Fingerprints That Survive What? Model Lineage Names Three Relations, Robustness Is Indexed by Which Party Is Adversarial, and a Benchmark's Distillation Column Scores the Parent That Was Not Distilled From
Year: 2026
Type: preprint
Abstract: Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Language-model fingerprinting is asked to answer questions of the form "is this model derived from that one?", and its methods are routinely reported as robust to fine-tuning, quantization, pruning, merging and distillation. This paper argues that such reports are not comparable, for reasons that a common benchmark does not remove. The word lineage covers three different relations: weight descent, training-signal descent through distillation or imitation, and the identity of the checkpoint behind a served endpoint. A distilled model built on an open base has two parents, one for each of the first two relations, and the first systematic benchmark scores its distillation rows against the weight parent, so a perfect score there says nothing about finding the teacher. Robustness is also indexed by which party is adversarial: an evading host wants a missed match, a substituting provider wants a false match, and a false-claiming accuser wants a match against an independent model. The paper argues, as an inference from published results, that for similarity-based schemes the tolerance that buys robustness against the host is the opening the accuser uses, and that against the provider it is the weakness itself, since the cheapest substitutes are quantized or fine-tuned copies that a lineage fingerprint is built to accept. The evidence is drawn from roughly seventy published sources, among them a 2025 study in which adaptive attacks defeated eight of ten fingerprint schemes completely, and results showing that models trained independently on the same data, or fine-tuned on the same teacher's outputs, look related to output-level instruments. The paper contributes a partition, a decision procedure and a reporting protocol. Every figure it cites is credited to the source that reported it, and how often these confusions have decided real disputes is left as an open question for future work.
Author: Mahendrakar, Pranay
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7214937200