Kazunari Sugiyama

Kazunari has published extensively on scholarly paper recommendation — long before retrieval-augmented generation made the field fashionable. He brings a skeptical, measurement-first lens to the current wave of AI-powered discovery tools.

Kazunari opens the Tech Stage with a live comparison of four retrieval-augmented scholarly assistants on the same five research questions. His methodology is unsentimental, his results occasionally surprising, and his refusal to trust vendor benchmarks refreshing.