Physics • Machine Learning • Curiosity
Welcome to Tensors & Quarks
Exploring the cosmos of physics and the depths of machine learning with hands-on experiments, notes, and essays.
Latest Posts
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Geometry vs Quantum Damping: Two Roads to a Smooth Big Bang
Imagine rewinding the Universe until every galaxy, atom and photon collapses into a single blinding flash. Is that primal flash a howling chaos or an eerie stillness? In 1979 Roger Penrose wagered on stillness, proposing that the Weyl tensor—the slice of curvature that stores tidal distortions and gravitational waves—was precisely zero at the Big Bang. Four decades later two very different papers revisit his bet. One rewrites Einstein’s equations so the zero-Weyl state drops out of geometry itself; the other unleashes quantum back-reaction that actively damps any distortion away. Which path makes a smooth dawn more believable?
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From Heads to Factors: A Deep Dive into Tensor Product Attention and the T6 Transformer
A Transformer layer must preserve every key–value pair for every head, layer, and past token—a memory bill that rises linearly with context length.
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The Hidden Danger of AI Oversight: Why Model Similarity Might Undermine Reliability
Artificial Intelligence, particularly Large Language Models (LLMs) like ChatGPT, Llama, and Gemini, has witnessed extraordinary progress. These powerful models can effortlessly handle tasks from writing articles to solving complex reasoning problems. Yet, as these models become smarter, ensuring they’re behaving as intended is becoming harder for humans alone.
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The Semiclassical Death of Warp Bubbles
Introduction: From Sci-Fi to Semi-Classical Reality
The dream of faster-than-light travel has long danced on the edge of science and imagination. Since Miguel Alcubierre first proposed a warp drive metric in 1994—a solution to Einstein’s field equations that allows a spaceship to “surf” through spacetime by contracting space in front of it and expanding it behind—scientists have speculated whether such a phenomenon could ever be physically realized.
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How AlexNet Lit the Spark and ResNet Fanned the Flames
In the ever-evolving landscape of deep learning, certain architectures have defined turning points in how neural networks are designed, trained, and understood. Among these, AlexNet and ResNet stand out as monumental contributions that shifted the paradigm of computer vision and image classification. Though separated by just three years, these two architectures reflect fundamentally different eras of deep learning—AlexNet laid the groundwork for deep convolutional networks, while ResNet solved the pressing problems that deeper architectures introduced.
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