Lowe’s partnered with OpenAI to build Mylow and Mylow Companion, AI-powered tools that bring expert help to both customers and store associates—making complex home improvement projects easier to plan, navigate, and complete.
A new initiative to support countries around the world that want to build on democratic AI rails.
arXiv:2505.01441v1 Announce Type: new
Abstract: Large language models (LLMs) have achieved remarkable progress in complex reasoning tasks, yet they remain fundamentally limited by their reliance on static internal knowledge and text-only reasoning. Real-world problem solving often demands dynamic, multi-step reasoning, adaptive decision making, and the ability to interact with external tools and environments. In this work, we introduce ARTIST (Agentic Reasoning and Tool Integration in Self-improving Transformers), a unified framework that tightly couples agentic reasoning, reinforcement learning, and tool integration for LLMs. ARTIST enables models to autonomously decide when, how, and which tools to invoke within multi-turn reasoning chains, leveraging outcome-based RL to learn robust strategies for tool use and environment interaction without requiring step-level supervision. Extensive experiments on mathematical reasoning and multi-turn function calling benchmarks show that ARTIST consistently outperforms state-of-the-art baselines, with up to 22% absolute improvement over base models and strong gains on the most challenging tasks. Detailed studies and metric analyses reveal that agentic RL training leads to deeper reasoning, more effective tool use, and higher-quality solutions. Our results establish agentic RL with tool integration as a powerful new frontier for robust, interpretable, and generalizable problem-solving in LLMs.
Large language models (LLMs) have made significant strides in complex reasoning tasks, but they are still constrained by their reliance on static internal knowledge and text-only reasoning. Real-world problem solving often requires dynamic, multi-step reasoning and the ability to interact with external tools and environments. In a groundbreaking new study, researchers have introduced ARTIST (Agentic Reasoning and Tool Integration in Self-improving Transformers), a unified framework that combines agentic reasoning, reinforcement learning, and tool integration for LLMs.
This multi-disciplinary approach represents a significant advancement in the field of artificial intelligence, as it allows models to make autonomous decisions on when, how, and which tools to use within multi-turn reasoning chains. By incorporating outcome-based reinforcement learning, ARTIST learns robust strategies for tool use and environment interaction without the need for step-level supervision.
The extensive experiments conducted on mathematical reasoning and multi-turn function calling benchmarks show that ARTIST outperforms state-of-the-art baselines by up to 22%, demonstrating strong gains on even the most challenging tasks. Detailed studies and metric analyses indicate that agentic RL training leads to deeper reasoning, more effective tool utilization, and higher-quality solutions.
Overall, these results establish agentic RL with tool integration as a powerful new frontier for robust, interpretable, and generalizable problem-solving in LLMs. This innovative framework not only pushes the boundaries of language models but also opens up new possibilities for AI systems to tackle complex real-world problems with agility and efficiency.
Sam Altman has written that we are entering the Intelligence Age, a time when AI will help people become dramatically more capable. The biggest problems of today—across science, medicine, education, national defense—will no longer seem intractable, but will in fact be solvable. New horizons of possibility and prosperity will open up.
John Deere’s Justin Rose talks about transforming agriculture with AI and shares how the company is scaling innovation to help farmers work smarter, more efficiently, and sustainably.
arXiv:2505.00733v1 Announce Type: new
Abstract: Autonomous robots must operate in diverse environments and handle multiple tasks despite uncertainties. This creates challenges in designing software architectures and task decision-making algorithms, as different contexts may require distinct task logic and architectural configurations. To address this, robotic systems can be designed as self-adaptive systems capable of adapting their task execution and software architecture at runtime based on their context.This paper introduces ROSA, a novel knowledge-based framework for RObot Self-Adaptation, which enables task-and-architecture co-adaptation (TACA) in robotic systems. ROSA achieves this by providing a knowledge model that captures all application-specific knowledge required for adaptation and by reasoning over this knowledge at runtime to determine when and how adaptation should occur. In addition to a conceptual framework, this work provides an open-source ROS 2-based reference implementation of ROSA and evaluates its feasibility and performance in an underwater robotics application. Experimental results highlight ROSA’s advantages in reusability and development effort for designing self-adaptive robotic systems.
The development of autonomous robots capable of operating in diverse and uncertain environments poses significant challenges in terms of software architecture and decision-making algorithms. Traditional approaches may struggle to handle the complexity of these systems, as different tasks and environmental conditions require unique logic and configurations.
ROSA, the new knowledge-based framework for RObot Self-Adaptation, addresses this issue by allowing robotic systems to adapt their task execution and software architecture in real-time based on their context. This concept not only enhances the flexibility and efficiency of robotic systems but also opens up opportunities for multi-disciplinary collaboration.
By integrating knowledge modeling and reasoning techniques, ROSA enables task-and-architecture co-adaptation (TACA) in robotic systems. This approach reflects the growing trend towards integrating diverse disciplines such as artificial intelligence, robotics, and knowledge engineering to tackle complex challenges in autonomous systems.
The open-source ROS 2-based reference implementation of ROSA provides an accessible platform for researchers and developers to explore the capabilities of self-adaptive robots. The feasibility and performance evaluation in an underwater robotics application demonstrate the practical advantages of ROSA, including reusability and reduced development effort.
Overall, the innovative approach of ROSA demonstrates the potential for advancing the field of autonomous robotics through the integration of knowledge-based systems and adaptive technologies. This work exemplifies the importance of multi-disciplinary collaboration in designing complex robotic systems capable of addressing dynamic and uncertain environments.