update bibliography

HEPML.bib

@@ -1,5 +1,16 @@
 # HEPML Papers
 
+% December 18, 2025
+@article{Araz:2025vuw,
+    author = "Araz, Jack Y. and Spannowsky, Michael",
+    title = "{Another Fit Bites the Dust: Conformal Prediction as a Calibration Standard for Machine Learning in High-Energy Physics}",
+    eprint = "2512.17048",
+    archivePrefix = "arXiv",
+    primaryClass = "hep-ph",
+    reportNumber = "IPPP/25/88",
+    month = "12",
+    year = "2025"
+}
 
 % November 11, 2025
 @article{Gavranovic:2025wcj,

HEPML.tex

@@ -214,9 +214,9 @@
 \item \textbf{Uncertainty Quantification}
 \\\textit{Estimating and mitigating uncertainty is essential for the successful deployment of machine learning methods in high energy physics. }
 	\begin{itemize}
-		\item \textbf{Interpretability}~\cite{Voetberg:2025fix,Vent:2025ddm,Erdmann:2025xpm,Chung:2025zib,Larkoski:2025clo,Kriesten:2024are,Gavrikov:2024rso,Wilkinson:2024xva,Ngairangbam:2023cps,Mengel:2023mnw,Barman:2023hbs,Roy:2022gge,Khot:2022aky,Grojean:2022mef,Anzalone:2022hrt,Bradshaw:2022qev,Mokhtar:2021bkf,Collins:2021pld,Romero:2021qlf,Grojean:2020ech,Agarwal:2020fpt,Diefenbacher:2019ezd,Chang:2017kvc,deOliveira:2015xxd}
+		\item \textbf{Interpretability}~\cite{Araz:2025vuw,Voetberg:2025fix,Vent:2025ddm,Erdmann:2025xpm,Chung:2025zib,Larkoski:2025clo,Kriesten:2024are,Gavrikov:2024rso,Wilkinson:2024xva,Ngairangbam:2023cps,Mengel:2023mnw,Barman:2023hbs,Roy:2022gge,Khot:2022aky,Grojean:2022mef,Anzalone:2022hrt,Bradshaw:2022qev,Mokhtar:2021bkf,Collins:2021pld,Romero:2021qlf,Grojean:2020ech,Agarwal:2020fpt,Diefenbacher:2019ezd,Chang:2017kvc,deOliveira:2015xxd}
 		\\\textit{Machine learning methods that are interpretable maybe more robust and thus less susceptible to various sources of uncertainty.}
-		\item \textbf{Estimation}~\cite{Romero:2025rck,Keller:2025bac,Peron:2025mtj,Benevedes:2025nzr,Desai:2025mpy,Elsharkawy:2025yeb,Khot:2025kqg,Kriesten:2024ist,Panahi:2024sfb,Bieringer:2024nbc,Dickinson:2023yes,Golutvin:2023fle,Koh:2023wst,Cheung:2022dil,Bellagente:2021yyh,Barnard:2016qma,Nachman:2019yfl,Nachman:2019dol}
+		\item \textbf{Estimation}~\cite{Araz:2025vuw,Romero:2025rck,Keller:2025bac,Peron:2025mtj,Benevedes:2025nzr,Desai:2025mpy,Elsharkawy:2025yeb,Khot:2025kqg,Kriesten:2024ist,Panahi:2024sfb,Bieringer:2024nbc,Dickinson:2023yes,Golutvin:2023fle,Koh:2023wst,Cheung:2022dil,Bellagente:2021yyh,Barnard:2016qma,Nachman:2019yfl,Nachman:2019dol}
 		\\\textit{A first step in reducing uncertainties is estimating their size.}
 		\item \textbf{Mitigation}~\cite{SuperCDMS:2025ywe,Azakli:2025yfb,Stein:2022nvf,Araz:2021wqm,Louppe:2016ylz,Englert:2018cfo,Estrade:DLPS2017}
 		\\\textit{This category is for proposals to reduce uncertainty.}

README.md

@@ -2608,6 +2608,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A
 ##  Uncertainty Quantification
 ###  Interpretability
 
+* [Another Fit Bites the Dust: Conformal Prediction as a Calibration Standard for Machine Learning in High-Energy Physics](https://arxiv.org/abs/2512.17048) (2025)
 * [NuGraph2 with Explainability: Post-hoc Explanations for Geometric Neural Network Predictions](https://arxiv.org/abs/2509.10676) (2025)
 * [How to Deep-Learn the Theory behind Quark-Gluon Tagging](https://arxiv.org/abs/2507.21214) (2025)
 * [What exactly did the Transformer learn from our physics data?](https://arxiv.org/abs/2505.21042) [[DOI](https://doi.org/10.1007/s41781-025-00145-4)] (2025)
@@ -2635,6 +2636,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A
 
 ###  Estimation
 
+* [Another Fit Bites the Dust: Conformal Prediction as a Calibration Standard for Machine Learning in High-Energy Physics](https://arxiv.org/abs/2512.17048) (2025)
 * [Efficient Estimation of Unfactorizable Systematic Uncertainties](https://arxiv.org/abs/2509.15500) (2025)
 * [Experimental Uncertainty Propagation in Neural Network Extraction in Hadronic Physics](https://arxiv.org/abs/2509.11456) (2025)
 * [Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments](https://arxiv.org/abs/2508.16518) (2025)

docs/index.md

@@ -2828,6 +2828,7 @@ const expandElements = shouldExpand => {
     ###  Interpretability
     </div>
 
+    * [Another Fit Bites the Dust: Conformal Prediction as a Calibration Standard for Machine Learning in High-Energy Physics](https://arxiv.org/abs/2512.17048) (2025)
     * [NuGraph2 with Explainability: Post-hoc Explanations for Geometric Neural Network Predictions](https://arxiv.org/abs/2509.10676) (2025)
     * [How to Deep-Learn the Theory behind Quark-Gluon Tagging](https://arxiv.org/abs/2507.21214) (2025)
     * [What exactly did the Transformer learn from our physics data?](https://arxiv.org/abs/2505.21042) [[DOI](https://doi.org/10.1007/s41781-025-00145-4)] (2025)
@@ -2860,6 +2861,7 @@ const expandElements = shouldExpand => {
     ###  Estimation
     </div>
 
+    * [Another Fit Bites the Dust: Conformal Prediction as a Calibration Standard for Machine Learning in High-Energy Physics](https://arxiv.org/abs/2512.17048) (2025)
     * [Efficient Estimation of Unfactorizable Systematic Uncertainties](https://arxiv.org/abs/2509.15500) (2025)
     * [Experimental Uncertainty Propagation in Neural Network Extraction in Hadronic Physics](https://arxiv.org/abs/2509.11456) (2025)
     * [Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments](https://arxiv.org/abs/2508.16518) (2025)