AI模型也有“血缘鉴定”了
开源大模型被微调、量化、剪枝、合并后，往往没人记录它的“家谱”。这篇研究只靠模型权重本身，就能判断两个模型是不是“一家人”——即使它们被改得面目全非。方法很巧妙：训练会在模型里留下一种“身份印记”，研究者先抹掉所有模型共有的部分，再比较剩下的独特结构，相似度高的就是同源。在多个测试中，它能100%准确区分“亲缘”模型和“陌生人”，而且比现有方法快76倍。这不是你明天能用的工具，但它给AI治理提供了一种“验DNA”的手段：以后想查某个模型是不是偷偷从另一个模型改来的，不用看数据、不用跑测试，直接看权重就行。
📄 原文摘要(英文)
Open-weight language models are fine-tuned, quantized, pruned, and merged, yet their provenance is often undocumented. We study data-free white-box lineage verification: can weights alone reveal whether two compatible model checkpoints share ancestry? Residual training produces a shared identity-aligned component in branch products, so this structure alone cannot establish ancestry. We remove it and compare checkpoint-specific structure across residual blocks, yielding a symmetric lineage score calibrated against independent checkpoints. On residual-MLP and GPT-2 benchmarks, the score separates fine-tuned, LoRA-merged, pruned, and quantized descendants from independent and distilled models (AUROC=1.0), distinguishing weight ancestry from behavioral similarity. Under function-preserving checkpoint laundering experiments, weight-space baselines lose margin or fail; our score remains unchanged and runs 76x faster than the nearest robust baseline on GPT-2. The projection-pairing signal appears across six language-model families and beyond, and a case study correctly identifies 3 related and 7 unrelated LLaMA-2 public checkpoints. Collectively, these results establish a passive, data-free provenance signal for compatible open-weight language-model checkpoints