Guests enter the public stream first. Create an account only when you want the product to remember what you read, follow and save.
We study how follow edges, review quality, graph proximity and freshness can be blended into an explainable feed optimized for high-signal research discovery.
We evaluate rubric design for structured reviews, moderation queues and reviewer calibration in technical communities.
We combine publication metadata, author neighborhoods and dataset signals to improve retrieval quality for computational biology teams working across papers, code and experiments.
We propose ledger-based moderation records that improve accountability, appeals and policy learning in research products.
We extract actionable collaboration intent from follows, saves, reviews and topic overlap without collapsing trust into a single opaque metric.
We present a graph-native product architecture for scholarly sensemaking where work pages, topic maps and researcher trust signals reduce time-to-understanding for new literature.
The work drawing the strongest visible discussion right now.
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Core graph coverage and claimed identity on the platform.
Public profiles building visible momentum through reviews, comments and linked work.
