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LLM에서 추론 노력 제어하기
Controlling Reasoning Effort in LLMs
How LLMs Learn Low-, Medium-, and High-Effort Reasoning Modes
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로컬 코딩 에이전트 사용
Using Local Coding Agents
Using Open-Weight Models in Local Coding Harnesses as an Alternative to Claude Code and Codex Subscriptions
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LLM 연구 논문: 2026년 목록 (1월~5월)
LLM Research Papers: The 2026 List (January to May)
A curated roundup of notable LLM research papers that came out this year
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LLM 아키텍처의 최근 발전: KV 공유, mHC, 그리고 압축된 어텐션
Recent 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
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LLM 아키텍처를 이해하기 위한 내 워크플로우
My Workflow for Understanding LLM Architectures
A learning-oriented workflow for understanding new open-weight model releases
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코딩 에이전트의 구성 요소 - Sebastian Raschka 박사
Components of A Coding Agent - by Sebastian Raschka, PhD
How coding agents use tools, memory, and repo context to make LLMs work better in practice
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현대 LLM의 어텐션 변형 시각 가이드
A Visual Guide to Attention Variants in Modern LLMs
From MHA and GQA to MLA, sparse attention, and hybrid architectures
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봄날의 꿈: 2026년 1-2월 오픈웨이트 LLM 10가지 아키텍처
A 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
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LLM 추론 개선을 위한 추론 시간 스케일링의 카테고리
Categories of Inference-Time Scaling for Improved LLM Reasoning
And an Overview of Recent Inference-Scaling Papers
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2025년 LLM의 현황: 진전, 문제, 그리고 예측
The 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.
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LLM 연구논문: 2025년 목록 (7월~12월)
LLM 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.
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DeepSeek V3에서 V3.2로: 아키텍처, 희소 주의, 강화학습 업데이트
From DeepSeek V3 to V3.2: Architecture, Sparse Attention, and RL Updates
Understanding How DeepSeek's Flagship Open-Weight Models Evolved
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표준 LLM을 넘어서 - Sebastian Raschka 박사
Beyond Standard LLMs - by Sebastian Raschka, PhD
Linear Attention Hybrids, Text Diffusion, Code World Models, and Small Recursive Transformers
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LLM 평가의 4가지 주요 접근법 이해하기 (기초부터)
Understanding the 4 Main Approaches to LLM Evaluation (From Scratch)
Multiple-Choice Benchmarks, Verifiers, Leaderboards, and LLM Judges with Code Examples
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Qwen3을 처음부터 이해하고 구현하기
Understanding and Implementing Qwen3 From Scratch
A Detailed Look at One of the Leading Open-Source LLMs
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GPT-2에서 gpt-oss로: 아키텍처 발전 분석
From GPT-2 to gpt-oss: Analyzing the Architectural Advances
And How They Stack Up Against Qwen3
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주요 LLM 아키텍처 비교
The Big LLM Architecture Comparison
From DeepSeek-V3 to Kimi K2: A Look At Modern LLM Architecture Design
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LLM 연구 논문: 2025년 목록 (1월~6월)
LLM Research Papers: The 2025 List (January to June)
A topic-organized collection of 200+ LLM research papers from 2025
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LLM의 KV 캐시 이해와 처음부터 구현하기
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.
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바닥부터 배우는 LLM 코딩: 완전한 강의
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.
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LLM 추론을 위한 강화학습의 현황
The State of Reinforcement Learning for LLM Reasoning
Understanding GRPO and New Insights from Reasoning Model Papers
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처음부터 시작하는 추론: 1장
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 statistic…
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LLM 추론 모델 인퍼런스의 현황
The State of LLM Reasoning Model Inference
Inference-Time Compute Scaling Methods to Improve Reasoning Models