Olmo-core 3 introduces an enhanced training infrastructure for large language models, featuring an efficient mixture-of-experts system. This upgrade significantly boosts scalability and throughput while reducing computational costs, making advanced AI model development more accessible to researchers and smaller labs.

InstructMesh is an innovative design software that enables users to easily create functional 3D models, overcoming common limitations of generative AI. Developed by a collaboration of experts, it streamlines the editing process, allowing both novices and experts to produce unique, practical items tailored to their needs.

This article explores the use of LLM-powered agents, specifically GPT-6 Astra, to play World of Warcraft for the first time. By analyzing how these agents navigate the game, the piece reveals insights into their capabilities for complex decision-making and strategic planning in a richly simulated environment.

This article discusses the importance of efficient retrieval systems for company knowledge, introducing the Company Knowledge Bench as a new benchmarking tool. It explains how the bench allows teams to evaluate their retrieval methods on real data for various use cases, highlighting the limitations of public benchmarks.

Professor Matthew Schwartz explores a new method for integrating AI in scientific research through a toolkit called BootLoops. By shifting focus to "Claude-shaped" problems, Schwartz illustrates how AI can efficiently enhance quantitative science across various fields, bridging gaps between human scientists and AI capabilities.

a clear representation of its knowledge and reasoning processes. This article explores the distinction between AlphaGo's sophisticated reasoning capabilities and the limitations of current AI, particularly large language models. It argues for a new approach that could enhance AI's reliability in critical fields like medicine and science.

TypeSafe's new classifier model, Jev, offers a novel approach to decision-making by merging transformer architecture with classification tasks. Though initial calibration results suggest Jev struggles with probability distributions, it shows promise in specific cases, like Lorentzian distributions, while raising questions about the reliability of automated evaluation methods.

SaaS businesses are evolving into "harnesses," integrating technology with human insights for enhanced productivity and quality. This article explores how these structures shift responsibilities from individuals to automated agents, thereby redefining company dynamics and maintaining differentiation through effective harness design.

AstaBrief is an open-weight language model designed for efficient scientific report generation. By focusing on evidence grounding and structure, it provides quick, cited reports tailored to researchers' needs. AstaBrief's faster generation and open accessibility encourage collaboration and customization for various scientific contexts.

Recent studies in Nature and Cell propose a new understanding of aging, combining traditional damage accumulation theories with concepts of cell identity loss. This loss may lead to mesenchymal drift, impacting various age-related diseases. The findings suggest interventions to preserve or restore cellular identity, possibly reversing age-related changes.

OpenAI's Decisions API, powered by the GPT-6 Luna model, aims to improve decision-making processes by utilizing confidence thresholds. After evaluating Luna's accuracy across various reasoning tasks, it reveals significant limitations, particularly in multi-step logic. The findings stress the importance of reliable confidence calibration in automated decision-making systems.

On September 29, 2026, the White House introduced the term "Super Intelligence" to redefine AI, but the distinction remains unclear. This article critiques current technologies, explaining the gap between today's machine learning systems and true artificial intelligence, and calls for clearer terminology that reflects what these technologies actually do.

This article compares two main approaches in autonomous driving systems: End-to-End Learning and Modular Architectures. It evaluates their strengths and weaknesses, emphasizing the potential of hybrid models. A new framework for architecture selection is proposed, guiding future improvements in autonomous driving technology.

This study examines bias in open-source large language models used for pediatric emergency triage. By conducting a counterfactual audit across various models, it reveals how demographic and contextual factors influence prediction accuracy. The findings highlight potential fairness risks, advocating for rigorous analysis before clinical use.

This article explores the mathematical reasoning capabilities of Large Language Models (LLMs). It introduces a new benchmark to assess their understanding, identifies key limitations in their reasoning processes, and proposes a framework to enhance their mathematical skills, showing improvements through targeted post-training methods.