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
LH-AVLN
LH-AVLN is a benchmark for long-horizon navigation that combines multi-goal missions, heterogeneous goal specifications, and persistent spatialized audio cues. Agents must execute missions of two to four goals specified by category, language, or reference image, using RGB-D, pose, and binaural audio in indoor 3D environments. It supports ordered and unordered missions with alternating goal-associated sounds.
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.
Evaluation context and evidence
- Source
- Claire Radar
- Publisher
- Not recorded
- Benchmark release
- 2026-07-04
- 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.