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No. 03 · Research note · September 22, 2026
Scaling Almost Anything?
Scaling laws let us trade compute for capability, but parameter count is only a coarse measure of capacity. Through a least-squares toy problem and three examples from LLMs — LoRA rank, Transformer depth, and MoE experts — larger can even hurt. What matters is how much of that capacity training actually exploits.
Bingcong Li · 12 min read
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No. 02 · AI for Math and Optimization Seminar · September 15, 2026
Open Problems as Active Learning in the Age of AI
Lessons from a semester of the AI for Mathematics and Optimization seminar at ETH Zürich, where BSc and MSc students worked on nine genuine open problems with AI as a fallible partner. Three were solved, two produced partial progress — but the more important lesson was educational.
Niao He, Zebang Shen, Fan Wang · 11 min read
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No. 01 · AI for Math and Optimization Seminar · July 7, 2026
Cycling and Non-Acceleration of the Heavy-Ball Method in One Dimension
Recent work rules out acceleration of the heavy-ball method in dimension two and above by constructing functions on which it cycles. We close the remaining case: by rotating and projecting the two-dimensional cycle onto a line, heavy ball provably cannot accelerate on univariate functions either. The core construction is formalized in Lean 4.
Konstantinos Fotopoulos, Michael Helcig, Karl Deck, Jannis Alsbach · 9 min read
