HTTPS makes sure you’re communicating on a secure channel with the API endpoint. ZKPs guarantees that the computation happened correctly behind the endpoint.

The work from Anthropic isn't a new idea, but they made the process more efficient, as inference verification is one of the biggest bottlenecks in the AI lifecycle. Beyond that, there's a lot more that can be proven, like showing a model was trained on a specific dataset with certain properties.

From an auditing perspective, this used to be handled either by showing the data (which is pretty rare), checking samples (which has been shown to be easily hackable), or similar workarounds. For inference, an auditor might want to interact with a model on demand and have absolute certainty that the model they're talking to is actually the one declared, and not a substitute.

I’m not sure what you mean by correctly here - do you mean with the model expected? That’s not related to correctness.

Pleased to hear somebody cares about this. What is it a bottleneck for though? Are people actually doing this right now in non-academic settings? I had the impression this is not in fact usable with larger models.

> guarantees that the computation happened correctly behind the endpoint

but why do I care about that? can you some example "user stories"?