Category: Uncategorized
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The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity – lecture
Welcome, everyone. Please take your seats. Today, we are going to dissect a fascinating paper by researchers at Apple titled, “The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity”. Opening Hook When you watch a modern Large Reasoning Model (LRM) output a “chain-of-thought”—step-by-step logic, reflection, self-correction—it…
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The Victorian Supercomputers – lecture
Welcome, class. Please take your seats. Today, we are going to explore a conceptual leap in artificial intelligence that challenges everything we think we know about how machines communicate. Opening Hook: The Victorian Supercomputers Imagine two state-of-the-art supercomputers. They are capable of processing trillions of calculations per second, analyzing complex geometries, and modeling fluid dynamics.…
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The Geometry of Intelligence – A lecture
Welcome, everyone. Please take your seats. Today, we are going to dive into the architecture of Large Language Models—but we are going to look at them through a fundamentally different lens than you might be used to. The Hook: The Geometry of Intelligence Imagine you are trying to understand a sprawling, massive metropolis. If you…
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Engineering Spec for a Latent-Space LLM Communication System
Below is a concrete design for a machine-to-machine communication stack where two LLMs exchange latent packets instead of natural language. The goal is not mystical “telepathy.” The goal is an engineering protocol that lets one model send a compact, typed, verified semantic state to another model. 1. System overview We will call the system: LCP-1…
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How an llm transforms the world into semantic geometry
Think of the whole process as three layers: An LLM never directly “understands” a word the way a human does. It converts text into structured numbers that can be manipulated algebraically. The key object is the embedding vector, which is a point in a high-dimensional space. 1. A token starts as a discrete symbol Before…
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Latent Directions Mastery: PCA, SVD, LoRA, Attention-Head Subspaces, Eigenspaces, Distillation, and Long-Context Memory — A Frank-said / Grok-said Dialogue on How Directional Geometry Powers the Next Leap in LLMs
Frank said:All right, now let’s go deeper and clean this up. I want a plain-English but serious Frank-said / Grok-said discussion focused specifically on PCA vs LoRA vs SVD vs attention-head subspaces, and then let’s push it further into eigenspaces, distillation, and long-context memory. I want to understand how they relate, how they differ, and…