AI EVALUATION, IN CONTEXT.BY EQUALITY MACHINE

Claire Radar

EgoSafetyBench

EgoSafetyBench is a diagnostic egocentric video benchmark of 1,200 robot-view scenarios to evaluate vision-language models as runtime safety guards. It assesses situational awareness across routine, suspicious, obvious, and contextual hazards, and visual-channel robustness against misleading in-scene text.

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

Hugging Face paper upvotes
1

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Dated attention observations, not live counts or benchmark scores.

Evaluation context and evidence

Source
Claire Radar
Publisher
Not recorded
Benchmark release
2026-06-30
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.