How do I verify that an open-weight model was actually trained from scratch?
机会
When a lab publishes weights and claims to have built them from scratch, there is no standard protocol for verifying the claim. Spectral fingerprinting via SVD decomposition, attention-based differential analysis, and refusal-vector behavioral comparison can each suggest a model is a fine-tuned derivative of Llama or DeepSeek, but the methods often disagree after quantization or pruning, no protocol is standardized, and any result can be disputed. The problem went commercially significant in 2026 when several labs making self-developed claims were found, under independent analysis, to be substantially more derivative than advertised, with legal and regulatory implications. EU AI Act high-risk model provisions now require provenance disclosure, but the technical machinery for making or verifying that disclosure does not exist at scale.
为什么重要
Without a reliable way to trace model lineage, IP law is unenforceable, safety audits lose their anchor, and regulatory compliance disclosures are unfalsifiable.
我如何评估机会
机会评分是我的个人判断，而非量化指标：痛苦程度、发生频率，以及当前解决方案的匮乏程度。分数越高，意味着我认为越值得去构建。
出现时造成的痛苦程度。
人们实际遇到它的频率。
当前针对它的优质工具有多匮乏。