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2,343 notes. Notes 551 to 600, page 12 of 47.
Does FLAIR super-resolution erase or hallucinate small white-matter lesions?
The study investigates whether using super-resolution (SR) techniques on Fluid-attenuated Inversion Recovery (FLAIR) scans affects the detection of small white-matter lesions. The research finds that SR tends to erase…
ECHO: A Locally-Deployable Agentic Health Assistant with Temporal Memory, Safety Guardrails, and Speech Assessment
The paper introduces ECHO, a locally-deployable health assistant designed for long-term chronic care management. It consists of three integrated modules: an agentic chatbot, a safety layer, and a speech assessment…
EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning
Researchers have developed EnvACE, a method for training large language model agents that simulates environment interactions during training using world rehearsal instead of relying on real or synthesized executable…
EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning
The researchers introduce EnvACE, a method for reinforcement learning that replaces external environment interactions during training with world rehearsal. This method allows the policy to internalize the relationship…
EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning
Researchers have introduced EnvACE, a method for training large language model agents using world rehearsal instead of interactions with real or simulated environments. The method involves the policy alternating…
EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning
The paper introduces EnvACE, a method for training large language model agents for long-term tool use without relying on interactions with real or synthesized environments, which can be costly to construct and verify…
Evaluating Investment Logic in Large Language Models: A Real-World Benchmark Towards Personalzied Financial Agents
The paper introduces InvestLogicBench, a new benchmark to evaluate the investment logic of large language models (LLMs). The benchmark focuses on personalized financial decision-making and uses real-world data from 151…
Evaluating Investment Logic in Large Language Models: A Real-World Benchmark Towards Personalzied Financial Agents
The paper introduces InvestLogicBench, a benchmark for evaluating the investment logic of large language models (LLMs). The benchmark measures the logical plausibility and event grounding of LLMs, suggesting that…
FinEvo-Bench: A Longitudinal Benchmark for Self-Evolving Agents in Professional Financial Workflows
Researchers have created a new benchmark for self-evolving agents in financial workflows called FinEvo-Bench. The benchmark includes 120 real-life tasks across six domains, with each task having a specific professional…
FinEvo-Bench: A Longitudinal Benchmark for Self-Evolving Agents in Professional Financial Workflows
Researchers introduce a new benchmark called FinEvo-Bench for testing self-evolving agents in various financial workflows. The benchmark consists of 120 real-world tasks across six domains, with each task having a…
FinEvo-Bench: A Longitudinal Benchmark for Self-Evolving Agents in Professional Financial Workflows
The research introduces a new benchmark called FinEvo-Bench for evaluating the performance of self-evolving agents in professional financial workflows. The benchmark consists of 120 real-case-grounded tasks across six…
FinEvo-Bench: A Longitudinal Benchmark for Self-Evolving Agents in Professional Financial Workflows
The study introduces FinEvo-Bench, a benchmark for evaluating the performance of self-evolving agents in various financial tasks. The benchmark consists of 120 real-case-grounded tasks across six financial domains…
From Passive Mirrors to Active Agents: Holonic Digital Twins for Physical AI over Networks
The paper proposes a framework called Holonic Digital Twins Networks (HDT-Nets) to improve the performance of AI in physical systems like robots and vehicles. The framework aims to deliver real-time physical AI…
From Passive Mirrors to Active Agents: Holonic Digital Twins for Physical AI over Networks
The paper proposes a new framework called HDT-Nets to enable real-time physical AI inference for robots and vehicles operating under real-world conditions. This is achieved by creating holonic digital twins (HDTs) that…
From Passive Mirrors to Active Agents: Holonic Digital Twins for Physical AI over Networks
The paper proposes a framework called HDT-Nets to enable real-time physical AI inference for robots and vehicles operating under real-world conditions. The framework utilizes holonic digital twins (HDTs), which are…
