«`html Grounding Medical AI in Expert-Labeled Data: A Case Study on PadChest-GR A Multimodal Radiology Breakthrough Recent advances in medical AI have underscored that breakthroughs hinge not solely on model sophistication, but fundamentally on the quality and richness of the underlying data. This case study spotlights a pioneering collaboration among Centaur.ai, Microsoft Research, and the… →

«`html How to Build a Multi-Round Deep Research Agent with Gemini, DuckDuckGo API, and Automated Reporting In this tutorial, we will design a modular deep research system that operates directly on Google Colab. The system will utilize Gemini as the core reasoning engine, integrate DuckDuckGo’s Instant Answer API for lightweight web searches, and orchestrate multi-round… →

«`html Australia’s Large Language Model Landscape: Technical Assessment Understanding the Target Audience The target audience for this assessment includes AI researchers, business leaders, policymakers, and academic professionals in Australia. Their pain points involve the reliance on international large language models (LLMs) that often lack alignment with Australian English and cultural nuances. Additionally, they seek solutions… →

Nous Research Team Releases Hermes 4: A Family of Open-Weight AI Models with Hybrid Reasoning Nous Research has introduced Hermes 4, a family of open-weight models (14B, 70B, and 405B parameter sizes based on Llama 3.1 checkpoints) that achieves frontier-level performance through post-training techniques. Hermes 4 features hybrid reasoning, allowing models to switch between standard… →

«`html A Coding Implementation of Quantum State Evolution, Decoherence, and Entanglement Dynamics using QuTiP In this advanced QuTiP tutorial, we explore the rich dynamics of quantum systems using Python and the QuTiP framework. We’ll begin by preparing fundamental single- and two-qubit states, including Bell pairs, and then move on to implement key quantum operations such… →

«`html What is Agentic RAG? Use Cases and Top Agentic RAG Tools (2025) What is Agentic RAG? Agentic RAG combines the strengths of traditional retrieval-augmented generation (RAG) with agentic decision-making and tool use. Unlike static approaches, agentic RAG features AI agents that orchestrate retrieval, generation, query planning, and iterative reasoning. These agents autonomously choose data… →

«`html Meta AI Introduces DeepConf: First AI Method to Achieve 99.9% on AIME 2025 with Open-Source Models Using GPT-OSS-120B Understanding the Target Audience The target audience for DeepConf primarily includes AI researchers, business leaders in technology, and data scientists interested in enhancing AI reasoning capabilities. Their pain points often revolve around the inefficiencies and computational… →