Understanding How Neural Networks Learn (with interactive visualizations)

Key points

• TL;DR -- Interpretability might be way more tractable than we thought, and the guy who helped convince everyone otherwise is now saying we can understand models by directly studying neurons and their interactions after all.1
• Studies Qwen3 32B, Qwen3.6-35B-A3B, and Kimi K3 -- along with convolutional vision models.1
• For years the standard view in AI interpretability has been that neural networks are a tangled mess.1
• Individual neurons seemed to respond to random unrelated stuff (famous example: one neuron fired for both cat faces and car fronts), and the explanation was “superposition,” the idea that networks cram unrelated concepts into the same neurons to save space.1

Sources (1)

• [1]Understanding How Neural Networks Learn (with interactive visualizations)
  r/LocalLLaMA (top, daily) · Oct 16, 06:05 PM

  This essay rewrites fundamental assumptions about how neural networks learn and connect, with some of the best interactive visualizations I've ever seen.

  TL;DR -- Interpretability might be way more tractable than we thought, and the guy who helped convince everyone otherwise is now saying we can understand models by directly studying neurons and their interactions after all.

Extractive summary: sentences quoted from the sources.