Health
1353 storiesPublic health and medicine worldwide — sourced from WHO News.
Audio-Text Cross-Attention with Psycholinguistic Support Features for Ambivalence/Hesitancy Recognition
arXiv:2607.13345v2 Announce Type: replace Abstract: We present a frame-independent audio-text system for the 3rd Ambivalence/Hesitancy Video Recognition Challenge at the 11th Affective & Behavior Analysis in-the-Wild (AB…
SeerGuard: A Safety Framework for Mobile GUI Agents via World Model Prediction
arXiv:2607.15550v3 Announce Type: replace Abstract: Mobile graphical user interface (GUI) agents have demonstrated remarkable capabilities in automating complex tasks, yet they introduce critical safety risks because a s…
Scope3Trace: Evidence-Based Identification and Extraction of Scope 3 GHG Emissions from Sustainability Reports
arXiv:2607.17122v2 Announce Type: replace Abstract: Scope 3 greenhouse gas (GHG) emissions account for the majority of corporate carbon footprints, yet remain difficult to analyze at scale due to sparse disclosures, hete…
Zero Hallucination, by Construction: Hallucination-Aware Layered Oversight for Trustworthy Enterprise AI
arXiv:2607.17883v2 Announce Type: replace Abstract: Enterprises will not deploy AI agents they cannot trust, and the most-cited reason for distrust is hallucination: confident, fluent output that is simply not true. The …
How Does Alignment Tuning Shape Representations of Sycophancy and Related Cue-Induced Biases in LLMs?
arXiv:2607.18114v2 Announce Type: replace Abstract: Modern LLMs are alarmingly susceptible to surprisingly simple immaterial changes of input prompts: a casual hint, an incorrectly labeled few-shot example, or a fake pri…
A Classifier That Teaches Itself: Self-Improving, Frozen-gate Training (SIFT) for Dynamic Document Classification
arXiv:2607.18358v2 Announce Type: replace Abstract: Document classification is a solved problem in the laboratory and an unsolved one in the enterprise. The blocker is rarely model architecture; it is the labeling projec…
Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges
arXiv:2607.19011v2 Announce Type: replace Abstract: Multimodal humor in memes, cartoons, and comics remains difficult for AI systems because intended meaning depends on non-literal mechanisms, shared cultural knowledge, …
AREX: Towards a Recursively Self-Improving Agent for Deep Research
arXiv:2607.21461v3 Announce Type: replace Abstract: Deep research requires agents to find answers that jointly satisfy multiple constraints. Discovering such answers is costly, whereas verifying a candidate can often be …
Shallower ReLU Network Representations via Exact Linear Algebra
arXiv:2607.21651v2 Announce Type: replace Abstract: We study the depth required by ReLU networks to exactly represent piecewise linear functions, focusing specifically on the maximum function. This problem has recently r…
Does Runtime Topology Context Improve LLM-Generated Kubernetes Security Patches?
arXiv:2607.25995v2 Announce Type: replace Abstract: Kubernetes is central to the cloud-native ecosystem, orchestrating containerised workloads. Recent work suggests that large language models (LLMs) can automate cluster …
UrbanDS: A Graph-Guided LLM Multi-Agent System for Data-Intensive Urban Tasks
arXiv:2607.26724v2 Announce Type: replace Abstract: Large language model (LLM) agents have been widely applied in automating data science tasks. However, existing methods typically rely on a limited set of provided datas…
S-CEReBrO: Breaking the Memory Barrier in Continuous EEG Monitoring
arXiv:2607.27913v2 Announce Type: replace Abstract: Foundation models offer a promising paradigm for Electroencephalography (EEG) analysis, leveraging generalizable representations from vast unlabeled datasets. Yet, Tran…
A Closed-Loop Thermal Dynamic Model for AI Data Center Cooling Load Simulation
arXiv:2607.28962v2 Announce Type: replace Abstract: Cooling demand constitutes a significant and flexible component of AI data center electricity consumption, but time-synchronized measurements are scarce and constant co…
Can We Trust In-Distribution Success? Locked Evaluation Reveals Transfer Failure and Sampling-Depth Entanglement in CRISPRi Perturbation Prediction
arXiv:2608.00152v3 Announce Type: replace Abstract: AI evaluation can support the wrong inference when an in-domain benchmark success does not survive distribution shift, or when the benchmark endpoint is entangled with …
Bridging the English-Arabic Medical Knowledge Gap: Targeted Low-Rank Adaptation via Causal Layer Selection
arXiv:2608.00207v2 Announce Type: replace Abstract: Large Language Models (LLMs) perform strongly in English medical tasks but degrade substantially in Arabic, a gap widely attributed to limited training data. We systema…
When Measurement Conventions Masquerade as Calibration Gains in Cardiac Digital Twins
arXiv:2608.01602v2 Announce Type: replace Abstract: Cardiac digital twins convert clinical images into physiological measurements through observation operators, yet calibration studies often assume a fixed reference conv…
Dynamic Modeling of Target Cell Location for Mobility Robustness Analysis in Cellular Networks: Technical Report
arXiv:2608.02467v3 Announce Type: replace Abstract: Mobility robustness optimization (MRO) requires an appropriate selection of handover (HO) parameters such as the time-to-trigger (TTT) and offset margin to balance HO f…
Field-Aware Agent Skill Retrieval
arXiv:2608.02880v3 Announce Type: replace Abstract: As lifelong learning agents accumulate lifelong growing skill banks, retrieving the correct skill becomes an increasingly important bottleneck. Most current skill retri…
When Oracle Conditioning Misleads Deployment: Conditioning-Availability Bias in Echocardiographic Segmentation
arXiv:2608.03342v2 Announce Type: replace Abstract: Conditional segmentation models may be trained and evaluated with auxiliary signals cleaner than those available at deployment. We study this protocol-level manifestati…
Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility
arXiv:2608.04001v2 Announce Type: replace Abstract: Large language models can solve harder reasoning problems with more inference-time compute. The term "test-time scaling," however, covers several inference algorithms: …