Model Genome tool fingerprints LLM origins—from-scratch vs. derived

The Model Genome pipeline addresses a real gap exposed by late July 2026 releases. When several Korean organizations announced DeepSeek-rivaling "self-developed" models, the Chinese tech community—particularly Zhihu—erupted in debate (2.7M views), signaling genuine public interest in verifying vendor claims. The tool's insight is that lineage leaves detectable traces across three independent surfaces: the declared architecture, the learned vocabulary, and the numerical embedding space. Each axis is noisy on its own (architecture alone can be copied, tokenizers can be retrained independently, weights shift under continued training), but together they form a fingerprint.

The researchers hit two instructive obstacles that shaped their method. First, naive row-wise cosine similarity fails because transformers store information in a rotationally-invariant way—two models can be identical but differ only by an arbitrary rotation. Second, even CKA (which is rotation-invariant) struggles to separate continued-pretraining from from-scratch because large-scale training so thoroughly reshapes embeddings that a derivative and a true independent model become indistinguishable by this metric alone. The honest conclusion is that the weights axis confirms from-scratch (near-zero CKA) but is a weak detector of derivation, pushing config and tokenizer evidence into the primary role. Applying the same pipeline to nine Korean organizations' public models revealed heterogeneity: some exactly matched foreign architectures (Ported), others built their own (Native), most occupied a middle ground (Adapted or Mixed).