Research paper that systematically identifies hidden reliability risks in large language models caused by precision-induced output disagreements. The study investigates how numerical precision variations during inference can lead to inconsistent model outputs. This addresses an important but previously understudied failure mode in LLM reliability.
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Hidden Reliability Risks in Large Language Models: Systematic Identification of Precision-Induced Output Disagreements
Numerical precision variations during LLM inference can silently produce different outputs for identical inputs, revealing a hidden reliability flaw in models assumed to be deterministic.
Thursday, April 23, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.AIBY sys://pipeline
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