From Passive Mirrors to Active Agents: Holonic Digital Twins for Physical AI over Networks
The paper proposes a new framework called HDT-Nets to improve the performance of AI in physical systems like robots and vehicles. This framework uses holonic digital twins (HDTs) which are hierarchical structures that…
From Siloed Algorithms to Compliance-First Agentic Platforms: A Multi-Layered Architecture for Hospital AI Systems
The research proposes a multi-layered architecture for hospital AI systems aimed at addressing the challenges of isolated AI deployments in hospitals. The proposed architecture includes an Agent Orchestration Layer, a…
From Siloed Algorithms to Compliance-First Agentic Platforms: A Multi-Layered Architecture for Hospital AI Systems
The research proposes a new architecture for hospital AI systems aimed at addressing the issues of isolated point solutions and lack of compliance in current AI deployments in hospitals. The proposed architecture…
From Siloed Algorithms to Compliance-First Agentic Platforms: A Multi-Layered Architecture for Hospital AI Systems
The research proposes a multi-layered compliance-first Agentic AI architecture for hospital systems. The architecture aims to address the challenges of siloed AI deployments in hospitals by integrating various layers…
From Siloed Algorithms to Compliance-First Agentic Platforms: A Multi-Layered Architecture for Hospital AI Systems
The research proposes a multi-layered compliance-first Agentic AI architecture for hospital AI systems. This architecture aims to address the issues of duplicated effort, hidden risks, and unrealized value in current…
Hardware Keystores for AI Agent Signing Workflows: A Zero-Trust MCP Enforcement Architecture
Researchers propose a system to store AI agent's private keys in hardware-confined devices, improving key confidentiality and authorization. They replace software-stored keys with ones stored in HSMs, TPMs, or smart…
Hardware Keystores for AI Agent Signing Workflows: A Zero-Trust MCP Enforcement Architecture
The paper proposes a system to secure AI agents' private keys by replacing software-stored keys with hardware-confined ones, accessible through a standard PKCS#11 interface. The system aims to ensure key…
Hardware Keystores for AI Agent Signing Workflows: A Zero-Trust MCP Enforcement Architecture
The authors propose a new architecture to improve security for AI agents that perform cryptographic operations by replacing software-stored private keys with hardware-confined ones, using a vendor-neutral PKCS#11…
Hardware Keystores for AI Agent Signing Workflows: A Zero-Trust MCP Enforcement Architecture
The researchers propose a system that uses hardware-confined keys instead of software-resident keys to enhance security for AI agents performing cryptographic operations. The system aims to protect against key…
HarnessOpt-Bench: Evaluating LLMs at Harness Optimization
Researchers introduce HarnessOpt-Bench, a benchmark for evaluating the ability of Large Language Models (LLMs) to optimize their own systems. The study compares five frontier LLMs across four tasks and finds that they…
HarnessOpt-Bench: Evaluating LLMs at Harness Optimization
Researchers have introduced HarnessOpt-Bench, a benchmark to measure the performance of Large Language Models (LLMs) in optimizing their own harnesses (the prompts, tools, etc. surrounding them). The research shows…
HarnessOpt-Bench: Evaluating LLMs at Harness Optimization
The paper introduces HarnessOpt-Bench, a benchmark for evaluating the ability of Language Models (LLMs) to optimize the harnesses surrounding them, which includes prompts, tools, control flow, memory, and orchestration…
HarnessOpt-Bench: Evaluating LLMs at Harness Optimization
Researchers have introduced HarnessOpt-Bench, a benchmark for evaluating the performance of language models (LLMs) in optimizing harnesses (the tools and code surrounding them). The study tests 5 leading LLMs across 4…
HarnessOpt-Bench: Evaluating LLMs at Harness Optimization
The text introduces HarnessOpt-Bench, a benchmark for evaluating the performance of Large Language Models (LLMs) in optimizing AI systems. The benchmark involves an iterative process where an LLM edits a given harness…
HarnessOpt-Bench: Evaluating LLMs at Harness Optimization
Researchers introduce HarnessOpt-Bench, a benchmark for evaluating the performance of Large Language Models (LLMs) in optimizing their own harnesses. The study examines five frontier LLMs on four tasks and finds that…
