Ai-research

This post explores RAPTOR, a novel approach to retrieval-augmented language models that constructs a hierarchical tree structure of documents through recursive embedding, clustering, and summarization. This method enables retrieval of information at different levels of abstraction, significantly improving performance on complex question answering tasks involving long documents compared to traditional contiguous chunk retrieval.

This post walks through our real-world evaluation of Manus AI using a 1000-credit free plan to generate a comprehensive industry research report on Robot Framework. We explore report quality, token usage, time-to-output, and Manus AI's practical viability for research tasks.

Discover the definitive guide to large language models (LLMs) with the book Large Language Models: A Deep Dive. This comprehensive review explores how the book bridges cutting-edge AI theory with real-world applications, offering unparalleled insights into the latest advancements, practical use cases, and ethical considerations in the field of LLMs.

Breaking down DeepSeek-V3’s revolutionary AI architecture—exploring its key innovations, expert routing, and inference optimizations step by step. This post dives deep into the mathematics and mechanisms that power its efficiency and scalability.