Fingerprinting Large Language Models (LLMs)is essential for provenance verification and model attribution. Existing fingerprinting methods are primarily evaluated after fine-tuning, where models have already acquired stable signatures from training data, optimization dynamics, or hyperparameters. However, most of a model's capacity and knowledge are acquired during pretraining rather than downstream fine-tuning, making large-scale pretraining a more fundamental regime for lineage verification. We show that existing fingerprinting methods become unreliable in this regime, as they rely on post-hoc signatures that only emerge after substantial training. This limitation contradicts the classical Galton notion of a fingerprint as an intrinsic and persistent identity. In contrast, we propose a stronger and more intrinsic notion of LLM fingerprinting: SeedPrints, a method that leverages random initialization biases as persistent, seed-dependent identifiers present even before training begins. We show that untrained models exhibit reproducible prediction biases induced by their initialization seed, and that these weak signals remain statistically detectable throughout training, enabling high-confidence lineage verification. Unlike prior techniques that fail during early pretraining or degrade under distribution shifts, SeedPrints remains effective across all training stages, from initialization to large-scale pretraining and downstream adaptation. Experiments on LLaMA-style and Qwen-style models demonstrate seed-level distinguishability and enable birth-to-lifecycle identity verification. Evaluations on large-scale pretraining trajectories and real-world fingerprinting benchmarks further confirm its robustness under prolonged training, domain shifts, and parameter modifications.

翻译：指纹识别大型语言模型（LLM）对于溯源验证和模型归属至关重要。现有指纹识别方法主要在微调后进行评估，此时模型已从训练数据、优化动态或超参数中获得了稳定特征。然而，模型的大部分能力和知识是在预训练而非下游微调过程中习得的，这使得大规模预训练成为更基础的溯源验证阶段。我们证明现有指纹识别方法在此阶段不可靠，因为它们依赖仅在充分训练后才显现的事后特征。这一局限性违背了高尔顿关于指纹作为固有且持久身份标识的经典概念。与此相反，我们提出了一种更强且更本质的LLM指纹识别概念：SeedPrints——一种利用随机初始化偏差作为持久性、种子依赖标识符的方法，这些标识符甚至在训练开始前就已存在。我们证明未训练模型会展现出由初始化种子诱导的可复现预测偏差，且这些微弱信号在整个训练过程中仍可统计检测，从而实现高置信度的溯源验证。与先前在预训练早期失效或受分布偏移影响而退化的技术不同，SeedPrints从初始化到大规模预训练再到下游适配的所有训练阶段均保持有效性。在LLaMA风格和Qwen风格模型上的实验证明了种子级可区分性，并实现了从诞生到生命周期的身份验证。对大规模预训练轨迹和真实指纹识别基准的评估进一步证实了其在长期训练、领域偏移和参数修改下的鲁棒性。