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Spectral Distillation: From Nonlinear Dynamics to Linear State-Space Models
A new machine learning method called Spectral Distillation provides a provable, convex pipeline to extract compact linear state-space models from nonlinear dynamical systems, avoiding non-convex system identification.
Aug 7, 2026
Convex, which provides an AI-optimized application backend for developers, raised a $57M Series B led by Insight Partners, taking its total funding to $110.5M (Maria Deutscher/SiliconANGLE)
Convex Inc., a developer tooling startup offering an AI-optimized application backend, secured $57M in Series B funding led by Insight Partners, bringing its total disclosed funding to $110.5M.
Aug 4, 2026
The Convergence Behavior of Adam under Heavy-Tailed Noise
A new theoretical analysis establishes the first convergence guarantees for the standard Adam optimizer under heavy-tailed stochastic noise — a common but poorly understood condition in modern deep learning — revealing both its robustness and suboptimal iteration complexity without domain-radius adaptation.
Jul 31, 2026
After OpenAI’s CDC proof announcement, GPT-5.6 used a similar prompt to close a 30-year gap in convex optimization, verified in Lean
A Reddit post claims GPT-5.6 closed a 30-year gap in convex optimization using a prompt inspired by OpenAI’s CDC proof announcement and verified in Lean — but provides no evidence, source, or verifiable details.
Jul 19, 2026