SourceArXiv AI•Stalecollected in 5h
LLM Chaos from Numerical Instability
#chaos#reliability#floating-pointllmsllmtransformer
💡Exposes why LLMs diverge from tiny errors—key for stable agents
⚡ 30-Second TL;DR
What Changed
Floating-point errors propagate chaotically in early Transformer layers
Why It Matters
This analysis explains root causes of LLM non-determinism, vital for reliable agentic systems. AI builders can prioritize precision tweaks to reduce chaos.
What To Do Next
Reproduce avalanche effect by perturbing float16 inputs in your Transformer model.
Who should care:Researchers & Academics
Key Points
- •Floating-point errors propagate chaotically in early Transformer layers
- •Avalanche effect leads to amplification or attenuation of perturbations
- •Three regimes: stable (vanish), chaotic (diverge), signal-dominated (override noise)
- •Universal scale-dependent behavior across LLMs
- •Impacts reliability in agentic workflows
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Original source: ArXiv AI ↗
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