iARCS: Iterative Agentic RL for Controllable 3D Scene Generation
The paper presents iARCS, an iterative agentic reinforcement learning framework for enhancing the performance of synthetic 3D scene generators by adapting them to meet task-specific functional constraints, such as…
iARCS: Iterative Agentic RL for Controllable 3D Scene Generation
Researchers present iARCS, an iterative agentic reinforcement learning framework to adapt a pre-trained 3D scene generator for meeting task-specific requirements in natural language. The system uses a two-stage…
Improving the Realism of Synthetic Clinical Benchmarks Under Utility Constraints
Researchers propose a method to improve the realism of synthetic clinical benchmarks for AI agents in healthcare settings without compromising their downstream utility checks. They develop a framework that ensures…
Improving the Realism of Synthetic Clinical Benchmarks Under Utility Constraints
The authors propose a method to improve the realism of synthetic clinical benchmarks for enterprise AI agents while maintaining their utility. They focus on healthcare settings where operational data is hard to access…
Improving the Realism of Synthetic Clinical Benchmarks Under Utility Constraints
Researchers propose a method to create more realistic synthetic clinical benchmarks for AI agents in healthcare settings, where operational data is hard to access. The method aims to improve the benchmark's realism…
Improving the Realism of Synthetic Clinical Benchmarks Under Utility Constraints
Researchers propose a method to improve the realism of synthetic clinical benchmarks for AI agents in privacy-sensitive healthcare settings without compromising their practical utility. They formulate this as a problem…
Investigating Artificial Intelligence Digital Sovereignty in Mobile Shopping Apps: A Case Study of Nigeria
This research investigates the impact of Artificial Intelligence (AI) in Nigerian mobile shopping apps on digital sovereignty, focusing on platform transparency as a key indicator of user awareness and control. The…
Investigating Artificial Intelligence Digital Sovereignty in Mobile Shopping Apps: A Case Study of Nigeria
This research investigates how Artificial Intelligence (AI) affects digital sovereignty in mobile shopping apps in Nigeria. The study finds that while AI is widely used, transparency about its implementation is…
Investigating Artificial Intelligence Digital Sovereignty in Mobile Shopping Apps: A Case Study of Nigeria
The research investigates how Artificial Intelligence (AI) in Nigerian mobile shopping apps affects digital sovereignty, focusing on platform transparency as a key indicator of user awareness and control. The findings…
Learning Globally Reusable Skills for Coding Agents
Researchers present a framework called GSE (Globalized Skill Evolution) that aims to improve automated skill evolution for Large Language Model (LLM) agents in software engineering tasks. The proposed method addresses…
Learning Globally Reusable Skills for Coding Agents
Researchers propose a new method called GSE (Globalized Skill Evolution) to help Large Language Model agents continuously improve their skills without needing expensive retraining. The proposed method optimizes skill…
Learning Globally Reusable Skills for Coding Agents
Researchers have developed a new approach called Globalized Skill Evolution (GSE) to help Large Language Model (LLM) agents continuously improve their coding skills without needing expensive retraining. This method is…
MicroEvo: Knowledge-Guided LLM Sampling for Efficient Microarchitecture Design Space Exploration
A new method called MicroEvo is proposed for efficient exploration of microarchitecture design spaces. This approach utilizes a combination of large language models (LLMs) and Monte Carlo Tree Search (MCTS) to optimize…
Poli-Bias: Understanding and Measuring Large Language Model Biases in International Political Conflicts
Researchers introduce Poli-Bias, a method to measure political bias in large language models (LLMs) by comparing responses to paired prompts where country identities are swapped across various geopolitical…
Poli-Bias: Understanding and Measuring Large Language Model Biases in International Political Conflicts
The text introduces a new method called Poli-Bias, which is designed to measure bias in large language models (LLMs) when dealing with international political conflicts. This framework compares responses to paired…
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…
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