Matrix Math Just Got a Tiny Bit Quicker?

Ever feel like computers are just… slow? We’re constantly pushing them to do more, faster, from rendering the latest games to training those AIs that are writing (or at least inspiring) articles like this one. And at the heart of so many of these tasks lies matrix multiplication – a fundamental operation that’s been the…

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Averages Learn to Read Time in the Language of Space

Mathematicians think with abstractions that feel almost cinematic: space, time, randomness, and the ways they tuck themselves around one another. A new paper from the heartland of rigorous thought asks a surprisingly approachable question: what happens when you blend space and time into one operation on averages? The author, Aidan Young, writing from Ben-Gurion University…

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The Two Spikes That Let LLaMA Shrink Without Loss

The scale of modern language models can feel like watching a glacier slide: immense, intricate, and almost impossibly heavy. These giants—open or closed—are built from billions of parameters, tuned to predict the next word with uncanny fluency. But there’s a catch that scientists have wrestled with for years: the bigger the model, the heavier the…

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A Hidden Link Between Jumping and Diffusion Emerges

Unifying two worlds on one stage The microscopic world of particles and biomolecules is full of motion that looks chaotic, yet follows strict rules. In physics and chemistry we typically describe such motion with two mathematical languages. In continuous space, diffusion is painted with the blurred brushstrokes of Langevin equations and Fokker–Planck equations. In discrete…

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When AI Meets Many Domains, Which Path Will It Choose

Deep learning has a knack for mastering one thing at a time: recognize this photo, label that object, predict tomorrow’s weather. But real life isn’t tidy. A model trained on one set of images—say, studio portraits or product photos—fights to perform when the world shifts under its pixels: different lighting, different backgrounds, different camera quirks….

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Counting crowds gets a softer, smarter nudge

Counting people in a photo isn’t just a nerdy puzzle; it’s a real‑world skein of tiny decisions: who counts, who’s occluded, where a group ends and a stray limb begins. For years, researchers trained counting systems with a blunt signal: either the count was right, or it wasn’t. But in messy scenes—dense crowds, shifting light,…

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