DeepSeek V4 is released. Compare V3 vs V4-Pro vs V4-Flash on confirmed specs, benchmarks, and API pricing — no speculation, only real data from the April 2026 launch.

The rapid advancement of three-dimensional (3D) computational technologies has led to the emergence of highly specialized platforms such as SpatialLM and Virtway, each addressing distinct challenges in spatial cognition and immersive virtual environments. This analysis offers a rigorous comparative study of these two systems, scrutinizing their underlying architectures, functional capabilities,

Cache-Augmented Generation (CAG) and Retrieval-Augmented Generation (RAG) constitute two distinct paradigms for augmenting large language models (LLMs) with external knowledge. While both frameworks are designed to enhance response fidelity and contextual relevance, they differ fundamentally in their architectural implementations, computational trade-offs, and optimal deployment scenarios. This article provides a rigorous

Retrieval-Augmented Generation (RAG) represents a sophisticated AI paradigm that synthesizes document retrieval methodologies with generative AI, enabling nuanced, contextually enriched outputs. When integrated into Excel, RAG facilitates enhanced data interrogation and semantic inference within structured datasets. This guide systematically explores the theoretical underpinnings of RAG, its functional application within Excel,