From Representations to Computational Trajectories: Intrinsic Fingerprinting for Large Language Model Provenance Verification

Summary

Researchers propose TrFP, an intrinsic fingerprinting method for verifying the provenance of large language models without modifying them or embedding ownership information. Instead of comparing parameters or representations, TrFP captures how representations evolve across network depth and builds compact fingerprints from permutation-invariant trajectory descriptors. Experiments across multiple LLM families show it reliably distinguishes related and unrelated models and stays robust under fine-tuning, pruning, quantization, model merging, parameter permutation, and adaptive attacks, while needing less than 0.3 MiB of fingerprint storage.