SM-GNN prunes multi-view GNNs to pure propagation, cutting training time while outperforming prior MKGC accuracies on two multilingual datasets via joint KGC and alignment loops.

Research team cuts shadow-tomography sample cost to ε^(−3) by seeding FTRL with a rough state guess ϱ, halving measurements when ϱ sits within trace distance Θ(ε) of the true ρ.

PyABV injects high-performance assertion engines into PyRTL, doubling verification coverage on complex HADM designs while sustaining RTL simulation speed for both academia and industry.

 Recent survey delivers the first systematic map of LLM tool-learning, dissecting why tools supercharge models and how four-stage workflows (plan→select→call→respond) perform, while cataloging benchmarks and open challenges to jump-start newcomers.

SharpSMT first fuses DPLL(T) polytope enumeration with new factorization, variable-elimination and caching pre-processing, cutting runtime on instances from program-analysis benchmarks by orders of magnitude.

 SFDS introduces the first bilateral attribute-based access control and puncturable-encryption key update that eliminates the trusted KGC, cutting key-generation and decryption costs while blocking unauthorized uploads and downloads.

 Research team unveils ProSyno, a context-free prompt-learning model that taps Wiktionary descriptions and a dynamic matching encoder to spot synonyms across domains without KGs or corpora, setting new benchmarks on four datasets.

Researchers at the Department of Energy’s Oak Ridge National Laboratory are pioneering the design and synthesis of quantum materials, which are central to discovery science involving synergies with quantum computation. These innovative materials, including magnetic compounds with honeycomb-patterned lattices, have the potential to host states of matter with exotic behavior.