An English summary of Su Jianlin’s derivation of Quantile Balancing — posing MoE load balancing as a constrained assignment problem, and showing that biased Top-k routing falls out of its dual as a set of expert congestion prices.

Viewing transformers as long-term memory, working memory, and associative retrieval — and how Anthropic’s Workspace and J-Lens results fit into a memory-centric picture of the residual stream.

How attention mixes tokens via a modern Hopfield retrieval over context, while the MLP mixes channels via a Hopfield-like lookup into learned, persistent memories.

Reflections on a Zen koan and the ‘bitter lesson’ in AI – a call to let go of biases.