GPT-6 Astra, Looped Transformers, and Hidden Reasoning
探討遞迴深度、隱藏推理與迴圈…
作者 Sebastian Raschka, PhD
Intel · 來源
Ahead of AI
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How Claude Watermarks AI-Generated Text
A 48-minute video walkthrough of token sampling, watermark detection, and removal
作者 Sebastian Raschka, PhDBuilding an AI Text Detector From Scratch
An End-to-End Project With Dataset Construction, Model Training, Local Deployment, and RLVR
作者 Sebastian Raschka, PhDControlling Reasoning Effort in LLMs
How LLMs Learn Low-, Medium-, and High-Effort Reasoning Modes
作者 Sebastian Raschka, PhDUsing Local Coding Agents
Using Open-Weight Models in Local Coding Harnesses as an Alternative to Claude Code and Codex Subscriptions
作者 Sebastian Raschka, PhDLLM Research Papers: The 2026 List (January to May)
A curated roundup of notable LLM research papers that came out this year
作者 Sebastian Raschka, PhDRecent Developments in LLM Architectures: KV Sharing, mHC, and Compressed Attention
From Gemma 4 to DeepSeek V4, How New Open-Weight LLMs Are Reducing Long-Context Costs
作者 Sebastian Raschka, PhDMy Workflow for Understanding LLM Architectures
A learning-oriented workflow for understanding new open-weight model releases
作者 Sebastian Raschka, PhDComponents of A Coding Agent
How coding agents use tools, memory, and repo context to make LLMs work better in practice
作者 Sebastian Raschka, PhDA Visual Guide to Attention Variants in Modern LLMs
From MHA and GQA to MLA, sparse attention, and hybrid architectures
作者 Sebastian Raschka, PhDA Dream of Spring for Open-Weight LLMs: 10 Architectures from Jan-Feb 2026
A Round Up And Comparison of 10 Open-Weight LLM Releases in Spring 2026
作者 Sebastian Raschka, PhDCategories of Inference-Time Scaling for Improved LLM Reasoning
And an Overview of Recent Inference-Scaling Papers
作者 Sebastian Raschka, PhDThe State Of LLMs 2025: Progress, Problems, and Predictions
A 2025 review of large language models, from DeepSeek R1 and RLVR to inference-time scaling, benchmarks, architectures, and predictions for 2026.
作者 Sebastian Raschka, PhDLLM Research Papers: The 2025 List (July to December)
In June, I shared a bonus article with my curated and bookmarked research paper lists to the paid subscribers who make this Substack possible.
作者 Sebastian Raschka, PhDFrom DeepSeek V3 to V3.2: Architecture, Sparse Attention, and RL Updates
Understanding How DeepSeek's Flagship Open-Weight Models Evolved
作者 Sebastian Raschka, PhDBeyond Standard LLMs
Linear Attention Hybrids, Text Diffusion, Code World Models, and Small Recursive Transformers
作者 Sebastian Raschka, PhDUnderstanding the 4 Main Approaches to LLM Evaluation (From Scratch)
Multiple-Choice Benchmarks, Verifiers, Leaderboards, and LLM Judges with Code Examples
作者 Sebastian Raschka, PhDUnderstanding and Implementing Qwen3 From Scratch
A Detailed Look at One of the Leading Open-Source LLMs
作者 Sebastian Raschka, PhDFrom GPT-2 to gpt-oss: Analyzing the Architectural Advances
And How They Stack Up Against Qwen3
作者 Sebastian Raschka, PhDThe Big LLM Architecture Comparison
From DeepSeek-V3 to Kimi K2: A Look At Modern LLM Architecture Design
作者 Sebastian Raschka, PhDLLM Research Papers: The 2025 List (January to June)
A topic-organized collection of 200+ LLM research papers from 2025
作者 Sebastian Raschka, PhD