“I’m curious if this proposal necessitates forbidding multi-data center training.”
That’s a tricky edge case for the “all information in and out of a comput island is explainable”, for two reasons: One is the extreme information bandwidth, the other is that -as you explained- the links between datacenters would be hard to secure.
Since this is an edge-case and the size of upcoming megaclusters will probably eliminate the need for inter-datacenter pretraining, I expect this to not be an immediate dealbreaker.
If one really did insist on multi-datacenter training, you could still do the following: Each cluster is treated as an “island” whose in-out traffic must be fully explainable. Rather than capturing the whole traffic (which would be expensive), you can retrofit a setup that randomly samples from passively tapped optical fiber links. Basically a “panopticon” in which the prover can not predict which link will be sampled by the verifier, or when.
“(additionally, if multi-data center training isn’t allowable it also seems like you wouldn’t be able to transfer the completed model to a different data center, like one more suited for inference, after training)”
I am not quite following. Why would that be the case? Because the model has not been trained in a verified manner? Inference verification and training verification are different beasts. It probably makes sense to start with the former, and rely on post-hoc audits of models before training verification is ready.
“I’m curious if this proposal necessitates forbidding multi-data center training.”
That’s a tricky edge case for the “all information in and out of a comput island is explainable”, for two reasons: One is the extreme information bandwidth, the other is that -as you explained- the links between datacenters would be hard to secure.
Since this is an edge-case and the size of upcoming megaclusters will probably eliminate the need for inter-datacenter pretraining, I expect this to not be an immediate dealbreaker.
If one really did insist on multi-datacenter training, you could still do the following: Each cluster is treated as an “island” whose in-out traffic must be fully explainable. Rather than capturing the whole traffic (which would be expensive), you can retrofit a setup that randomly samples from passively tapped optical fiber links. Basically a “panopticon” in which the prover can not predict which link will be sampled by the verifier, or when.
“(additionally, if multi-data center training isn’t allowable it also seems like you wouldn’t be able to transfer the completed model to a different data center, like one more suited for inference, after training)”
I am not quite following. Why would that be the case? Because the model has not been trained in a verified manner? Inference verification and training verification are different beasts. It probably makes sense to start with the former, and rely on post-hoc audits of models before training verification is ready.
Here is recent work on both, if you are interested: https://amododesign.com/notes/2026-06-23-verification-algorithms/