Posts tagged "anthropic"
12 posts
Double Descent Explained: Why More Data Can Make a Model Worse
window.MathJax = { tex: { inlineMath: [['$','$'], ['\\(','\\)']], displayMath: [['$$','$$'], ['\\[','\\]']] }, svg: { fontCache: 'global' } };  Attribution graphs gave us a way to see *what* information a model moves between tokens. But they froz
How Does an LLM Know When to Start a New Line? The Geometry of Counting
 Here's a deceptively simple puzzle. When a language model wraps text to a fixed width, it has to know how many chara
Towards Monosemanticity: Pulling Clean Features Out of a Messy Neuron
 Inside a neural network, a single neuron fires for many unrelated things at once — it's **po
Reading the Mind of Claude: Millions of Features Inside a Frontier AI
 In 2023, Anthropic learned to pull clean, single-meaning **features** out of a tiny one-layer model. The obviou
Circuit Tracing: How We Draw the Wiring Diagram of an AI's Thoughts
 In a companion post we toured *the biology of a language model* — watching Claude plan rh
The Biology of a Large Language Model: Tracing the Circuits Inside Claude
 We don't really *build* large language models — we **grow** them. We set up a trainin
Can an AI Look Inward? Emergent Introspection in Language Models
 Ask a language model what it's thinking and it will happily tell you — it describes its reasoning
Do AI Models Have Emotions? Inside Claude's Emotion Concepts
window.MathJax = { tex: { inlineMath: [['$','$'], ['\\(','\\)']], displayMath: [['$$','$$'], ['\\[','\\]']] }, svg: { fontCache: 'global' } }; ![A map of feeling — emotion concepts la
A Mathematical Framework for Transformer Circuits: How LLMs Actually Work
window.MathJax = { tex: { inlineMath: [['$','$'], ['\\(','\\)']], displayMath: [['$$','$$'], ['\\[','\\]']] }, svg: { fontCache: 'global' } }; ![A Mathematical Framework for Transform
Mechanistic Interpretability & Sparse Autoencoders: Giving AI an X-Ray
window.MathJax = { tex: { inlineMath: [['$','$'], ['\\(','\\)']], displayMath: [['$$','$$'], ['\\[','\\]']] }, svg: { fontCache: 'global' } }; ![Dense vs. sparse activations — the SAE
Natural Language Autoencoders: Reading a Model's Mind in Plain English
window.MathJax = { tex: { inlineMath: [['$','$'], ['\\(','\\)']], displayMath: [['$$','$$'], ['\\[','\\]']] }, svg: { fontCache: 'global' } }; Inside a large language model, every "th