The idea
Hassan describes using a language model to summarize research papers, then asking Jev to classify each title and summary into a topic. The resulting labels power a visual exploration interface.
Inside the decision loop
Paper title + summary + topic list
Select a research topic
Organize an explorable library
What to take from it
Different stages can use different models. The classifier is only as informed as its input summary, so preserve the source and make it possible to inspect papers that cross multiple topics.
The source explicitly separates summarization cost from classification cost and says classification evaluations were still in progress. Do not present the combined pipeline as a Jev-only result.
Follow the original work
This case is an editorial interpretation of a public community demonstration. It has not been reproduced or benchmarked by this publication.
Original post by @nutlope