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Model-intrinsic, Weight-level, and Training-free Spectral Fingerprinting for Large Language Models

Yanan He, Yanli Chen, Ling Xiong, Gejian Zhao, Hanzhou Wu · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.30626 · cited by 0 works (OpenAlex) · open-access PDF

Russian

Все существующие методы отпечатков пальцев обычно конструируют доказательства от ответов на запросы, генерируемых выходов или индуцированных активаций. Хотя они эффективны в настройках, основанных на представлении, отпечатки пальцев модели могут быть затронуты выбором проб, конфигурацией декодирования, средой вывода и внедрением forward-pass, что может ограничить их применимость. Здесь мы вводим модель-интринную структуру на уровне веса, которая получает компактные отпечатки пальцев от спектральных структур, кодируемых в параметрах LLM. В частности, рамка конструирует спектральные характеристики GATE от спектральных характеристик от дистрибуций единоличной стоимости проекции GATE, которые представляют собой различимые весы

Machine translation (Meta M2M100); the English original follows.

Abstract

The increasing release and reuse of Large Language Models (LLMs) as open-weight checkpoints make model fingerprinting important for ownership protection and offline auditing. Existing fingerprinting methods typically construct evidence from query responses, generated outputs, or input-induced activations. Although effective in representation-based settings, they can be affected by prompt selection, decoding configuration, inference environment, and forward-pass implementation, which may limit their applicability. Here we introduce a model-intrinsic weight-level framework that derives compact fingerprints from spectral structures encoded in LLM parameters. Specifically, the framework constructs GATE spectral scalar features from the singular-value distributions of GATE projection weights in gated feed-forward networks (FFNs), and aggregates the resulting layer-wise features through depth-wise segmented pooling to form a model-level fingerprint. Such representation preserves coarse layer-depth organization while reducing dependence on strict layer-to-layer alignment, enabling comparison across LLMs with different depths. The similarity between fingerprints is computed by linear centered kernel alignment. The entire pipeline is training-free and does not require input samples, generated outputs, forward activations, or an auxiliary verifier. Experiments on representative LLM families and post-training variants have demonstrated a clear separation between source-related models and unrelated ones, and stable similarity under instruction tuning, quantization, pruning, and distillation-related modifications, which verify that spectral features are robust evidence for LLM fingerprinting.

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Topics: Computer science, Decoding methods, Pipeline (software), Inference, Pooling, Artificial intelligence

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