Equation 5 · The Hardest Unsolved Problems in Open-Weight AI
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the probability. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.
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This is a deliberately simplified model — real retention decisions are not independent, and q is not a single fixed number — but it isolates the right variable. Because copying an already-downloaded checkpoint costs approximately nothing, N grows with every re-share, and P() climbs toward one almost as soon as distribution starts, for any q short of certainty. This is a restatement, in one line of algebra, of what NTIA’s 2024 report and RAND’s 2024 report both conclude in prose: monitoring and mitigation are the available tools, and recall is not among them [ 2 , 3 ] .
Sources cited in the article section
- [15] Runaway LLaMA: How Meta's LLaMA NLP Model Leaked ↗
- [3] Securing AI Model Weights: Preventing Theft and Misuse of Frontier Models ↗
- [1] Our Position on Open-Weights Models ↗
- [5] LoRA Fine-tuning Efficiently Undoes Safety Training in Llama 2-Chat 70B ↗
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