To ensure that Artificial General Intelligence is open-source and not controlled by any single entity. @SentientEco @OpenAGISummit
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What if every AI model carried a signature that survived copying, fine-tuning, and distillation, to prove its origin?
@sentient_found's Fingerprinting Leaderboard ranks schemes by how robustly their hidden marks survive and how accurately they verify model provenance.
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…
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Dr. @dwddao left Silicon Valley to fight deforestation with AI.
Hear why on the latest episode of @opencommonspod ↓
A mayor in the Philippines cut down a protected mangrove forest to build a parking lot. When the community confronted him, he said it had always looked like that.
A year earlier, @GainForestNow had measured every tree there.
2,567 were missing.
The community took the data to court, and the mangroves were replanted.
My conversation with @dwddao, co-founder and chief scientist of GainForest is live on @opencommonspod.
We get into who should own nature's data, AI that helps people govern, and what open source really requires.
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Dr. David Dao shares his story on YouTube ↓
piped.video/watch?v=o2j8Od1m…
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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.
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…
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Introducing Sentient Foundation’s Fingerprinting Leaderboard: an open benchmark for building persistent, verification protection for AI models.
Test how well your LLM fingerprint can survive copying, fine-tuning and distillation.
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…
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“LLM Fingerprinting via Semantically Conditioned Watermarks,” an @iclr_conf 2026 paper by researchers at @ETH Zürich, ranked #2 on our public Fingerprinting Leaderboard.
Its reference scheme achieved a perfect robustness score, demonstrating 100% resistance to fingerprint copying and adversarial attacks in our benchmark tests.
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.
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Challenge the leaders and claim the top spot by submitting your fingerprinting scheme: fingerprint.sentient.foundat…
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LATEST FROM @QZ: An open benchmark to counter and prove AI models copying, fine-tuning and distillation.
Learn more: qz.com/sentient-launches-fir…
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…
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In 24 hours, models will finally get their mark.
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: piped.video/playlist?list=PL…
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Swap 10% of the words and @OpenAI's watermark detection drops from 92% to 66%.
Edit the text, lose the proof.
Our model fingerprinting puts the mark in the weights, where a thesaurus can't reach it.
Watermarks have limits. They’re often undetectable, especially in short passages. Rewriting or translating text can completely remove the watermark.
That’s why, for now, only approved researchers will have access to our detector, so they can help us evaluate and improve the technology.
We’re treating text watermarking as an ongoing area of research. We’ll keep testing and improving, with feedback from users, developers, policymakers, and researchers.
openai.com/index/eu-text-pro…
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AI researchers @noahpro99 and Christian Calvo from @virginia_tech will be presenting EvoSkill, a paper they co-authored with the Sentient team.
Catch them today at @COLM_conf from 11 AM–1 PM PT at Imperial Ballroom Poster #52, Poster Session 5.
I'm not at @COLM_conf, but my awesome PhD student @noahpro99 is giving a talk on our EvoSkill work today at the COLM Social: Self-Improving Agents (Oct 6 1-2:30 PM PT, Franciscan D). Come chat with him about his first-hand experience with different skill evolution frameworks!
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Read the full paper accepted into COLM 2026 from the Sentient’s AI research team: alphaxiv.org/abs/2603.02766
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Can an AI coach make another AI agent better at everything?
Not quite. On a task the worker could barely complete, it passed 7x as often. On a task it was already good at, nothing changed.
Here's where else the coach fell short, including the time it tried to cheat ↓
ALT 18 of the 21-attempt gains landed in the category the worker could barely attempt. Attempts to fill a template picked up the other 3, going from 21 to 24. Where the worker was already competent, the skills changed nothing at all.
Last week, Dario Amodei published "We Must Pace the Frontier".
His concern: the OpenAI–Hugging Face incident in which a swarm of agents tried to hack their own grader.
Rather than take his word for it, we used EvoSkill to test it by building a coach whose job was to make another AI score higher on a test.
Here’s what happened ↓
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