Disposable Projections vs Durable Truth: Engineering Self-Healing Cloud State Against Split-Brain

In distributed state engines, conflating temporary projection caches with immutable ledgers leads to split-brain corruption during disaster recovery. We evaluated whether frontier models can engineer self-healing infrastructure that survives deliberate state sabotage.

In this evaluation, we analyzed the divergence between specification-driven architectural requirements and the actual solutions synthesized by frontier models:

Financial institutions and high-security compliance hubs maintain immutable dataset promotions. While primary transaction records must never be lost, secondary query indexes are designed to be ephemeral and rebuildable. When an infrastructure failure strikes, automated recovery workflows must reconcile cloud state without causing split-brain divergences.

The diagram below illustrates the multi-tier cloud topology authored for this evaluation. Note the decoupling of streaming ingress, compute containers, durable state ledgers, and dead-letter recovery:

The evaluation environment spans S3 versioned storage vaults, durable DynamoDB ledgers, rebuildable projection indexes, recovery authority verification tables, controller Lambdas running in stage, commit, and reindex modes, and lifecycle scripts governed by mutual-exclusion locks (.lifecycle-held). The long-horizon challenge requires the model to manage state transitions across multiple simulated disaster recovery events, proving that the infrastructure converges deterministically after deliberate state corruption.

Autonomous models must learn to differentiate immutable truth from disposable projections. Reinforcement learning environments that sabotage state mid-run provide the only empirical proof of self-healing capability.