Abstract: General Text-to-3D (GT23D) generation is crucial for creating diverse 3D content across objects and scenes, yet it faces two key challenges: 1) ensuring semantic consistency between input ...

Supports execution-based metrics, multiple ground truths, LLM-as-judge, and rich error analysis. Text-to-SQL systems aim to translate natural language questions into ...

Every data engineering team right now is being asked the same question: "How do we build a chatbot that talks to our data?" The prototypes are deceptively simple. A developer connects GPT-5.1 to a ...

A team of AI researchers at Bloomberg have developed PExA, an agentic framework that achieved 70.2% execution accuracy, sharing one of the top positions on the Spider 2.0 (Snow) leaderboard, one of ...

Semantic SEO helps search engines understand context. Learn how to use entities, topics, and intent to build richer content that ranks higher. Semantic SEO aims to describe the relationships between ...

Workflow is still at the heart of the new framework. Building on the strengths of the Semantic Kernel and AutoGen agent implementations, the new framework offers support for workflow orchestration and ...

In this tutorial, we build an advanced AI agent using Semantic Kernel combined with Google’s Gemini free model, and we run it seamlessly on Google Colab. We start by wiring Semantic Kernel plugins as ...

While data warehouses and cloud-native architectures have grown rapidly, business users are still finding it difficult to reach enterprise data. Even as dashboards and query tools become easier to use ...

A new SQL Server 2025 feature lets organizations run vector-based semantic searches on their own data, connecting to local or cloud-hosted AI models without relying on massive general-purpose LLMs. I ...

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