Published June 19, 2026
| Version v2
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Mercury Model Birthmarks: Structural Signatures of Token Cost, Latency, and Hidden Defects Across LLM Scales
Description
A Mercury methods paper defining model birthmarks as stable structural signatures of tokenizer burden, latency habits, routing scars, dead-tissue regions, alignment reflexes, and compression scars. The paper argues that parameter count is not a health report and proposes measurable birthmark vectors for language model inspection. Copyright (c) 2026 Chen, Ho Yiing (norika). Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).
Notes
Author ORCID: https://orcid.org/0009-0006-6816-9891. Draft-only Zenodo record prepared for author review; not published. Mercury series artifact from the Hope of City / LLM City measurement program.
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References
- Kaplan, Jared; McCandlish, Sam; Henighan, Tom; Brown, Tom B.; Chess, Benjamin; Child, Rewon; Gray, Scott; Radford, Alec; Wu, Jeffrey; and Amodei, Dario. Scaling Laws for Neural Language Models. arXiv:2001.08361, 2020. https://arxiv.org/abs/2001.08361
- Hoffmann, Jordan; Borgeaud, Sebastian; Mensch, Arthur; Buchatskaya, Elena; Cai, Trevor; Rutherford, Eliza; de Las Casas, Diego; Hendricks, Lisa Anne; Welbl, Johannes; Clark, Aidan; Hennigan, Tom; Noland, Eric; Millican, Katie; van den Driessche, George; Damoc, Bogdan; Guy, Aurelia; Osindero, Simon; Simonyan, Karen; Elsen, Erich; Rae, Jack W.; Vinyals, Oriol; and Sifre, Laurent. Training Compute-Optimal Large Language Models. arXiv:2203.15556, 2022. https://arxiv.org/abs/2203.15556
- Mitchell, Margaret; Wu, Simone; Zaldivar, Andrew; Barnes, Parker; Vasserman, Lucy; Hutchinson, Ben; Spitzer, Elena; Raji, Inioluwa Deborah; and Gebru, Timnit. Model Cards for Model Reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019. https://arxiv.org/abs/1810.03993
- Frantar, Elias; Ashkboos, Saleh; Hoefler, Torsten; and Alistarh, Dan. GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers. arXiv:2210.17323, 2022. https://arxiv.org/abs/2210.17323
- Elhage, Nelson; Nanda, Neel; Olsson, Catherine; Henighan, Tom; Joseph, Nicholas; Mann, Ben; Askell, Amanda; Bai, Yuntao; Chen, Anna; Conerly, Tom; DasSarma, Nova; Drain, Dawn; Ganguli, Deep; Hatfield-Dodds, Zac; Hernandez, Danny; Jones, Andy; Kernion, Jackson; Lovitt, Liane; Ndousse, Kamal; Amodei, Dario; Brown, Tom; Clark, Jack; Kaplan, Jared; McCandlish, Sam; and Olah, Chris. A Mathematical Framework for Transformer Circuits. Transformer Circuits, 2021. https://transformer-circuits.pub/2021/framework/index.html