From GPT-2 to gpt-oss: Analyzing the Architectural Advances
And How They Stack Up Against Qwen3
Can doctors trust AI diagnostic tools enough to delegate tasks?
Towards physician-centered oversight of conversational diagnostic AI
Can seeing the document like a human dramatically boost a RAG system’s IQ?
Vision-Guided Chunking Is All You Need: Enhancing RAG with Multimodal Document Understanding
The Big LLM Architecture Comparison
From DeepSeek-V3 to Kimi K2: A Look At Modern LLM Architecture Design
Can AI reconstruct super-slow-motion 4D models from regular speed multi-camera video?
4DSloMo: 4D Reconstruction for High Speed Scene with Asynchronous Capture
LLM Research Papers: The 2025 List (January to June)
A topic-organized collection of 200+ LLM research papers from 2025
Understanding and Coding the KV Cache in LLMs from Scratch
KV caches are one of the most critical techniques for efficient inference in LLMs in production.
Coding LLMs from the Ground Up: A Complete Course
Why build LLMs from scratch? It's probably the best and most efficient way to learn how LLMs really work. Plus, many readers have told me they had a lot of fun doing it.
The State of Reinforcement Learning for LLM Reasoning
Understanding GRPO and New Insights from Reasoning Model Papers
First Look at Reasoning From Scratch: Chapter 1
Welcome to the next stage of large language models (LLMs): reasoning. LLMs have transformed how we process and generate text, but their success has been largely driven by statistical pattern recognition. However, new advances in reasoning methodologies now enable LLMs to tackle more complex tasks, such as solving logical puzzles or multi-step arithmetic. Understanding these methodologies is the central focus of this book.