TokenPrint: A Calibrated Token-Space Fingerprint for Language-Model Provenance

arXiv:2511.08905v3 Announce Type: replace-cross Abstract: Given the high cost of large language model (LLM) training from scratch, safeguarding LLM intellectual property (IP) has

arXiv:2608.07786v1 Announce Type: new Abstract: Open-weight large language models (LLMs) are increasingly developed through complex, multi-stage pipelines, leading to intricate

arXiv:2604.13206v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly integrated into agentic workflows, their unpredictability stemming from numerical

arXiv:2605.04454v1 Announce Type: new Abstract: Alignment evaluation in machine learning has largely become evaluation of models. Influential benchmarks score model outputs under

arXiv:2609.31784v1 Announce Type: new Abstract: Open-weight models are often released, fine-tuned, aligned, merged, and re-released, making provenance audits ask not only whether

arXiv:2607.18445v1 Announce Type: cross Abstract: Regulatory regimes such as the EU AI Act mandate machine-readable marking of synthetic text, but existing watermark detectors

arXiv:2604.21952v1 Announce Type: cross Abstract: This work presents a multi-layered methodology for efficiently accelerating multimodal foundation models (MFMs). It combines

arXiv:2603.22303v1 Announce Type: cross Abstract: Hallucinations in large language models (LLMs) remain a central obstacle to trustworthy deployment, motivating detectors that are