This research conducts a set of experiments using open-source Large Language Models with small parameters hosted on local servers to screen titles and abstracts against pre-defined eligibility criteria respective to each topic, and suggests that AI may indeed aid with initial relevance screening, but more research is n...
Min-Ji Kang, Bipasha Banerjee, C. Comer· Proceedings of the 26th ACM/...· 0 citations
Results show that Synthetic UAV benchmark trust is conditional: semantic coverage is insufficient, and validity must be established empirically for the task, operating regime, and model panel of interest.
The authors' quantitative MRI-to-structured reporting pipeline, integrating quantitative MRI software with large language models with LLM-based conclusion generation, might be feasible for automated structured drafting of neurodegenerative imaging markers.
Ji-Su Ko, Wooseok Jung, Pae Sun Suh et al.· Academic Radiology· 0 citations
The findings show that the response lies at the level of how the unit organizes education rather than with individual patients, indicating that the response lies at the level of how the unit organizes education rather than with individual patients.
R. Mostoles, Dolores I. Cabansag, Grace Ann Lim Lagura et al.· Journal of Nursing Managemen...· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
A model trained on readers'highlights can show a newcomer where earlier readers converged on a page, even on pages with few readers. A year of stronger language models suggests two upgrades: use them directly, or use them as teachers. We compare both with a third, more reader data, on one social highlighting platform,...
Code-switching (CS), the alternation between languages within an utterance, is common in multilingual speech but underrepresented in pretraining corpora. Increasing CS coverage requires finding relevant utterances in large speech collections so that they can be included in training data. One way to find such utterances...
Jie Chi, Zakaria Aldeneh, Masha Fedzechkina et al.· 0 citations
Machine unlearning for large language models seeks to remove specified knowledge while preserving general capability, yet current methods often treat an entire response as the unit of forgetting. This coarse granularity causes unnecessary utility loss when the forget target is only a small set of sensitive facts embedd...
Jing-Yue Cong, Anyang Song, Yang Yu et al.· 0 citations
Bengali has no grammatical gender, but honorificity is obligatory: every third-person reference must choose ordinary or respectful register. English has no such signal, so the system must choose a level of deference that the source never specified. We use this property to measure how large language models (LLMs) alloca...
Ajwad Abrar, Md. Kamrul Hasan, Hasan Mahmud· 0 citations
Pretraining generalist Earth observation foundation models requires data that combine several sensors, varied temporal sampling and, increasingly, text, yet open datasets rarely provide all three in one consistent, easily accessible form. We introduce MajorTOM:Elliot Pretrain, a collection of 279,166 tiles of 10 km dra...
C. Huidobro, Mikolaj Czerkawski, K. Monsálvez-Pozo et al.· 0 citations
Watermarking embeds an imperceptible signal in text from large language models that is statistically detectable with a secret key. The recent textGrain watermark (Li et. al. (2026b)) calibrates this signal against the model's sampling entropy through an explicit entropy budget, combining the stochasticity of Red-Green...
Speculative Decoding (SD) has demonstrated its efficacy in accelerating large language model inference, but for Vision-Language Models (VLMs), the diverse input modalities make it difficult for a small trained drafter to achieve consistently high throughput. Self-speculative decoding reuses parts of the target model as...
Yu-Qi Huang, Xing-Yao Li, Yun-Long Hou et al.· 0 citations
Kronecker-preconditioned optimizers recompute a cached curvature eigendecomposition only periodically, and implementations and adaptive methods control this through the refresh frequency. We show that frequency leaves part of a schedule free: at a fixed number of refreshes, when they occur changes held-out loss at the...
Xin-Yu Wang, Zhi-Wei Zhai, Yu Gu et al.· 0 citations
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.
