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 **B**ayesian **I**nformation-**T**heoretic **S**ampling for hierarchical **GA**ussian **P**rocess **S**urrogates.
 
+![BITS for GAPS framework overview: a hierarchical Gaussian process surrogate (GAPS) places priors on the kernel hyperparameters; Bayesian information-theoretic sampling (BITS) repeatedly selects the next input by maximizing the predictive differential entropy and collects data there; the result is an information-optimal surrogate whose predictions average over the hyperparameter posterior.](https://raw.githubusercontent.com/dowlinglab/bits_for_gaps/main/docs/graphical_abstract.jpg)
+
+*Graphical abstract from the paper (© 2026 The Authors, [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)).*
+
 A framework for information-theoretic sequential experimental design with Bayesian
 hierarchical Gaussian-process surrogates. Prior physical knowledge is encoded through
 priors on the GP hyperparameters; sampling is guided by maximizing the predictive
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 and ignore uncertainty in the hyperparameters themselves, which (we argue) matters most exactly when data are scarce.
 BITS for GAPS addresses this limitation.
 
+```{figure} graphical_abstract.jpg
+:alt: BITS for GAPS framework overview: a hierarchical Gaussian process surrogate (GAPS) places priors on the kernel hyperparameters; Bayesian information-theoretic sampling (BITS) repeatedly selects the next input by maximizing the predictive differential entropy and collects data there; the result is an information-optimal surrogate whose predictions average over the hyperparameter posterior.
+:align: center
+:width: 100%
+
+The BITS for GAPS framework. A hierarchical GP surrogate (**GAPS**) carries priors on its
+kernel hyperparameters; **BITS** repeatedly picks the next input by maximizing the
+predictive differential entropy of the hierarchical posterior, collects data there, and
+updates the surrogate; predictions then average over the hyperparameter posterior.
+*Graphical abstract from the paper (© 2026 The Authors,
+[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)).*
+```
+
 ```{admonition} Reference
 K. D. Jones and A. W. Dowling, "BITS for GAPS: Bayesian Information-Theoretic
 Sampling for hierarchical GAussian Process Surrogates," *Computers & Chemical