Six Navers Lab / Einsia benchmarks and the work they test
Compare the tasks, scoring and evidence behind six Einsia benchmarks, with complete Radar detail pages and original references.
Claire Radar
Distract-Bench
Distract-Bench evaluates robustness of vision-language models to semantic visual distractions, which are meaningful but task-irrelevant cues that preserve the ground-truth answer.
Evidence available for this benchmark
Numeric results have not been collected for this source record. Use the paper, code or dataset below to check the task and experiments; this does not imply that the original work contains no results.
Start with these resources
Cited documents and dates (1)
Catalog collected on 2026-10-14; resources and fields reflect that saved source.
Recorded attention
Observed on: 2026-10-11
- GitHub stars
- 10
- Star change · 30 days before observation
- 0
yizheng-sun/distract-bench · View source snapshot
Dated attention observations, not live counts or benchmark scores.
Evaluation context and evidence
- Source
- Claire Radar
- Publisher
- Not recorded
- Benchmark release
- 2026-06-08
- Collected
- 2026-10-14
- Modality
- Not recorded
These are source-reported observations, not independent reruns by this site. Check the task version, evaluation protocol, unit and reporting date before comparing models. Results from different setups are not a universal ranking.
Reported results 0
This source record has no numeric results in our collection. That does not mean a score of zero or that no public results exist. Consult the original resources above.