diff --git a/neurips23/leaderboard.md b/neurips23/leaderboard.md
index 84a105f1..730e11de 100644
--- a/neurips23/leaderboard.md
+++ b/neurips23/leaderboard.md
@@ -6,8 +6,8 @@ The recall of the baselines at this QPS threshold is listed [above](#measuring_y
 For tasks "Filter", "Out-of-Distribution" and "Sparse" tracks, algorithms were ranked on the QPS they achieve on the track dataset, as long as the recall@10 is at least 90%. 
 These results files for [Azure D8lds_v5](Azure_D8lds_v5_table.md) and [AWS EC2 c6i.2xlarge](ec2_c6i.2xlarge_table.md) list the maximum QPS measured for each algorihtm with at least 90% recall@10.
 
-For the Streaming track, algorithms will be ranked on recall@10, as long as each algorithm completes the runbook within the alloted 1 hour. 
-The [result file](streaming/res_final_runbook_AzureD8lds_v5.csv) lists measurements on Azure D8lds_v5.
+For the Streaming track, algorithms were ranked on recall@10, as long as each algorithm completes the runbook within the alloted 1 hour. The leading entry had a recall of 0.9849.
+The [result file](streaming/res_final_runbook_AzureD8lds_v5.csv) lists measurements for all streaming algorithms on Azure D8lds_v5.
 
 QPS vs recall@10 plots for tracks based on public queries on Azure D8lds_v5:
 **Filter track**
@@ -18,3 +18,5 @@ QPS vs recall@10 plots for tracks based on public queries on Azure D8lds_v5:
 
 **Sparse track**
 ![sparse-full](sparse/plot_public_queries_AzureD8lds_v5.png)
+
+More plots to follow.