When a worker thread completes a task, it doesn't return a sprawling transcript of every failed attempt; it returns a compressed summary of the successful tool calls and conclusions.

The first entangling stage produces the dominant QFI increase, while additional stages yield diminishing returns. Entanglement primarily amplifies cross-parameter correlations rather than individual ...

Rand Fishkin just published the most important piece of primary research the AI visibility industry has seen so far. His conclusion – that AI tools produce wildly inconsistent brand recommendation ...

Insomnia disorder (ID) is neurobiologically heterogeneous and often eludes characterization by traditional group-level neuroimaging. Subtyping based on neuroimaging and clinical data offers a ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

Machine learning is increasingly applied in environmental chemistry for contaminant screening and property prediction, yet consistent benchmarks are lacking. We compared eight graph neural networks ...

Graph database provider Neo4j Inc. today announced that it will invest $100 million to accelerate its role as what it calls the “default knowledge layer” for agentic systems and generative artificial ...

This contribution is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected in 2024. Many real-world networks change dynamically but can be notoriously ...

The methodology presented here is described in depth in Kalirad, A., & Sommer, R. J. (2025). Ecological graph theory: Simulating competition and coexistence on graphs ...

Abstract: Vision Graph Neural Network (ViG) is the first graph neural network model capable of directly processing image data. The community primarily focuses on the model structures to improve ViG's ...

Abstract: We study a family of random graph models - termed subgraph generated models (SUGMs) - initially developed by Chandrasekhar and Jackson in [1] in which higher-order structures are explicitly ...