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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Attention Is All You Need: The Paper That Changed Everything
If you’ve ever interacted with ChatGPT, asked an AI to summarize a document, or translated a phrase using Google Translate, you’re experiencing the legacy of a paper that redefined modern artificial intelligence. Published in 2017 by Vaswani et al., the paper “Attention Is All You Need” introduced the world to the Transformer architecture. This seemingly simple idea — that attention mechanisms alone can model complex language patterns without relying on recurrence or convolutions — has since become the bedrock of nearly every major NLP system.
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Introduction: The First Second That Changed Everything
On February 23, 1987, astronomers witnessed something extraordinary. A massive blue supergiant in the Large Magellanic Cloud went supernova. Its light was dazzling, but for physicists, the real treasure arrived hours earlier—in the form of 19 ghostly signals captured by two underground neutrino detectors. This was Supernova 1987A, the closest observed supernova in nearly four centuries and the first ever accompanied by direct neutrino detections. These few dozen elusive particles validated decades of theoretical work in core-collapse physics and marked the beginning of what we now call multi-messenger astronomy.
Yet, more than 35 years later, our best simulations—armed with full general relativity, detailed microphysics, and modern computing power—still cannot reproduce what those neutrino detectors saw in 1987.
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Teaching AI to Use Tools — The Right Way
A Deep Dive into Seal-Tools: The Dataset That Makes LLMs Smarter Agents
Imagine asking your AI assistant to “book a flight to Paris, then schedule a taxi to the airport and convert the final bill to Euros.” Sounds simple, right? In reality, for most AI models, this isn’t just hard — it’s nearly impossible to get right without human babysitting.
That’s because tool use, chaining functions, and executing multi-step operations requires structured reasoning, parameter handling, and format control — things even the smartest LLMs struggle with today.
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What Are Tensors?
Tensors are fundamental mathematical objects that appear across various domains such as physics, computer science, and engineering. At their core, tensors are multi-dimensional arrays that generalize the concepts of scalars (single numbers), vectors (one-dimensional arrays), and matrices (two-dimensional arrays). Unlike simple arrays, tensors are not just containers of numbers—they come with transformation rules that allow them to describe physical phenomena in a way that remains consistent across coordinate systems.
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From “Why” to “How”: ReAct’s Unified Reasoning-Acting Paradigm
Large language models (LLMs) have reshaped natural language processing by demonstrating impressive capabilities in text generation, summarization, and translation. Yet, as powerful as they are, these models often struggle when asked to perform complex, multi-step tasks that require deliberate planning and interaction with external information sources. Traditional chain-of-thought (CoT) prompting enables LLMs to articulate intermediate reasoning steps, but it remains confined to the model’s internal knowledge and inference capabilities. Conversely, action-based approaches have allowed models to execute external operations—such as querying an API or navigating an environment—but lack explicit internal reasoning, leading to unexplainable or brittle behavior. The ReAct framework addresses this gap by synergizing reasoning and acting in a unified prompt-based paradigm that interleaves “thoughts” and “actions” to solve complex tasks more effectively and transparently.
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