diff --git a/src/bin/sink_state_enumerator.rs b/src/bin/sink_state_enumerator.rs
new file mode 100644
index 0000000..0633f64
--- /dev/null
+++ b/src/bin/sink_state_enumerator.rs
@@ -0,0 +1,105 @@
+use biodivine_aeon_server::scc::algo_interleaved_transition_guided_reduction::interleaved_transition_guided_reduction;
+use biodivine_aeon_server::scc::algo_xie_beerel::xie_beerel_attractors;
+use biodivine_aeon_server::scc::{Behaviour, Classifier};
+use biodivine_aeon_server::GraphTaskContext;
+use biodivine_lib_param_bn::biodivine_std::bitvector::BitVector;
+use biodivine_lib_param_bn::biodivine_std::traits::Set;
+use biodivine_lib_param_bn::symbolic_async_graph::SymbolicAsyncGraph;
+use biodivine_lib_param_bn::BooleanNetwork;
+use std::convert::TryFrom;
+use std::io::Read;
+use std::time::SystemTime;
+
+fn main() {
+    let mut buffer = String::new();
+    std::io::stdin().read_to_string(&mut buffer).unwrap();
+
+    let start = SystemTime::now();
+
+    let model = BooleanNetwork::try_from(buffer.as_str()).unwrap();
+    let names: Vec<_> = model
+        .variables()
+        .map(|id| model.get_variable_name(id))
+        .collect();
+    println!("Model loaded...");
+    println!("{} variables: {:?}", model.num_vars(), names);
+
+    let graph = SymbolicAsyncGraph::new(model.clone()).unwrap();
+
+    println!("Asynchronous graph ready...");
+    println!(
+        "Admissible parametrisations: {}",
+        graph.unit_colors().approx_cardinality()
+    );
+    println!(
+        "State space: {}",
+        graph.unit_colored_vertices().approx_cardinality()
+    );
+
+    let classifier = Classifier::new(&graph);
+    let task_context = GraphTaskContext::new();
+    task_context.restart(&graph);
+
+    // Now we can actually start the computation...
+
+    // First, perform ITGR reduction.
+    let (universe, active_variables) = interleaved_transition_guided_reduction(
+        &task_context,
+        &graph,
+        graph.mk_unit_colored_vertices(),
+    );
+
+    // Then run Xie-Beerel to actually detect the components.
+    xie_beerel_attractors(
+        &task_context,
+        &graph,
+        &universe,
+        &active_variables,
+        |component| {
+            println!("Found attractor... {}", component.approx_cardinality());
+            println!("Remaining: {}", task_context.get_percent_string());
+            println!(
+                "Unique states: {}",
+                component.vertices().approx_cardinality()
+            );
+            println!("Unique colors: {}", component.colors().approx_cardinality());
+            classifier.add_component(component, &graph);
+        },
+    );
+
+    classifier.print();
+
+    println!(
+        "Analysis completed. Classes: {}",
+        classifier.export_result().len()
+    );
+    println!("Elapsed time: {}s", start.elapsed().unwrap().as_secs());
+
+    let mut all_sinks = graph.mk_empty_vertices();
+    for (attractor, behaviours) in classifier.export_components() {
+        if let Some(sink_colors) = behaviours.get(&Behaviour::Stability) {
+            all_sinks = all_sinks.union(&attractor.intersect_colors(sink_colors));
+        }
+    }
+
+    println!(
+        "Explicit sinks ({} states):",
+        all_sinks.vertices().approx_cardinality()
+    );
+
+    for vertex in all_sinks.vertices().materialize().iter() {
+        let values = model
+            .variables()
+            .enumerate()
+            .map(|(i, v)| (model.get_variable_name(v).clone(), vertex.get(i)))
+            .collect::<Vec<_>>();
+        for (name, value) in values {
+            print!("{}: {}; ", name, value);
+        }
+        let sink = graph.vertex(&vertex);
+        println!(
+            "{} instance(s).",
+            sink.intersect(&all_sinks).approx_cardinality()
+        );
+    }
+}