stemma-induced-decision-regions-reveal-llm-provenance-8c983b20·1 events·first seen Aliases: Stemma: Induced Decision Regions Reveal LLM Provenance
Researchers introduce Stemma, a black-box LLM fingerprinting method that determines whether a suspect model belongs to the same lineage as a source model. The approach maps open-ended outputs into a finite decision space ('induced decision regions') to abstract away surface-form variation caused by fine-tuning or deployment, then measures inheritance of those regions as a provenance signal. Evaluated across 770 source-suspect pairs from 56 public checkpoints, Stemma achieves 0.967 AUC and 87.8% TPR at 1% FPR, substantially outperforming four baselines, with even stronger results on deployment-setting robustness tests. The method addresses a practical gap in model IP protection and lineage auditing as fine-tuned derivatives proliferate.