Geriatrics

TimeRLM: Recursive Language Models Enable Precise Anomaly Localization in Long-Context Time-Series

Precise anomaly localization over long-context time series is a crucial task in monitoring applications across clinical care, industrial operations, financial services, and logistics, where brief evidence may hide inside long spans of high-frequency data. Time-Series Language Models (TSLMs) are able to ingest time series data and verbalize findings on anomalies in natural language; however, recent...

When Do Fewer Visual Tokens Accelerate Multimodal Inference? A Break-Even Study Across Decision Locations and Hardware

Fewer visual tokens do not guarantee lower end-to-end latency. We evaluate break-even with a reproducible protocol that accounts for decision overhead, shared work, and the operators each policy can avoid. A stage-level decomposition reconciles these components with measured end-to-end latency. In a 30-example pilot, the two tested autoregressive probes remain slower than Full despite state reuse....

PatTree: a novel approach for automated creation of multimodal, graph-based patient representations for medical classification tasks

Access to holistic, multimodal data improves the performance of Artificial Intelligence (AI) in medical classification tasks compared to utilizing sin...

GoT-CD: Graph-of-Thoughts Causal Discovery and the Fragility of Post-hoc Path-Specific Fairness Audits

Causal discovery recovers directed structure from observational data and is increasingly used in clinical settings to support mechanism reasoning and ...

The aging rhythm: spatio-temporal dynamics of resting alpha oscillations in young and older brains

Aging is associated with substantial alterations in brain oscillatory activity, particularly within the alpha band (8 -12 Hz). Yet, little is known ab...

DeepVoyager-VL: Incentivizing Vision-in-the-Loop Search for Long-Horizon Multimodal Agents

Multimodal large language models (MLLMs) have advanced visual understanding and reasoning, yet their static parametric knowledge limits their ability ...

Fermat Active Laplace Learning for Semi-Supervised Hyperspectral Image Classification

Two active learning algorithms for hyperspectral image (HSI) classification are proposed that combine density-aware Fermat distances with Poisson-rewe...

CAPEval: A Decoupled Caption Evaluation across Understanding and Generation

Captions serve as a primary supervision signal for both multimodal understanding and text-to-image generation. However, previous evaluations treat the...

Remember-R1: Mitigating Long-Context Visual Forgetting through Reinforcement Learning

Multimodal large language models (MLLMs) increasingly rely on long chain-of-thought reasoning for complex tasks. However, as reasoning sequences lengt...

Long-Horizon Embodied Decision-Making via Multimodal Memory Compression

Agents are increasingly expected to act not only as task executors, but also as decision-makers on behalf of human users. This shift requires agents t...

A PRISMA-Aligned Agentic Framework for Medical Systematic Reviews and Evidence Synthesis

Medical systematic reviews are central to evidence-based medicine, but they remain slow, labor-intensive, and difficult to maintain under the full Pre...

What Is Missing in Surgical Risk Stratification and Outcome Prediction: A Scoping Review of End-to-End Machine Learning Approaches

Postoperative adverse events, including mortality and morbidity, remain a major global burden, many of which are preventable through early identificat...

On the Efficacy of Self-Supervised Point Cloud Encoders for Efficient 3D Large Language Models

3D point cloud-language models (3D-LLMs) enable 3D understanding by pairing point cloud encoders with large language models, but existing methods rely...

End-to-End Fairness Optimization with Fair Decision-Focused Learning

Many real-world systems rely on predictive models to inform decisions, and fairness concerns arise in both the prediction and decision stages. We intr...

A Neurosymbolic Approach for Explainable Early Diagnosis of Alzheimer's Disease

Identifying reliable Alzheimer's disease (AD) markers typically requires manual, labor-intensive transcription and expert analysis, limiting its scale...

A Supervised Text-Embedded Transformer Matching Model to Detect Fall Injuries in Medicare Data

Objective: To develop and validate a supervised text-embedded transformer matching model to identify fall injuries in Medicare data, and evaluate the ...

CENO: A Genome-Scale World Model for Evolutionary Sequence Interpretation and Programmable Regulatory Design

DNA encodes biological function across a continuum of sequence scales, from single-nucleotide and motif-level grammar to regulatory neighborhoods, chr...

Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation

The deep learning revolution, kicked off by AlexNet, taught us that end-to-end training beats decomposing a problem into hand-designed stages. Generat...

Generalizability of EEG-Based Dementia Classifiers: A Multicenter study of Alzheimer, MCI, and FTD

EEG-based machine learning shows promise for neurodegenerative disease classification, but robustness to sample imbalance, center heterogeneity, and v...

An Attention-Based Framework for Alzheimers Disease Classification Using Resting-State fMRI

Accurate identification of Alzheimers disease (AD) using resting-state functional magnetic resonance imaging (rs-fMRI) remains challenging due to the ...