Early Research! This system is a very early experimental prototype.
Do NOT trust the model's predictions for real-world decisions, we've trained it for very few steps. And keep in mind that we iterate frequently, so any outputs you see here may change significantly as we keep developing it. Our goal here is to showcase how a real system could work, for example when it comes to showing the annotated reasons for the predictions, but you should not expect it to perform well, at least for now.
SYSTEM DEMONSTRATION
TELL: Traced Evidence for Learned Labels
This is a demo of a GRPO-trained model that detects AI-generated text by marking the specific "tells" that indicate AI or human authorship, where we focus on interpretability and evidence rather than just a classifier score (read more in How this works).
OUR APPROACH
Imagine you see this text:
Dublin is the capital city of Ireland and one of the most exciting places I have ever visited. The city is full of history, culture, and friendly people.
and you go to check it on an AI detector:
Other detectors
87%
AI
↑ this is telling
TELL
↑ this is showing
Current AI detectors only give you a score. But is that enough?
We think that's not how we can build trust. For example, if I want to "accuse" someone of writing with AI, I need evidence, I can't just say "this website told me it's 87% AI".
So if we want proper AI text detection, we need to know why something is AI-generated. That's what TELL does.
TEST IT YOURSELF
0%
Verdict
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AI likelihood
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You can also rate individual tells by hovering over highlighted text and using the rating buttons in the tooltip.
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