Maintaining comprehensive and up-to-date knowledge graphs (KGs) is critical for modern AI systems, but manual curation struggles to scale with the rapid growth of scientific literature. This paper presents KARMA, a novel framework employing multi-agent large language models (LLMs) to automate KG enrichment through structured analysis of unstructured text. Our approach employs nine collaborative agents, spanning entity discovery, relation extraction, schema alignment, and conflict resolution that iteratively parse documents, verify extracted knowledge, and integrate it into existing graph structures while adhering to domain-specific schema. Experiments on 1,200 PubMed articles from three different domains demonstrate the effectiveness of KARMA in knowledge graph enrichment, with the identification of up to 38,230 new entities while achieving 83.1\% LLM-verified correctness and reducing conflict edges by 18.6\% through multi-layer assessments.

When we launched the AI 50 almost five years ago, I wrote, "Although artificial general intelligence (AGI)… gets a lot of attention in film, that field is a long way off." Today, that sci-fi future feels much closer. The biggest change has been the rise of generative AI, and particularly the use of transformers (a type of neural network) for everything from text and image generation to protein folding and computational chemistry. About a third of this year's companies use generative AI in some way. Generative AI, which refers to AI that processes huge amounts of data in order to create something completely original, is not new.

Nvidia Senior Director of Enterprise David Hogan spoke at this year's AI Expo about how the company is seeing artificial intelligence adoption making an impact. In the keynote session, titled'What is the true adoption of AI', Hogan provided real-world examples of how the technology is being used and enabled by Nvidia's GPUs. But first, he highlighted the momentum we're seeing in AI. "Many governments have announced investments in AI and how they're going to position themselves," comments Hogan. "Countries around the world are starting to invest in very large infrastructures." The world's most powerful supercomputers are powered by Nvidia GPUs.

In real-life temporal scenarios, uncertainty and preferences are often essential and coexisting aspects. We present a formalism where quantitative temporal constraints with both preferences and uncertainty can be defined. We show how three classical notions of controllability (that is, strong, weak, and dynamic), which have been developed for uncertain temporal problems, can be generalized to handle preferences as well. After defining this general framework, we focus on problems where preferences follow the fuzzy approach, and with properties that assure tractability. For such problems, we propose algorithms to check the presence of the controllability properties. In particular, we show that in such a setting dealing simultaneously with preferences and uncertainty does not increase the complexity of controllability testing. We also develop a dynamic execution algorithm, of polynomial complexity, that produces temporal plans under uncertainty that are optimal with respect to fuzzy preferences.