MAP: A Benchmark on Multimodal Accessibility Planning for Real World Places
Ranking
Overall
78
Content
95
Popularity
37
Observed public metrics from 1 member.
Merged summary
TL;DR - MAP is a benchmark for evaluating whether multimodal AI assistants can reliably help users plan real-world visits around accessibility requirements. It matters because accessibility details and place information change over time and require verifiable, location-specific evidence.
- Evaluates both verification and recommendation of points of interest that meet requested accessibility features.
- Tests whether systems can determine if accessibility claims are supported and identify qualifying places.
- Measures retrieval of relevant visual evidence for the specified place and accessibility requirement.
- Supports scheduled ground-truth refreshes, automated scoring, and human review of a subset of responses.
Sources (1)
MAP: A Benchmark on Multimodal Accessibility Planning for Real World Places
Public signals
Semantic Scholar citations 0 · Semantic Scholar influential citations 0
TL;DR - MAP is a benchmark for evaluating whether multimodal AI assistants can reliably help users plan real-world visits around accessibility requirements. It matters because accessibility details and place information change over time and require verifiable, location-specific evidence.
- Evaluates both verification and recommendation of points of interest that meet requested accessibility features.
- Tests whether systems can determine if accessibility claims are supported and identify qualifying places.
- Measures retrieval of relevant visual evidence for the specified place and accessibility requirement.
- Supports scheduled ground-truth refreshes, automated scoring, and human review of a subset of responses.