Terrence C. Kim

  TLDR For those of you not familar with the term (TLDR: Too Long;Didn't Read) Running efficient LLMs locally for coding tasks on an older local hardware (e.g. RTX 3080 (10GB VRAM)) requires careful optimization to balance model capability with hardware constraints. After extensive research into the latest developments through 2025, In most cases, native small models  outperform heavily quantized large models  but not always. In short, further research is needed. As most things you find with the latest transformer based research, nothing seems conclusive in June 2025. Local Agent - Native vs. Quantized Deploying these models locally on personal computers (PCs) for specialized tasks, such as coding assistance tailored to a specific subject or project, presents a considerable challenge. The primary constraints are hardware limitations, particularly VRAM, RAM, and processing power, which often preclude the use of the largest, most capable models.  This necessitates ...

  Common Themes Emerge in Advanced AI Research An analysis of the three research papers reveals a shared focus on advancing artificial intelligence capabilities, particularly in the realms of reinforcement learning, enhancing model reasoning, and developing autonomous or self-improving systems. A significant emphasis on code generation and algorithmic optimization is also evident. The first paper, introduces "Absolute Zero," a paradigm employing Reinforcement Learning with Verifiable Rewards (RLVR). This system enables reasoning models to train without human-curated data by self-evolving their training curriculum and enhancing their reasoning abilities through a code executor. The research highlights state-of-the-art performance in coding and mathematical reasoning tasks. Similarly, the INTELLECT_2 Technical Report details a globally distributed reinforcement learning approach for training a large-scale (32 billion parameter) reasoning language model. This work unique...

While back, my team and I were exploring how to use the most lightweight model possible to perform quick fact-checking before we deliver responses to end users. Our goal was to achieve that final 99.9% accuracy in our overall system. Back then, we were thinking about creating a small, specialized AI assistant whose only job would be to verify facts against our data sources. This paper from Microsoft Research that takes a completely different approach to this same challenge. Let's break down what makes this research so interesting. The paper is called  "Towards Effective Extraction and Evaluation of Factual Claims"  and it tackles a fundamental problem: when large language models create long pieces of text, how do we effectively pull out the factual claims that need to be checked? Even more importantly, how do we determine whether our extraction methods are actually any good? Think of it like trying to identify specific ingredients in a complex recipe. You need not ...

LLM as an Operating System? Since 2023, researchers have been exploring the concept of LLMs functioning as operating system. This analogy makes intutive sense when we consider how traditional operating systems serve as intermediaries between users and computer resources. I remember encountering visualization that mapped out this transformation. In the traditional OS model, we have layers like the kernel, system calls, and user interface sitting between hardware and applications. With LLM as an OS, we can reimagine these layers, positoning language models and agentic components as the new intermediaries between user and their digital resources - whether that's a data repositories, computational tools, or planning system.  What makes this vision particularly compelling is the role of multimodal interfaces in this "compressed intellgience". Voice and vision capabilities fundamentally reshape how humans interact with thiis "cognitive OS". Instead of typing comm...

Microsoft has quietly released several impressive open-weight reasoning models base on Phi-4, accompanied by three significant research papers. In this article, I'll guide you through each of these three papers, examining their key findings and discussing their implication for the broader development of reasoning-capable language models.  "Phi-4-reasoning Technical Report"    "Phi-4-Mini-Reasoning: Exploring the Limits of Small Reasoning Language Models in Math" ,  "Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Languange Models via Mixture-of-LoRAs" Why I like Phi My interest in phi models began with the 2023 paper   "Textbooks Are All You Need"  which introduced the original phi-1 model. This pioneering work emphasized the development of small language models, highlighting the critical importance of high-quality data, the creation of synthetic training data, and extended training periods. Just three months later, the team...