TP.TRUST Updated: Breaking Through Relay Caches to Improve Sample-Free Detection

We have released another update to TP.TRUST, our AI Token Verification platform.

This update focuses on further breaking through relay stations’ built-in caches and preset responses.

Many AI relay stations configure large caches. When a user’s question matches cached content, the relay may return a previously stored response instead of asking the underlying model to perform a complete inference again. While this can make responses appear faster, the result may not accurately reflect the actual capability of the model behind the API.

To address this issue, we use two complementary approaches: deep probes and question rephrasing.

By designing probe questions and expressing the same type of question in different ways, we attempt to break through fixed cached responses and encourage the underlying model to generate and reason again. We then analyze the reasoning results, response characteristics, and outputs across multiple tests to improve the accuracy of sample-free detection.

This does not mean that a single test can produce an absolute verdict for every API. Our goal is to continuously refine our probes and build a larger set of real-world cases, making the results more stable, objective, and useful.

We also welcome users to contact us and help improve our testing samples and methods. Any abnormal result, false positive, false negative, or unusual case you encounter may help us improve TP.TRUST.

👉 Try TP.TRUST: token.tpeol.com

Thank you again for your support.