Bristol Myers Squibb is warming up for the FIFA World Cup 2026 with a new media campaign, launching its “Won’t Lose” ad ahead of the soccer community arriving in the U.S. for this summer’s tournament.
Pfizer, Arvinas join SERD club with Veppanu approval
Arvinas and Pfizer’s Veppanu is the first PROTAC – and third oral SERD for breast cancer – to be approved by the FDA.
Big data integration for enhanced epidemiological research: insights and directions from NHLBI’s workshop
The landscape of epidemiological research is experiencing a technological transformation, driven by the rapid expansion of big data and advancements in artificial intelligence (AI) and machine learning (ML). This workshop explored the opportunities and challenges associated with integrating diverse data sources into population-based research at different levels, including electronic health records (EHRs), genomic and omics […]
Trustworthy intelligent rooms: integrating blockchain, federated learning, and data-centric AI for healthcare 4.0
IntroductionIntelligent room systems are experiencing a surge in demand within the Healthcare 4.0 ecosystem. The integration of Federated Learning (FL) and Data-Centric AI has led to substantial enhancements in the predictive capabilities of machine learning models while maintaining data privacy. However, centralized aggregation in FL remains a single point of failure and is vulnerable to […]
AI-driven mental health decision support linked to clinician resilience and preparedness
ObjectivesMental health services are facing unprecedented demand, placing significant pressure on clinicians to conduct timely and effective patient assessments. Rising staff turnover and burnout threatens service quality across many countries. This study examined whether providing clinical information, collected via an artificial intelligence (AI)—enabled decision support tool for mental health assessments in the UK’s National Health […]
Effectiveness of the mobile application Holidaily in reducing work-related rumination when returning to work after vacation: a randomized controlled trial
BackgroundVacations reliably improve indicators of mental health, largely by providing relief from work-related stress. Low levels of work-related rumination, a key transdiagnostic factor linked to burnout and depression, are considered prerequisites for successful recovery both during vacations and in daily working life. However, such benefits are typically short-lived, with a rapid “fade-out” upon return to […]
Rethinking Network Topologies for Cost-Effective Mixture-of-Experts LLM Serving
arXiv:2605.00254v1 Announce Type: cross Abstract: Mixture-of-experts (MoE) architectures have turned LLM serving into a cluster-scale workload in which communication consumes a considerable portion of LLM serving runtime. This has prompted industry to invest heavily in expensive high-bandwidth scale-up networks. We question whether such costly infrastructure is strictly necessary. We present the first systematic cross-layer analysis […]
AlphaInventory: Evolving White-Box Inventory Policies via Large Language Models with Deployment Guarantees
arXiv:2605.00369v1 Announce Type: cross Abstract: We study how large language models can be used to evolve inventory policies in online, non-stationary environments. Our work is motivated by recent advances in LLM-based evolutionary search, such as AlphaEvolve, which demonstrates strong performance for static and highly structured problems such as mathematical discovery, but is not directly suited […]
BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs
arXiv:2605.00422v1 Announce Type: cross Abstract: Large language models (LLMs) have driven major progress in NLP, yet their substantial memory and compute demands still hinder practical deployment. Binarization can compress weights to 1 bit, fundamentally lowering compute and bandwidth cost. However, existing methods cannot address activation heavy tails and thus must keep activations in high precision, […]
DynamicPO: Dynamic Preference Optimization for Recommendation
arXiv:2605.00327v1 Announce Type: cross Abstract: In large language model (LLM)-based recommendation systems, direct preference optimization (DPO) effectively aligns recommendations with user preferences, requiring multi-negative objective functions to leverage abundant implicit-feedback negatives and sharpen preference boundaries. However, our empirical analyses reveal a counterintuitive phenomenon, preference optimization collapse, where increasing the number of negative samples can lead […]
Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration
arXiv:2605.00310v1 Announce Type: cross Abstract: Super-resolution (SR) techniques have made major advances in reconstructing high-resolution images from low-resolution inputs. The increased resolution provides visual enhancement and utility for monitoring tasks. In particular, SR has been increasingly developed for satellite-based Earth observation, with applications in urban planning, agriculture, ecology, and disaster response. However, existing SR studies […]
Hypergraph and Latent ODE Learning for Multimodal Root Cause Localization in Microservices
arXiv:2605.00351v1 Announce Type: cross Abstract: Root cause localization in cloud native microservice systems requires modeling complex service dependencies, irregular temporal dynamics, and heterogeneous observability data. We present HyperODE RCA, a unified framework that combines hypergraph attention learning, latent ordinary differential equations, and multimodal cross attention fusion for fine grained root cause analysis. The method learns […]