Recursive Self-Improvement Now Has a Co-Evolving Evaluator: Cambridge-NVIDIA Paper Raises the Stakes
Visitors tour the Nvidia booth during the Nvidia Product Showcase at Computex 2026 in Taipei on June 3, 2026. AFP via Getty Images/I-Hwa Cheng A preprint published June 24, 2026, by 13 researchers at ...
AbCellera has an AI-driven antibody discovery platform, $650M liquidity, and an upcoming Phase 2 catalyst. Click here to read ...
Abstract: In several applications the information is naturally represented by graphs. Traditional approaches cope with graphical data structures using a preprocessing phase which transforms the graphs ...
Retrieval-augmented generation (RAG) has emerged as a pivotal framework in AI, significantly enhancing the accuracy and relevance of responses generated by large language models (LLMs) leveraging ...
Magic-angle twisted bilayer graphene (MATBG) is a material created by stacking two sheets of graphene onto each other, with a small twist angle of about 1.1°. At this "magic angle," electrons move ...
Abstract: Knowledge Graphs (KGs), with their intricate hierarchies and semantic relationships, present unique challenges for graph representation learning, necessitating tailored approaches to ...
This article introduces a model-based design, implementation, deployment, and execution methodology, with tools supporting the systematic composition of algorithms from generic and domain-specific ...
Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Birgitta Böckeler, Distinguished Engineer at ...
While retrieval-augmented generation is effective for simpler queries, advanced reasoning questions require deeper connections between information that exist across documents. They require a knowledge ...
Hypergraph Neural Networks (HGNNs) have been significantly successful in higher-order tasks. However, recent study have shown that they are also vulnerable to adversarial attacks like Graph Neural ...
This paper introduces a novel application of spatial-temporal graph neural networks (ST-GNNs) to predict groundwater levels. Groundwater level prediction is inherently complex, influenced by various ...
In this article, we explore how Postgres, a powerful and versatile relational database, can be effectively used to model and traverse graphs and trees. While specialized graph databases exist, such as ...
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