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QuanTiMedAI: Quantum-Enhanced Time-Series Model guided by Agentic AI for Cardiac Arrest Mortality Prediction
Researchers introduce QuanTiMedAI, a framework for predicting cardiac arrest mortality using a combination of an agentic large language model (LLM) and a quantum recurrent network. The LLM is used for feature…
QuanTiMedAI: Quantum-Enhanced Time-Series Model guided by Agentic AI for Cardiac Arrest Mortality Prediction
The researchers present QuanTiMedAI, a quantum-agentic framework designed for predicting cardiac arrest mortality rates. This system combines an agentic large language model for clinically informed feature discovery…
QuanTiMedAI: Quantum-Enhanced Time-Series Model guided by Agentic AI for Cardiac Arrest Mortality Prediction
Researchers have developed a new framework called QuanTiMedAI for predicting mortality in cardiac arrest patients in intensive care units (ICUs). The framework uses agentic AI and quantum enhancement to analyze…
QuanTiMedAI: Quantum-Enhanced Time-Series Model guided by Agentic AI for Cardiac Arrest Mortality Prediction
Researchers present QuanTiMedAI, a new framework for predicting mortality in cardiac arrest patients using quantum-enhanced time series models guided by an agentic AI. The system combines an agentic large language…
QuanTiMedAI: Quantum-Enhanced Time-Series Model guided by Agentic AI for Cardiac Arrest Mortality Prediction
The researchers introduce QuanTiMedAI, a framework for predicting cardiac arrest mortality using agentic AI and quantum enhancement in time series models. The system combines an agentic large language model for…
Reducing belief in conspiracy theories as they unfold using large language models
Researchers tested if conversations with a large language model (LLM) can reduce belief in emerging conspiracy theories. They conducted experiments in the days following two significant events, and found that…
Reducing belief in conspiracy theories as they unfold using large language models
A study conducted by Costello et al. explores the use of large language models (LLMs) in reducing belief in conspiracy theories that emerge following major events, such as the assassination attempts on Donald Trump and…
Resourced Authority A Mechanism-Design Model for Participatory Governance of Deployed AI Agents
The authors propose a formal model for continuous participatory governance of AI agents, focusing on resource allocation as a means to enforce authorization through compute budgets. The aim is to establish a 'Safe AI'…
Resourced Authority A Mechanism-Design Model for Participatory Governance of Deployed AI Agents
The authors propose a model for continuous participatory governance of AI agents, focusing on controlling them through resource allocation to make authorization self-enforcing via compute budgets. The model consists of…
Resourced Authority A Mechanism-Design Model for Participatory Governance of Deployed AI Agents
The authors propose a model for continuous participatory governance of AI agents through resource allocation, specifically compute budgets, to make authorization self-enforcing. The model is designed as a compliance…
Resourced Authority A Mechanism-Design Model for Participatory Governance of Deployed AI Agents
The paper presents a model for continuous participatory governance of deployed AI agents, focusing on resource allocation (compute budgets) as a means to enforce authorization and ensure safe AI. The mechanism involves…
Schema-Guided Hierarchical Information Extraction and Semantic Evaluation Using Generative AI
Researchers have developed a new method for extracting complex, structured information from unstructured text documents using generative AI. The method involves creating a schema that serves as an information model…
Schema-Guided Hierarchical Information Extraction and Semantic Evaluation Using Generative AI
Researchers have developed a framework using generative AI to extract complex, structured information from unstructured text documents. This framework also includes an automated semantic evaluation step to compare the…
The Illusion of Visual Tool-Use: A Causal Audit of Thinking with Images
Researchers audit the effectiveness of using visual tools like crop-and-zoom in multimodal language models by creating a causal graph to separate observation-mediated paths from action-induced shortcuts. They find that…
The Illusion of Visual Tool-Use: A Causal Audit of Thinking with Images
The study examines the 'thinking-with-images' paradigm used in multimodal Language Models (LLMs), which includes visual operations like crop-and-zoom. The research finds that these operations often result in minimal or…
Tracing the Heart: An Evidence-Linked Pipeline for Heart-Failure Feature Engineering
Researchers developed an automated system called nMAS for heart-failure feature engineering in electronic health records (EHR). The system generates structured and aggregated features that were verified and audited…
Tracing the Heart: An Evidence-Linked Pipeline for Heart-Failure Feature Engineering
Researchers developed an AI system called nMAS to automate the process of extracting structured and aggregated features from Electronic Health Records (EHR) for heart failure research. The system generates these…
Tracing the Heart: An Evidence-Linked Pipeline for Heart-Failure Feature Engineering
Researchers have developed an evidence-linked pipeline called Nimblemind Multi-Agent System (nMAS) to automate heart-failure feature engineering for electronic health record data. The system generates structured and…
Tracing the Heart: An Evidence-Linked Pipeline for Heart-Failure Feature Engineering
Researchers have developed a system called nMAS for automating heart-failure feature engineering in electronic health records (EHRs). The system generates structured and aggregated features that improve the accuracy of…
TRAJDEBUG: Tracing Error Lifecycle to Identify Critical Failures in Long-Horizon Agent Trajectories
The paper proposes a framework called TrajDebug to improve error detection in long-term AI agent trajectories. It addresses the challenges of identifying errors in lengthy trajectories and multiple local errors with…
TRAJDEBUG: Tracing Error Lifecycle to Identify Critical Failures in Long-Horizon Agent Trajectories
In this study, the authors propose a framework named TrajDebug to identify critical errors in long-horizon agent trajectories, which are complex systems that can perform tasks but often suffer from cascading errors…
TRAJDEBUG: Tracing Error Lifecycle to Identify Critical Failures in Long-Horizon Agent Trajectories
The paper presents TrajDebug, a framework designed to trace error lifecycles in long-horizon AI agents. The goal is to identify critical failures by compressing history and using evidence-based error identification…
TRAJDEBUG: Tracing Error Lifecycle to Identify Critical Failures in Long-Horizon Agent Trajectories
The paper proposes TrajDebug, a framework for error-lifecycle tracing in long-trajectory agentic systems. The goal is to identify critical errors responsible for final failures, especially in complex domains where…
TRAJDEBUG: Tracing Error Lifecycle to Identify Critical Failures in Long-Horizon Agent Trajectories
In this study, researchers present TrajDebug, a framework designed to identify critical errors in long trajectories of large language models (LLM) used in complex domains. The framework addresses two challenges: first…
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