Kazunari Sugiyama
Kyoto University — Academic Center for Computing and Media Studies · Japan
6 posts
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.
Articles
The Cataloguer's Problem Is Not Going Away — It Is Going Deeper
There is a conversation happening in digital library circles that frequently gets framed as a question about tools: should we use large language models to auto…
Shadow Libraries, AI Training Data, and the Copyright Problem Digital Libraries Cannot Avoid
The problem the field was handed without being asked In 2023 and 2024, a sequence of disclosures, lawsuits, and investigative reports established what many in…
The Persistent Identifier Problem Is Not Solved and COMET Is Trying to Explain Why
A persistent identifier is, at its most basic, a promise. It is a string of characters assigned to a digital object with the institutional commitment that the…
RAG Is Not a Cure — What Retrieval-Augmented Generation Actually Fixes in Biomedical Libraries
Retrieval-Augmented Generation has become the consensus answer to the question that biomedical information professionals and clinical AI developers have been a…
What Born-Digital Archives Are Losing Before Anyone Notices
There is a category of loss that archivists have spent two decades naming and still have not solved: the loss that accumulates not through flood, fire, or inst…
When Behaviour Replaces Identity in Online Gambling Systems
KYC was never meant to define the full architecture of player experience. It entered the system as a regulatory necessity — a checkpoint designed to confirm id…