Healthcare systems are increasingly turning to ambient Artificial Intelligence (AI) scribes to reduce documentation burden and lighten clinicians’ cognitive load. In this brief research report, we introduce MediVoice, an ambient AI scribe developed and implemented within the National University Health System, Singapore. MediVoice was piloted across multiple clinical settings and rapidly evaluated through Plan–Do–Study–Act cycles. […]
Essential Oil-enhanced digital hypnotherapy for subclinical generalized anxiety: a study protocol for a randomized controlled trial
BackgroundSubsyndromal generalized anxiety is highly prevalent and associated with impaired well-being, elevated stress, and functional limitations, yet affected individuals often do not meet criteria for guideline-based treatment. Scalable, low-threshold digital interventions that target psychophysiological regulation may help address this gap. Guided self-hypnosis and aromatherapy using essential oils have each demonstrated anxiolytic and relaxation-promoting effects. Combining […]
Exploration of wearable sensor measures associated with panic attacks differs across mental health conditions
Panic attacks (PAs) are acute anxiety episodes that are pervasive, with one in 10 individuals having experienced a PA in the past year. PAs impair daily functioning and are associated with an increase in emergency room visits and suicide attempts. Despite their impact, the unpredictable nature of PAs makes them challenging to manage. PAs are […]
Rethinking Layer Redundancy in Large Language Models: Calibration Objectives and Search for Depth Pruning
arXiv:2604.24938v1 Announce Type: cross Abstract: Depth pruning improves the inference efficiency of large language models by removing Transformer blocks. Prior work has focused on importance criteria and search algorithms, often treating layer redundancy as an inherent structural property of pretrained networks. In contrast, we adopt a emphfunctional perspective, where redundancy is jointly influenced by the […]
EVT-Based Generative AI for Tail-Aware Channel Estimation
arXiv:2604.25008v1 Announce Type: cross Abstract: Ultra-reliable and low-latency communication (URLLC) will play a key role in fifth-generation (5G) and beyond networks, enabling mission-critical applications. Meeting the stringent URLLC requirements, characterized by extremely low packet error rates and minimal latency, calls for advanced statistical modeling to accurately capture rare events in wireless channels. Traditional methods, such […]
Time-varying Interaction Graph ODE for Dynamic Graph Representation Learning
arXiv:2604.24811v1 Announce Type: cross Abstract: Graph neural Ordinary Differential Equations (ODE) combine neural ODE with the message passing mechanism of Graph Neural Networks (GNN), providing a continuous-time modeling method for graph representation learning. However, in dynamic graph scenarios, existing graph neural ODEs typically employ a unified message passing mechanism, assuming that inter-node interactions share the […]
HearthNet: Edge Multi-Agent Orchestration for Smart Homes
arXiv:2604.09618v2 Announce Type: replace-cross Abstract: Smart-home users increasingly want to control their homes in natural language rather than assemble rules, dashboards, and API integrations by hand. At the same time, real deployments are brittle: devices fail, integrations break, and recoveries often require manual intervention. Existing agent toolkits are effective for session-scoped delegation, but smart-home control […]
AOI: Context-Aware Multi-Agent Operations via Dynamic Scheduling and Hierarchical Memory Compression
arXiv:2512.13956v3 Announce Type: replace-cross Abstract: The proliferation of cloud-native architectures, characterized by microservices and dynamic orchestration, has rendered modern IT infrastructures exceedingly complex and volatile. This complexity generates overwhelming volumes of operational data, leading to critical bottlenecks in conventional systems: inefficient information processing, poor task coordination, and loss of contextual continuity during fault diagnosis and […]
From Soliloquy to Agora: Memory-Enhanced LLM Agents with Decentralized Debate for Optimization Modeling
arXiv:2604.25847v1 Announce Type: cross Abstract: Optimization modeling underpins real-world decision-making in logistics, manufacturing, energy, and public services, but reliably solving such problems from natural-language requirements remains challenging for current large language models (LLMs). In this paper, we propose emphAgora-Opt, a modular agentic framework for optimization modeling that combines decentralized debate with a read-write memory bank. […]
A Comparative Study in Surgical AI: Datasets, Foundation Models, and Barriers to Med-AGI
arXiv:2603.27341v2 Announce Type: replace Abstract: Recent Artificial Intelligence (AI) models have matched or exceeded human experts in several benchmarks of biomedical task performance, but surgical benchmarks in particular are often missing from prominent medical benchmark suites (specifically, those requiring visual recognition). Since surgery requires integrating disparate tasks, generally-capable AI models could be particularly attractive as […]
At the Edge of the Heart: ULP FPGA-Based CNN for On-Device Cardiac Feature Extraction in Smart Health Sensors for Astronauts
arXiv:2604.25799v1 Announce Type: cross Abstract: The convergence of accelerating human spaceflight ambitions and critical terrestrial health monitoring demands is driving unprecedented requirements for reliable, real-time feature extraction on extremely resource-constrained wearable health sensors. We present an ultra-low-power (ULP) Field-Programmable Gate Array (FPGA) based solution for real-time Seismocardiography (SCG) feature classification using Convolutional Neural Networks (CNNs). […]
How Fast Should a Model Commit to Supervision? Training Reasoning Models on the Tsallis Loss Continuum
arXiv:2604.25907v1 Announce Type: cross Abstract: Adapting reasoning models to new tasks during post-training with only output-level supervision stalls under reinforcement learning from verifiable rewards (RLVR) when the initial success probability $p_0$ is small. Using the Tsallis $q$-logarithm, we define a loss family $J_Q$ that interpolates between RLVR (at $q=0$, the exploitation pole) and the log-marginal-likelihood […]