> As models become capable of improving and deploying themselves ... how can a company be sure of which weights are actually running on its servers?

I find this a bit odd and backwards. Why do we choose not to make the deployment code to be robust, but instead to observe the model inference as proven? This moves unobservability layer to the system that collects the proofs. Given the assumption that code is fluid and LLMs are rewriting it often, how can we know it isn't being abused against the observer?

Goes without saying, it's also disappointing that this has nothing to do with making the model inference observable for consumers.