Welcome to Tensors & Quarks
Exploring the cosmos of Physics & the depths of Machine Learning.
Latest Posts
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Seeing from All Angles: Making 3D Reconstruction Models Robust to Viewpoint Changes
How do we teach machines to understand the shape of a 3D object—no matter how it’s viewed? This new research shows that letting the model “learn from its own mistakes” may be the answer.
In the realm of computer vision and 3D understanding, one of the longstanding challenges is teaching machines how to reconstruct a 3D object from 2D images. If you’ve ever seen photogrammetry tools turn a set of photos into a 3D model, you already know the basic idea. But beneath the surface, there’s a major problem: these models often perform well only when the images are captured from familiar angles. Once the camera moves to a new, unseen position, the model starts to stumble.
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From Silos to Synergy: How MCP and A2A Are Building the Future of AI Agents
Introduction
In the fast-evolving world of artificial intelligence, language models are no longer just powerful tools for answering questions or summarizing text. They’re evolving into intelligent agents capable of reasoning, planning, interacting with other agents, and autonomously executing complex tasks. But as the complexity of these agent systems grows, so does the need for standards that ensure consistency, interoperability, and scalability.
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Can Quantum Mechanics Describe Reality? A Tale of Two Papers
In the spring of 1935, two scientific giants—Albert Einstein and Niels Bohr—stood on opposite sides of a profound question: Is quantum mechanics a complete description of reality? That question became the title of two iconic papers, published in the same year, each offering diametrically opposed answers. This wasn’t just a scientific disagreement; it was a philosophical clash that would shape the direction of physics for decades.
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From Circuits to Cognition: Following the Thoughts of Claude 3.5
Decoding Anthropic’s Next Step in Understanding Language Models
In my previous post, we explored “On the Biology of a Large Language Model”, Anthropic’s groundbreaking research that mapped the internal circuits of Claude 3.5 Haiku using attribution graphs. These graphs offered a glimpse into the hidden architecture of reasoning — showing how Claude decomposes questions, plans poems, reasons across languages, and even hallucinates.
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From Black Box to Blueprint: Tracing the Logic of Claude 3.5
Exploring the Hidden Anatomy of a Language Model
In the age of large language models, capability often outpaces comprehension. Models like Claude 3.5 can write poetry, solve logic puzzles, and navigate multilingual queries — but we still don’t fully understand how. Beneath their fluent outputs lies a vast architecture of layers, weights, and attention heads that, until recently, remained largely inscrutable.
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