BackgroundJordan and Palestine face public health challenges due to infectious diseases, with the added detrimental factors of long-term conflict, forced relocation, and lack of resources. Added to these are the increased rates of morbidity and mortality from having limited healthcare services available due to a lack of funding, poor disease surveillance systems, and entrenched systemic […]
From pilot to policy: why AI health interventions fail to scale in developing countries
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Collaborative and Cooperative Hospital “In-House” Medical Device Development and Implementation in the AI Age: The European Responsible AI Development (EURAID) Framework Compatible With European Values
The last years have seen an acceleration in the development and uptake of artificial intelligence (AI) systems by “early adopter” hospitals, caught between the pressures to “perform” and “transform” in a struggling health care system. This transformation has raised concerns among health care providers as their voices and location-specific workflows have often been overlooked, resulting […]
Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?
arXiv:2506.10912v3 Announce Type: replace Abstract: Toxicity remains a leading cause of early-stage drug development failure. Despite advances in molecular design and property prediction, the task of molecular toxicity repair, generating structurally valid molecular alternatives with reduced toxicity, has not yet been systematically defined or benchmarked. To fill this gap, we introduce ToxiMol, the first benchmark […]
From Specialist to Generalist: Unlocking SAM’s Learning Potential on Unlabeled Medical Images
arXiv:2601.17934v2 Announce Type: replace-cross Abstract: Foundation models like the Segment Anything Model (SAM) show strong generalization, yet adapting them to medical images remains difficult due to domain shift, scarce labels, and the inability of Parameter-Efficient Fine-Tuning (PEFT) to exploit unlabeled data. While conventional models like U-Net excel in semi-supervised medical learning, their potential to assist […]
Detecting and Mitigating Memorization in Diffusion Models through Anisotropy of the Log-Probability
arXiv:2601.20642v1 Announce Type: cross Abstract: Diffusion-based image generative models produce high-fidelity images through iterative denoising but remain vulnerable to memorization, where they unintentionally reproduce exact copies or parts of training images. Recent memorization detection methods are primarily based on the norm of score difference as indicators of memorization. We prove that such norm-based metrics are […]
VLM-CAD: VLM-Optimized Collaborative Agent Design Workflow for Analog Circuit Sizing
arXiv:2601.07315v3 Announce Type: replace-cross Abstract: Analog mixed-signal circuit sizing involves complex trade-offs within high-dimensional design spaces. Existing automatic analog circuit sizing approaches rely solely on netlists, ignoring the circuit schematic, which hinders the cognitive link between the schematic and its performance. Furthermore, the black-box nature of machine learning methods and hallucination risks in large language […]
Learning Contextual Runtime Monitors for Safe AI-Based Autonomy
arXiv:2601.20666v1 Announce Type: cross Abstract: We introduce a novel framework for learning context-aware runtime monitors for AI-based control ensembles. Machine-learning (ML) controllers are increasingly deployed in (autonomous) cyber-physical systems because of their ability to solve complex decision-making tasks. However, their accuracy can degrade sharply in unfamiliar environments, creating significant safety concerns. Traditional ensemble methods aim […]
Human Values in a Single Sentence: Moral Presence, Hierarchies, and Transformer Ensembles on the Schwartz Continuum
arXiv:2601.14172v2 Announce Type: replace-cross Abstract: We study sentence-level detection of the 19 human values in the refined Schwartz continuum in about 74k English sentences from news and political manifestos (ValueEval’24 corpus). Each sentence is annotated with value presence, yielding a binary moral-presence label and a 19-way multi-label task under severe class imbalance. First, we show […]
HOMURA: Taming the Sand-Glass for Time-Constrained LLM Translation via Reinforcement Learning
arXiv:2601.10187v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved remarkable strides in multilingual translation but are hindered by a systemic cross-lingual verbosity bias, rendering them unsuitable for strict time-constrained tasks like subtitling and dubbing. Current prompt-engineering approaches struggle to resolve this conflict between semantic fidelity and rigid temporal feasibility. To bridge this gap, […]
RPO-RAG: Aligning Small LLMs with Relation-aware Preference Optimization for Knowledge Graph Question Answering
arXiv:2601.19225v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have recently demonstrated remarkable reasoning abilities, yet hallucinate on knowledge-intensive tasks. Retrieval-augmented generation (RAG) mitigates this issue by grounding answers in external sources, e.g., knowledge graphs (KGs). However, existing KG-based RAG approaches rely on semantics-unaware path sampling and are weakly aligned with KG reasoning objectives, which […]
Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models
arXiv:2601.16991v2 Announce Type: replace-cross Abstract: Adapting large pre-trained language models to downstream tasks often entails fine-tuning millions of parameters or deploying costly dense weight updates, which hinders their use in resource-constrained environments. Low-rank Adaptation (LoRA) reduces trainable parameters by factorizing weight updates, yet the underlying dense weights still impose high storage and computation costs. Magnitude-based […]