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AI · Sunday, 28 June 2026 · Issue 34 · 6 stories

Grok 4's smartest claim unravels, cost spiral threatens AI

Models & Releases

Research

Heavy‑Tailed Weight Spectra Reveal a Deep‑Learning Bias Toward Sparse Representations

Power-law distributions seen in neural network weights, activations, and attention can be traced to generalized central limit theorem universality, positioning heavy‑tailed spectra as a natural bridge between sparsity and Gaussianity. The paper argues this mechanism drives networks toward sparse, factored representations, offering a statistical‑physics lens on modern deep‑learning empirics.

lesswrong.com ↗27 min read at source

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Policy & Safety

Tools & Open Source

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That was all 6 stories in the 28 June issue.