Reproduce “BlockLock: Reproducible LLM Inference with Fixed Batch Geometry” on OpenResearch

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About this paper

Xiuyuan Qi, Pedro Reviriego, Javier Conde, Shanshan Liu

BlockLock makes a target request’s inference repeatable across companion-perturbed batches by fixing its execution geometry within a deployment. Its Rows and Pack modes retain shared prefill while keeping the target’s layout fixed, with different decode work. Across 1,316 single-GPU conditions, both achieved 100% sequence and Top-20 logit agreement, unlike padded batching; this does not establish determinism across deployments.

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