reading the whitepapers for swarm-style / decentralized ai training protocols this week and i’m stunned by what isn’t in them: determinism.

gpu inference is famously flaky - tiny fp32 reorderings, atomics in convolutions, tensor-core down-casts, multi-stream races - all add up to different logits on the “same” forward pass. the literature is full of workarounds (cuDNN deterministic modes, ticket-lock kernels, frozen engine builds), yet none of that shows up in the glossy dtrain papers.

why care? if every peer in a mesh spits out slightly different gradients, good luck reaching onchain consensus or proving an honest contribution. verification costs explode, slashing logic breaks, and the whole “trust-minimized training” slogan starts to feel like an ideal than implementation.

so, crypto-ml twitter: who’s actually tackling non-determinism in a distributed, adversarial setting? any papers / blogs i should read? analogies to other consensus layers? drop links below