dolphin3.0-r1-mistral-24b · cognitivecomputations

Drift by provider

The same model, served by every provider, on one timeline. Each strip is that provider's drift from its own reference period; dots mark detected changes, placed at the drift level they reached. A model version is nominally fixed — so a change here is the provider's serving stack moving, and it should stand out against providers that stay flat. Shared y-scale within each method, so strip heights are comparable.

LT change B3IT change line = drift from baseline · dot height = how far it moved hover or tap a strip to read every provider on the same day

Timothée Chauvin, Erwan Le Merrer, François Taïani, Gilles Tredan (2026), "Log Probability Tracking of LLM APIs". ICLR 2026. https://arxiv.org/abs/2512.03816

Timothée Chauvin, Clément Lalanne, Erwan Le Merrer, Jean-Michel Loubes, François Taïani, Gilles Tredan (2026), "Token-Efficient Change Detection in LLM APIs". ICML 2026. https://arxiv.org/abs/2602.11083

@inproceedings{
chauvin2026log,
title={Log Probability Tracking of {LLM} {API}s},
author={Timothee Chauvin and Erwan Le Merrer and Francois Taiani and Gilles Tredan},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=hFxivbAgVP}
}

@inproceedings{
chauvin2026tokenefficient,
title={Token-Efficient Change Detection in {LLM} {API}s},
author={Timothee Chauvin and Cl{\'e}ment Lalanne and Erwan Le Merrer and Jean-Michel Loubes and Francois Taiani and Gilles Tredan},
booktitle={Forty-third International Conference on Machine Learning},
year={2026},
url={https://openreview.net/forum?id=7cMlZZYZT0}
}