Breast arterial calcification (BAC) is a manifestation of medial arterial calcification frequently detected incidentally on screening mammography. Increasing evidence suggests that BAC may also serve as a marker of cardiovascular risk. This narrative review critically synthesizes the evidence on artificial intelligence...
Alexandru Cărăuleanu, Amelian Mădălin Bobu, Elena Porumb‐Andrese et al.· Medical Sciences· 0 citations
The integration of Artificial Intelligence (AI) in educational settings creates pedagogical opportunities while raising environmental concerns regarding data center energy consumption and physical infrastructure footprint. This systematic review synthesizes scientific evidence on the algorithmic carbon footprint of AI...
Verónica Vera Vera, Sonnya Patricia Mendoza Lombana, Ana Gabriela Balseca Vaca et al.· Sustainability· 0 citations
Together, these results show how readout scaling, the data distribution, and training shape sparse learning, and RC-DS provides an information-preserving method for studying and controlling the readout's contribution.
This work introduces Argument Graph Uncertainty, a post-hoc framework for multiple choice question tasks that estimates model confidence directly from the logical structure of a single chain-of-thought reasoning trace, and demonstrates through extensive combination analyses that argument graph uncertainty captures an o...
S. Baur, Sumire Honda, Wojciech Samek et al.· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
STP-GUI (Spatially-aware Token Pruning for GUI agents), a query-guided token pruning framework that preserves fine-grained instruction-conditioned token scoring while performing spatially coherent window-level selection under aggressive token reduction is proposed.
Shuo Wang, Si-Fan Zhou, Xiao-Bo Wang et al.· 0 citations
This work introduces VisualWorldBench, a taxonomy-grounded multi-task benchmark built on a hierarchical taxonomy of 21,291 leaf-node classes across 8 visual domains, with 32,541 evaluation instances whose ground-truth labels cover 15,479 categories across four complementary tasks.
XViewRank is presented, a lightweight retrieval-side method for conditionally fusing BM25 results from an original query, a hypothesis view, and a step-back view, and the results support overlap-conditioned score fusion as a compact retrieval-side alternative to fixed fusion, while also delimiting where its advantage i...
This work establishes context-scaling laws that differentiate engine-level optimisations from inherent model constraints, providing an empirical framework for sustainable LLM deployment.
DarKnight, a self-improving agentic framework designed to run on a single workstation GPU inside an investigator's Tor crawler and solve CAPTCHAs with small, locally hosted open-weight VLMs that no provider decision can withdraw, without per-market training or third-party services is presented.
Bing-Hui Wu, Yeo Zi Yi, Darren Tan et al.· 0 citations
Experimental results demonstrate that when fine-tuning with only a small set of high-value instruction data, CTI-TDS outperforms existing data selection methods and achieves competitive performance on threat intelligence analysis tasks.
Ji-Yu Tian, Ming-Chu Li, Xiang-Wei Chen et al.· 0 citations
A novel cognitively-inspired hybrid model is presented which optimally trades off simulation and reasoning to improve model performance and computational efficiency and draws a parallel with this to resource-rational theories of human cognition, which propose that people optimally trade off simulation and reasoning dep...
Ying-Qiao Wang, Eric J. Bigelow, Tomer D. Ullman et al.· 0 citations
A decision model for choosing LLMs in agentic workflows is developed, showing that each resource has a crossover task length at which a smaller, less reliable model ceases to be the greener, cheaper or faster option.
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Microsoft Research Blog· microsoft.comOct 15, 2026
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
