Hangtao/JevAdvBench
JevAdvBench Project page · Code JevAdvBench is, to our knowledge, the first adversarial benchmark for models trained with reinforcement learning for calibrated decisions (RLCD), such as Jev. These models answer a typed question about an input, the state, with a probability (Noul), a choice (Choice) or a score (Score), and software acts on the answer without a person reading it. The benchmark has 812 typed questions over 66 scenarios and a black-box attack suite of 9,744… See the full description on the dataset page: https://huggingface.co/datasets/Hangtao/JevAdvBench.
| Author / organization | Hangtao |
|---|---|
| License (as declared) | cc-by-nc-4.0 |
| Downloads | 248 (as reported by Hugging Face) |
| Likes | 0 |
| Created | 26 September 2026 |
| Last updated at source | 26 September 2026 |
Popularity, last 3 days
248 now (+8 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.
Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 18 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.