Research23h ago
Floating-Point Errors Cause LLM Unpredictability
A new paper identifies a chaotic "avalanche effect" in early Transformer layers where minor rounding errors trigger binary outcomes. This numerical instability stems from finite floating-point precision. The research tracks how these perturbations amplify across layers. Practitioners must now account for this inherent instability when deploying LLMs within high-reliability agentic workflows.