Nimble claims its new, domain-specialized Web Search Agents cut token costs in half while boosting retrieval accuracy

They combine proprietary indexes with real-time web retrieval and task-specific search policies rather than applying the same ...
PNAS

Spin-informed universal graph neural networks for simulating magnetic ordering

The development of universal machine-learning interatomic potentials capable of simulating magnetic ordering is vital for the in silico discovery of indispensable magnetic materials across vast ...
IEEE

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Abstract: Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly ...
GitHub

GOOD: A Graph Out-of-Distribution Benchmark

GOOD (Graph OOD) is a graph out-of-distribution (OOD) algorithm benchmarking library depending on PyTorch and PyG to make develop and benchmark OOD algorithms easily. Currently, GOOD contains 11 ...
Frontiers

Body Representations, Peripersonal Space, and the Self: Humans, Animals, Robots

The human self has many facets: there is the physical body and then there are different concepts or representations supported by processes in the brain such as the ecological, social, temporal, ...
GitHub

Graph-Native Infrastructure for Context and Accountable AI Systems

Ingest your enterprise data, extract what matters, build a Context Graph and knowledge graph (KG), and run graph analytics and causal reasoning over all of it, with full decision provenance baked in.
Frontiers

GRETNA: a graph theoretical network analysis toolbox for imaging connectomics

Recent studies have suggested that the brain’s structural and functional networks (i.e., connectomics) can be constructed by various imaging technologies (e.g., EEG/MEG; structural, diffusion and ...