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Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have become closed off, gated behind proprietary interfaces, with important details of their training data, architectures, and development undisclosed. Given the importance of these details in scientifically studying these models, including their biases and potential risks, we believe it is essential for the research community to have access to powerful, truly open LMs. To this end, we have built OLMo, a competitive, truly Open Language Model, to enable the scientific study of language models. Unlike most prior efforts that have only released model weights and inference code, we release OLMo alongside open training data and training and evaluation code. We hope this release will empower the open research community and inspire a new wave of innovation. 

Multiple visions of 6G networks elicit Artificial Intelligence (AI) as a central, native element. When 6G systems are deployed at a large scale, end-to-end AI-based solutions will necessarily have to encompass both the radio and the fiberoptical domain. This paper introduces the Decentralized Multi- Party, Multi-Network AI (DMMAI) framework for integrating AI into 6G networks deployed at scale. DMMAI harmonizes AI-driven controls across diverse network platforms and thus facilitates networks that autonomously configure, monitor, and repair themselves. This is particularly crucial at the network edge, where advanced applications meet heightened functionality and security demands. The radio/optical integration is vital due to the current compartmentalization of AI research within these domains, which lacks a comprehensive understanding of their interaction. Our approach explores multi-network orchestration and AI control integration, filling a critical gap in standardized frameworks for AI-driven coordination in 6G networks. The DMMAI framework is a step towards a global standard for AI in 6G, aiming to establish reference use cases, data and model management methods, and benchmarking platforms for future AI/ML solutions. 

Search for astrophysical electron antineutrinos in Super-Kamiokande with 0.01wt% gadolinium-loaded water. 

Cosmic-ray muons that enter the Super-Kamiokande detector cause hadronic showers due to spallation in water, producing neutrons and radioactive isotopes. These are a major background source for studies of MeV-scale neutrinos and searches for rare events. In 2020, gadolinium was introduced into the ultra-pure water in the Super-Kamiokande detector to improve the detection efficiency of neutrons. In this study, the cosmogenic neutron yield was measured using data acquired during the period after the gadolinium loading. The yield was found to be ð2.76  0.02ðstatÞ  0.19ðsystÞÞ × 10−4 μ−1 g−1 cm2 at an average muon energy 259 GeV at the Super-Kamiokande detector.