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aientity · corroborated across sources

masked diffusion training

for time-series ML engineers

Steady

19

signal score

as of 6d ago

1

live items

Too early

trajectory — needs a few more snapshots

arxiv

Why this scored 19

every term, weighted
Velocity+0.0 / 40 max

Its fastest-moving item, measured against the pace of its own source

Acceleration+9.9 / 25 max

Whether that velocity is itself speeding up, as a per-hour rate

Cross-source spread+0.0 / 25 max

How many independent communities its own items come from

Recency+9.3 / 10 max

Decays to zero over 14 days, counted from when we first saw it

Saturation penalty0.1 / 30 max

Subtracted once something is big and old — sized by its biggest item, aged from when we first saw it

Composite19.1

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.

  1. 119

    Discretizing Continuous Time Series for Imputation with Masked Diffusion Training

    Discretizes continuous time series for imputation via masked diffusion. — for time-series ML engineers

    arxivaiSteady7d ago