt Engineering, knowledge augmentation, and advanced reasoning strategies to enable systematic comparison of architectures and base models.
David Exler +7 more
wiley +1 more source
AI Powered Biobanks From Static Archives to Dynamic Discovery Engines
Advanced Intelligent Discovery, EarlyView.Large language models (LLMs) provide a potential framework for transforming biobanks from static data repositories into intelligent discovery engines. By enabling unified representation and analysis of multimodal biomedical data, LLM‐based systems facilitate dynamic risk prediction, biomarker identification, and mechanistic interpretation, thereby ...
Wenzhen Yin +5 more
wiley +1 more source
LLM for Everyone: Representing the Underrepresented in Large Language Models
CoRRPhD ...
openaire +2 more sources
Spatiotemporal Pre-Trained Large Language Model for Forecasting with Missing Values
Spatiotemporal data collected by sensors within an urban Internet of Things (IoT) system inevitably contains some missing values, which significantly affects the accuracy of spatiotemporal data forecasting.
Salim, FD +4 more
core +1 more source
An Autonomous Large Language Model‐Agent Framework for Transparent and Local Time Series Forecasting
Advanced Intelligent Discovery, EarlyView.Architecture of the proposed large language model (LLM)‐based agent framework for autonomous time series forecasting in thermal power generation systems. The framework operates through a vertical pipeline initiated by natural language queries from users, which are processed by the LLM Agent Core powered by Llama.cpp and a ReAct loop with persistent ...
William Gouvêa Buratto +5 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath +4 more
wiley +1 more source
Psychotic episode concurrent with interaction with a large language model (llm): a case report. [PDF]
BMC PsychiatryBaşaran AS, Coşar B.
europepmc +1 more source
When Biology Meets Medicine: A Perspective on Foundation Models
Advanced Intelligent Discovery, EarlyView.Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu +3 more
wiley +1 more source
Concept Drift in Large Language Models: Challenges of Evolving Language, Contexts, and the Web
Deep learning models including large language models (LLMs) suffer from concept drift for various reasons and causes. A drift can occur either during data collection due to changes in data distribution and data imbalance, or due to external factors such ...
Thierry J. Chaussalet +3 more
core +1 more source