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
SPIEval
SPIEval is a human-curated benchmark for evaluating large language models as mobile assistants that retrieve and reason over personal information scattered across multiple apps. It comprises 250 tasks across five cognitive capabilities, 4,335 fictional personal records in 10 simulated apps, and supports multi-turn interaction through 21 tools. Models receive underspecified user instructions and must search records and invoke tools; final execution calls are compared to human-annotated gold calls at the parameter level.
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: 2026-10-13
- Hugging Face paper upvotes
- 12
- Hugging Face downloads · 30 days before observation
- 106
Dated attention observations, not live counts or benchmark scores.
Evaluation context and evidence
- Source
- Claire Radar
- Publisher
- Not recorded
- Benchmark release
- 2026-08-11
- 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.