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 explainability 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

Dublin is the capital city of IrelandModerate AI | score 0.55: stating the obvious to complete the geographic introduction; models are trained to be didactical and tend to add well-known facts even when they add little new information and one of the most exciting places I have ever visitedWeak Human | score -0.30: personal POV; this kind of grounded first-person phrasing can be a signal that there is a real human perspective behind the text. The city is full of history, culture, and friendly peopleModerate AI | score 0.65: triad; good writing often uses them, but AI text overuses this pattern because neat, balanced lists of positives are statistically overrepresented in training data.

↑ 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

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