Exploring AI Learning, the specifics of Large Language Models, including architecture, prompt engineering, fine-tuning, RAG, and their application in text-based tasks.
Standard residuals Fixed, uniform weights Embedding h₁ Layer 1 Layer 2 Layer 3 Each layer only sees the accumulated sum Attention Residuals Learned, input-dependent Embedding h₁ Layer 1 Layer 2 Layer 3 Layer 3 selectively attends to any earlier layer Residual connections are one of those rare ideas in deep learning that became so successful, … Read more
If you build with LLMs, you know the moment. The model sounds polished, calm, and certain, then it gives you a made-up answer with the confidence of a senior consultant. That gap between fluency and truth is where AI hallucinations become costly. They waste time, erode trust, and in real workflows, they can quietly contaminate … Read more
Introduction Most LLM agents today are brilliant goldfish. They reason well. They plan decently. They can browse, search, shop, code. Then the episode ends, and it is as if nothing ever happened. No scars. No instincts. No accumulation. That is the quiet ceiling on current agent systems. Not reasoning depth. Not model size. Memory. In … Read more
Most “AI welfare” debates jump straight to consciousness. That’s the flashy question, but it’s not what Anthropic is doing in the Claude Sonnet 4.6 System Card. Their “model welfare” section is not a claim that Claude is sentient. It’s an evaluation program: track behavioral signals that may matter once you treat a model as a … Read more
Introduction Some days in theoretical physics feel like mountain climbing. You spend hours inching upward through algebra, you finally reach a viewpoint, and the “beautiful simple formula” everyone promised turns out to be hiding behind a boulder labeled “one more identity.” Then there are days when a language model strolls by, points at your pile … Read more
Introduction Most “feature selection” stories start the same way. You have a model, you have a mountain of inputs, and you suspect half of them are doing more harm than good. Then you open a paper, see “NP-hard,” and quietly decide you will just throw everything into the network and let SGD sort it out. … Read more
Drifting Models: The One-Step Image Generator That Trains The “Diffusion Steps” Away Play Introduction Most generative models feel like they’re doing the same ritual: start with noise, take many tiny steps, hope you land on something beautiful before your GPU fan files for overtime. The paper Generative Modeling via Drifting (arXiv: 2602.04770v1) proposes a different … Read more