Sentient Foundation is a non-profit dedicated to open-source AGI, free from single-entity control. Advancing the future through @SentientAGI.
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Joined February 2025
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Models cost millions to train and minutes to copy. Fine-tuning and distillation can erase where they came from.
Introducing the Fingerprinting Leaderboard, an open benchmark for model provenance — built so the proof lives in the weights, not the paperwork.
fingerprint.sentient.foundat…
Believe it or not, our Fingerprinting Leaderboard has been a year in the making.
See where it all began with @sentientagi’s @NeurIPSConf-accepted paper on OML ↓
Last year, @SentientAGI and @Princeton researchers @edoardocontente, @viswanathpramod, @sewoong79, @oleg_golev, and @ZeruiCheng published “OML: A Primitive for Reconciling Open Access with Owner Control in AI Model Distribution."
This groundbreaking research on model fingerprinting is what paved the way for @sentient_found’s Fingerprinting Leaderboard.
Rank your schemes on the Fingerprinting Leaderboard ↓
fingerprint.sentient.foundat…
Rank your schemes on the Fingerprinting Leaderboard ↓
fingerprint.sentient.foundat…
Our SF Tech Week happy hour at Novela, led by our Director of Events & Community @its_mils_, was one for the books 📚
Thanks to @WithAmpersand, @rootlyhq, @gauge_sh, and all the builders who stopped by to share notes on enterprise agents and open-source AGI!
Builders, don't forget our happy hour tomorrow 🥂
Join @WithAmpersand, @rootlyhq, and @sentient_found for an evening with the people building enterprise agents and open AGI.
📍 Novela, 662 Mission St, San Francisco
🗓 Thur, Oct 8th, 5:30-9:00PM PT
Most fingerprints train a model to give set answers to a few fixed questions. Retrain it a little and the fingerprint disappears.
Researchers at @ETH Zurich spread the fingerprint across a whole domain of answers, so it's harder to erase during retraining.
Models cost millions to train and minutes to copy. Fine-tuning and distillation can erase where they came from.
Introducing the Fingerprinting Leaderboard, an open benchmark for model provenance — built so the proof lives in the weights, not the paperwork.
fingerprint.sentient.foundat…
Rank your schemes on the Fingerprinting Leaderboard ↓
fingerprint.sentient.foundat…
Read the “LLM Fingerprinting via Semantically Conditioned Watermarks” paper from @ETH Zurich ↓
arxiv.org/abs/2505.16723
For @0xsachi, model fingerprinting is “the AI equivalent of the hidden marks Renaissance painters used to establish authorship.”
Learn more about @sentient_found's Fingerprinting Leaderboard in this latest feature from @QZ ↓
LATEST FROM @QZ: An open benchmark to counter and prove AI models copying, fine-tuning and distillation.
Learn more: qz.com/sentient-launches-fir…
Rank your schemes on the Fingerprinting Leaderboard ↓
fingerprint.sentient.foundat…
Lexicographers, mapmakers, and painters all found ways to prove where their work came from.
Now, AI can carry provenance of its own: not in the logs, metadata, or outside investigation, but in the model itself.
Learn more ↓
sentient.foundation/news/fin…
Check out @qz's feature on our Fingerprinting Leaderboard ↓
xitter.cf/qz/status/211039451230…
Who Owns an AI? A New Benchmark Wants to Find Out
As AI models are copied, distilled, and redistributed at scale, proving where they came from has become one of the industry's thorniest problems. Sen
qz.com/sentient-launches-fir…
#AIOwnership #ModelFingerprinting #SentientFoundation
AI is the first medium that can be copied without leaving proof of origin.
When one model distills another, no one can prove it happened.
On October 14, that will change.
Catch up on the story so far: youtube.com/playlist?list=PL…
Lexicographers planted words, mapmakers drew fake streets, artists hid their signature in the paint.
For centuries, creators have found ways to make copying visible.
xitter.cf/sentient_found/status/…
AI is the exception.
A model can be distilled, copied, and repackaged, but no one can prove where it came from.
xitter.cf/sentient_found/status/…
