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EvoVLM — Evolving Multimodal Inference Programs: LLM-guided evolutionary search for accurate and efficient vision-language inference.

EvoVLM uses evolutionary search to improve vision-language inference efficiency.

Steady
Signal score
10
as of 13h ago
Trajectory
↓ Cooling
7-day est. ~0

Why this scored 10

every term, weighted
Velocity+0.0 / 40

Engagement gained per hour since the last capture, against the fastest item on its own source

Acceleration+12.0 / 25

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

Cross-source spread+0.0 / 25

How many independent communities are talking about the same entity

Recency+5.4 / 10

Decays to zero over 14 days

Saturation penalty−7.5 / 30

Subtracted once something is big and old — we rank what's next, not what's peaked

Composite9.8

Weights are hand-tuned, not learned — we're calibrating them against realized trends as history accumulates. On a topic'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

Outlook

low confidence · estimate, not a guarantee

7-day

~0

range 0–12

14-day

~0

range 0–14

30-day

~0

range 0–19

Signal history

7-day window (free)
015

projected trajectory (estimate, not a guarantee)

Entities

evovlm
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Drop this in a README or blog post — it updates automatically as the score moves.