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research#llm🏛️ OfficialAnalyzed: Jan 16, 2026 01:15

Demystifying RAG: A Hands-On Guide with Practical Code

Published:Jan 15, 2026 10:17
1 min read
Zenn OpenAI

Analysis

This article offers a fantastic opportunity to dive into the world of RAG (Retrieval-Augmented Generation) with a practical, code-driven approach. By implementing a simple RAG system on Google Colab, readers gain hands-on experience and a deeper understanding of how these powerful LLM-powered applications work.
Reference

“This article explains the basic mechanisms of RAG using sample code.”

PermalinkZenn OpenAI
Research#LLM🔬 ResearchAnalyzed: Jan 10, 2026 07:17

New Research Reveals Language Models as Single-Index Models for Preference Optimization

Published:Dec 26, 2025 08:22
1 min read
ArXiv

Analysis

This research paper offers a fresh perspective on the inner workings of language models, viewing them through the lens of a single-index model for preference optimization. The findings contribute to a deeper understanding of how these models learn and make decisions.
Reference

“Semiparametric Preference Optimization: Your Language Model is Secretly a Single-Index Model”

PermalinkArXiv
Research#llm📝 BlogAnalyzed: Jan 3, 2026 01:45

How Do AI Models Actually Think?

Published:Jan 20, 2025 00:28
1 min read
ML Street Talk Pod

Analysis

This article summarizes a podcast discussion with Laura Ruis, a PhD student researching how large language models (LLMs) reason. The discussion covers fundamental mechanisms of LLM reasoning, exploring whether LLMs rely on retrieval or procedural knowledge. The table of contents highlights key areas, including LLM foundations, reasoning architectures, and AI agency. The article also mentions two sponsors, CentML and Tufa AI Labs, who are involved in GenAI model deployment and reasoning research, respectively.
Reference

“Laura Ruis explains her groundbreaking research into how large language models (LLMs) perform reasoning tasks.”

PermalinkML Street Talk Pod