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BlockLock: Reproducible LLM Inference with Fixed Batch Geometry
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Researchers introduce BlockLock, a method for reproducible LLM inference using fixed batch geometry to eliminate batching variance.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 6 November 2026
- Collected by OneBench
- 7 Nov 2026, 02:01 UK
- Original headline
- BlockLock: Reproducible LLM Inference with Fixed Batch Geometry ↗
Stored source excerpt
arXiv:2611.06977v1 Announce Type: cross Abstract: Reproducible LLM inference requires that a request produces the same output regardless of the other requests in the batch. Existing…
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OneBench interpretation
Institutional assessment
So what
Deterministic LLM outputs are critical for auditability and model risk compliance in regulated financial workflows.
Do what
Review inference determinism requirements with the team responsible for enterprise AI platform infrastructure.