Existing LLM graph benchmarks typically ask models to answer graph-theoreticquestions or compute symbolic solutions rather than construct spatial layouts.Within-task difficulty is also primarily stratified by vertex count. However, existingresearch also suggests that task difficulty is more closely related to the numbe...
Anna Kravchenko, Oleksandr Nikitin· Zenodo (CERN European Organi...· 0 citations
The synthesis clearly shows that the implementation of edge AI techniques has the capability to improve global water quality through immediate pollution detection, disaster forecasting, and automatic filter or alarm response without the need for cloud infrastructure.
A. Murei, I. Kamika· Limnological Review· 0 citations
Across five preprints (doi:10.5281/zenodo.22100546, .22100826, .22101059, .22120069, .22133403), we measured how perturbational complexity behaves once its estimator is debiased, what quantity it tracks — the reproducible dimensionality of the evoked response (R-dim) — and how that quantity depends on dynamics, trial c...
Nicolás Federico Galindez· Zenodo (CERN European Organi...· 0 citations
In [1] we introduced the frustration f(S)f(S) of a sheaf of pseudometric spaces — the least consistency radius (in the sense of Robinson [3, 4]) achievable by any assignment — and proved metric local-to-global principles: holonomy displacement bounds, exact values on cycles, and rectification on acyclic covers. Prompte...
Otaviano Lucas Duarte Santos· Zenodo (CERN European Organi...· 0 citations
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A machine-readable, course-level corpus of the curricula of the universities of the United Arab Emirates, assembled from their published course catalogs. The corpus comprises twenty-two institutions across 56 catalog editions, 52,802 course records, and 33,945 parsed prerequisite relations, of which 7,030 (20.7%) are d...
Sherzod Turaev, Saja Al-Dabet, Mary John et al.· Mendeley Data· 0 citations
Superseded. This record is retired and should not be used or cited. It is replaced by 10.5281/zenodo.22234051, which is the archive accompanying the manuscript. Version v2.0.0 of this record (10.5281/zenodo.22232689) additionally contained files that should not have been distributed and its removal has been requested....
Fabrice Tiku Nyambod· Zenodo (CERN European Organi...· 0 citations
TinyHLS is a Python-based hardware compiler that automatically generates hardware accelerators in the form of hardware description language (HDL) code for Convolutional Neural Networks (CNNs). The description of the CNN architecture as well as the training is done in advance using Python TensorFlow Keras. TinyHLS reduc...
R. Gaede, I. Hoyer, H. Kappert et al.· River Publishers eBooks· 0 citations
Simulation is an indispensable tool for validating distributed IoT architectures before physical deployment, and iFogSim has emerged as one of the most widely adopted platform in the fog and edge computing research community. Yet the experience of using iFogSim for non-canonical, application-specific architectures rema...
Milliam Maxime Zekeng Ndadji· arXiv (Cornell University)· 0 citations
Turbulence in the edge and scrape‐off layer regions plays a critical role for the performance of future magnetic confinement fusion power plants. Gyrokinetic simulations allow studying this regime with high fidelity. A key aspect in these regions is the high concentration of impurities, which can radiate energy, lead...
A. Sulimro, P. Ulbl, Jordy Trilaksono et al.· Contributions to Plasma Phys...· 0 citations
The Zero-Knowledge random range proof based scheduling data protection algorithm (ZKRRP-SDPA) is introduced, which employs an enhanced zero-knowledge range proof for real-valued ranges together with digital signatures, guaranteeing the security of node attribute data against any adversary except the node itself.
Feature Mode Decomposition (FMD) adaptively decomposes signals into band-limited modes through an adaptive finite impulse response (FIR) filter bank optimized via correlated kurtosis (CK) maximization, yet its denoising performance is highly sensitive to four hyperparameters—the number of decomposition modes nm, the fi...
Xiang Ji, Lei Han, Yan Zhang· Journal of Marine Science an...· 0 citations
The numerical results show that the proposed joint computing-and-relaying scheme consistently outperforms computing-only, relaying-only, and dedicated-helper cooperation.
Yuan Zheng, Feng-Xian Tang, Dongqing Li et al.· Italian National Conference...· 0 citations
Host Sowbhagya Suresh shares some of the latest news and cutting-edge research from across Microsoft—from understanding the challenges facing coding agents to uncovering how people use AI at work and improving extended-range weather forecasting. The post Microsoft Research Focus – Edition #97 appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 28, 2026
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
For decades, a fundamental question about the difficulty of computing in high-dimensional spaces has resisted proof. New research resolves this question, removing a major theoretical obstacle to understanding the efficiency of randomized algorithms used across science and computing. The Kannan–Lovász–Simonovits (KLS) conjecture was proposed in 1995. It asks whether, for a broad class of high-dimensional […] The post Solving a 31-year-old grand challenge in mathematics – the KLS conjecture appear…
MIT News · Artificial Intelligence· news.mit.eduOct 15, 2026
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.
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