[andrewnakas]

This updates our existing entry, gpttrace-ettin1b, in place instead of adding a new one, so the leaderboard doesn't get crowded with GPTTrace versions.

• Model: GPTTrace Ettin-1B v8s, open weights at https://huggingface.co/nakas/gpttrace-v8s-1b (CC BY-SA 4.0). Same base model (jhu-clsp/ettin-encoder-1b) as the previous entry.
• What changed: v8s is trained to be robust beyond RAID: humanizer and paraphrase rewrites, 2026 generators, and far fewer false positives on everyday human web text. On our held-out RAID-train titles it scores the same as the previous model (TPR@5% 99.94 vs 99.93), so we expect a similar leaderboard score.
• RAID data use: RAID train only (held-out titles never used). No RAID test data was used for training or calibration.
• Files: predictions.json (672,000 rows, same ids and [{id, score}] format as before; score = P(machine)) and metadata.json. results.json is left for the CI to regenerate.

Thanks for maintaining RAID!

[github-actions]

Eval run succeeded! Link to run: link

Here are the results of the submission(s):

GPTTrace (Ettin-1B v8s)

Release date: 2026-11-01

I've committed detailed results of this detector's performance on the test set to this PR.

On the RAID dataset as a whole (aggregated across all generation models, domains, decoding strategies, repetition penalties, and adversarial attacks), it achieved an AUROC of 99.94 and a TPR of 99.93% at FPR=5% and 99.54% at FPR=1%.
Without adversarial attacks, it achieved AUROC of 99.97 and a TPR of 99.95% at FPR=5% and 99.74% at FPR=1%.

If all looks well, a maintainer will come by soon to merge this PR and your entry/entries will appear on the leaderboard. If you need to make any changes, feel free to push new commits to this PR. Thanks for submitting to RAID!

[liamdugan]

Congrats on the strong performance @andrewnakas let me know if you'd like me to merge your submission

[andrewnakas]

Thanks @liamdugan yes please merge the submission!