diff --git a/docs/index.md b/docs/index.md
index c8b504d..6552324 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -20,8 +20,7 @@ For certain classes of problems, this approach has been shown to provide signifi
 ### Documentation
 
 * [Installation and typical usage](usage.md)
-* [Benchmark utilities](benchmark.md)
-* [Benchmark problems, challenges and results](problems.md)
+* [Benchmark problems and results](problems.md)
 * [Customizing the solver](customization.md)
 * [License, authors, references and acknowledgments](about.md)
 
diff --git a/docs/usage.md b/docs/usage.md
index 04c6426..750d1d2 100644
--- a/docs/usage.md
+++ b/docs/usage.md
@@ -109,7 +109,7 @@ After the optimization process starts, MIPLearn will periodically call `find_vio
 
 By default, `LearningSolver` uses Machine Learning to accelerate the MIP solution process, while maintaining all optimality guarantees provided by the MIP solver. In the default mode of operation, for example, predicted optimal solutions are used only as MIP starts.
 
-For more significant performance benefits, `LearningSolver` can also be configured to place additional trust in the Machine Learning predictors, by using the `mode="heuristic"` constructor argument. When operating in this mode, if a ML model is statistically shown (through *stratified k-fold cross validation*) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods.  For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see [references](about.md#references) and [benchmark results](benchmark.md)).
+For more significant performance benefits, `LearningSolver` can also be configured to place additional trust in the Machine Learning predictors, by using the `mode="heuristic"` constructor argument. When operating in this mode, if a ML model is statistically shown (through *stratified k-fold cross validation*) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods.  For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see [references](about.md#references) and [benchmark results](problems.md)).
 
 
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