vlm
for ai researchers
Why this scored 2
every term, weightedIts fastest-moving item, measured against the pace of its own source
Whether that velocity is itself speeding up, as a per-hour rate
How many independent communities its own items come from
Decays to zero over 14 days, counted from when we first saw it
Subtracted once something is big and old — sized by its biggest item, aged from when we first saw it
Weights are hand-tuned, not learned — we're calibrating them against realized trends as history accumulates. On an entity's first sighting there's no previous reading to compare against, so acceleration starts from a neutral prior rather than a measurement, and velocity falls back to engagement over its whole lifetime until a second reading exists. Full methodology
Signal history
7-day window (free)The evidence
The live items this entity's score aggregates — every community independently talking about it right now. This is the corroboration, shown, not claimed.
- 19
Simple Domain Generalization for Strong Pixel-Level Image Tampering Detection in Modern VLMs
Image tampering detection · for computer vision devs
arxivSteadyai52d ago - 29
DecoupleMix: Decoupled Ratio Search and Convex Allocation for Scalable VLM Data Recipes
Improves data recipe scalability · for ai researchers
arxivSteadyai45d ago - 37
More with Less: a Large Scale Remote Sensing VLM with a Simple Recipe
Scales remote sensing · for geospatial analysts
arxivSteadyai53d ago