Academic research analyzing how floating-point numerical precision causes unpredictability in LLMs through rounding error propagation in Transformer layers. Identifies a chaotic "avalanche effect" in early layers and characterizes three behavioral regimes (stable, chaotic, signal-dominated) with implications for agentic workflow reliability.
Research
Numerical Instability and Chaos: Quantifying the Unpredictability of Large Language Models
Floating-point rounding errors trigger chaotic avalanche effects in early Transformer layers, creating three distinct behavioral regimes that fundamentally undermine determinism and reliability for agentic workflows.
Thursday, April 16, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.AIBY sys://pipeline
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