Sakana AI introduces a novel framework for reasoning language models (LLMs) with a focus on efficiency and reusability: Reinforcement-Learned Teachers […]
Category: Staff
New AI Framework Evaluates Where AI Should Automate vs. Augment Jobs, Says Stanford Study
Redefining Job Execution with AI Agents AI agents are reshaping how jobs are performed by offering tools that execute complex, […]
Do AI Models Act Like Insider Threats? Anthropic’s Simulations Say Yes
Anthropic’s latest research investigates a critical security frontier in artificial intelligence: the emergence of insider threat-like behaviors from large language […]
VERINA: Evaluating LLMs on End-to-End Verifiable Code Generation with Formal Proofs
LLM-Based Code Generation Faces a Verification Gap LLMs have shown strong performance in programming and are widely adopted in tools […]
Building Production-Ready Custom AI Agents for Enterprise Workflows with Monitoring, Orchestration, and Scalability
In this tutorial, we walk you through the design and implementation of a custom agent framework built on PyTorch and […]
EmbodiedGen: A Scalable 3D World Generator for Realistic Embodied AI Simulations
The Challenge of Scaling 3D Environments in Embodied AI Creating realistic and accurately scaled 3D environments is essential for training […]
Google Researchers Release Magenta RealTime: An Open-Weight Model for Real-Time AI Music Generation
Google’s Magenta team has introduced Magenta RealTime (Magenta RT), an open-weight, real-time music generation model that brings unprecedented interactivity to […]
DeepSeek Researchers Open-Sourced a Personal Project named ‘nano-vLLM’: A Lightweight vLLM Implementation Built from Scratch
The DeepSeek Researchers just released a super cool personal project named ‘nano-vLLM‘, a minimalistic and efficient implementation of the vLLM […]
Why Apple’s Critique of AI Reasoning Is Premature
The debate around the reasoning capabilities of Large Reasoning Models (LRMs) has been recently invigorated by two prominent yet conflicting […]
Texas A&M Researchers Introduce a Two-Phase Machine Learning Method Named ‘ShockCast’ for High-Speed Flow Simulation with Neural Temporal Re-Meshing
Challenges in Simulating High-Speed Flows with Neural Solvers Modeling high-speed fluid flows, such as those in supersonic or hypersonic regimes, […]