Surveys

Mind the Rift: Cross-Scale Coupling Mismatch for AI-Generated Video Detection

As AI video generators achieve cinematic realism, reliable detection becomes essential for safeguarding digital trust. We identify cross-scale coupling mismatch as a new forensic signal, where scale refers to the level of abstraction (semantic dynamics vs. pixel-level residuals): in natural videos, macro-level temporal dynamics and micro-level residual patterns are intrinsically coupled by the uni...

ExBind: A Controlled Diagnostic Benchmark for Visual-to-Executable Correspondence

Multimodal coding and editing systems must map a visible or semantic referent to the exact executable object that can be edited. A wrong reference may select a valid but incorrect DOM node, SVG element, graph endpoint, hierarchy member, or table cell, while final execution success alone does not reveal the source of the failure. ExBind isolates this visual-to-executable correspondence layer as a c...

CRAD: Class-wise Reliability-Aware Distillation for Decentralized Heterogeneous Federated Learning

Conventional federated learning (FL) relies on parameter averaging, which forces clients to be doubly homogeneous: it demands an identical architectur...

CHARMS and PROBAST+AI: an updated template for Data Extraction and Risk of Bias Assessment in systematic reviews of prediction models

Background: Systematic reviews of clinical prediction models increasingly include studies using artificial intelligence (AI) and machine learning (ML)...

Machine Learning-Based Prediction of Maternal Morbidity across Heterogeneous Populations in the United States using Sequential Modeling of the All of Us Dataset

In this work, we demonstrate the unprecedented value of NIH's "All of Us Research Program" (AoURP) dataset in studying maternal morbidity and building...

Uncertainty of Vision Medical Foundation Models

Accurate uncertainty estimation is essential for machine learning systems de- ployed in high-stakes domains such as medicine. Traditional approaches p...

UFPR-PEs: A Brazilian Face Recognition Benchmark with Self-Declared Race/Color Labels

While face recognition systems are widely deployed, ensuring their demographic reliability and robustness under uncontrolled visual conditions remains...

Learning Dynamics of Logits Debiasing for Long-Tailed Semi-Supervised Learning

Long-tailed distributions are prevalent in real-world semi-supervised learning (SSL), where pseudo-labels tend to favor majority classes, leading to d...

Guardrail-Agnostic Societal Bias Evaluation in Large Vision-Language Models

We propose a societal bias evaluation method for large vision-language models (LVLMs) in the era of strong safety guardrails. Existing benchmarks rely...

ContextBias: Controlled Evaluation of Bias Persistence Under Context Shift in Text-to-Image Models

Text-to-image models learn associations between concepts - in the case of this paper, people's professions, which we refer to as roles - and visual at...

Can SMILES be fragmented into a concatenable ordered sequence of retrosynthetically interesting string block ?

Molecules generated by deep learning models are often difficult to synthesize. Their synthetic accessibility can be improved with automated retrosynth...

Surgical Video Generation From Diffusion to World Models: A Survey

Surgical video data provides the primary training resource for models of intraoperative perception, surgical workflow understanding, and robotic decis...

NeoTriFuse: Reliability-Aware Multimodal Fusion under Missingness Heterogeneity for Neonatal Mortality Risk Prediction

Neonatal mortality risk prediction from bedside monitoring data remains challenging due to extreme class imbalance, heterogeneous clinical risk factor...

GGSS: Geodesic-Gated Spherical Steering for Inference-Time Debiasing of Generative Vision-Language Models

Generative vision-language models (VLMs) are increasingly used in human-centered settings, yet they can produce demographically biased outputs even wh...

Controlling for Omitted Variable Bias in Deep Neural Networks

Control variables are widely used in statistical modelling to account for omitted variable bias of known confounders. However, they have largely been ...

Continually learning neural-operator surrogate for three-dimensional airborne electromagnetic Bayesian inversion

Three-dimensional probabilistic inversion of time-domain airborne electromagnetic (AEM) data is limited by the cost of the forward solve. Even though ...

Boot-and-Feedback Framework for Generalist-Expert Model Collaboration in Breast Ultrasound Diagnosis

Breast ultrasound (BUS) is widely used for breast cancer diagnosis yet remains operator-dependent. While deep learning shows promise, ensuring diagnos...

OmicSync: Reliability-Aware Spatial Multi-Omics Clustering with Evidence-Constrained LLM Reasoning

Spatial multi-omics technologies jointly profile gene expression, surface proteins, and histology at each tissue spot, yet most spatial domain discove...

Developing an open-source framework for LLM evaluation of patients using EHR clinical documentation; performance of LLMs relative to medical professionals

Background: Large language models (LLMs) have shown increasing capability in medical knowledge tasks, yet how they perform in extracting structured cl...

Reliability and disease sensitivity are dissociable properties of EEG foundation-model representations

Abstract EEG foundation models (EEG-FMs) are evaluated almost entirely on disease-discrimination accuracy. A clinical biomarker additionally requires ...