diff --git a/docs/NATS-Bench.md b/docs/NATS-Bench.md
index bdfc4b0..b42d816 100644
--- a/docs/NATS-Bench.md
+++ b/docs/NATS-Bench.md
@@ -35,6 +35,8 @@ To merge the chunks into the original full archive, you can use `cat file_name*
 |:-----------|:---------------------:|:-------------:|:------------------:|:-------------------------------:|:--------------------------:|:------------------:|
 | 2020.08.31 | [NATS-tss-v1_0-3ffb9.pickle.pbz2](https://drive.google.com/file/d/1vzyK0UVH2D3fTpa1_dSWnp1gvGpAxRul/view?usp=sharing) | [NATS-tss-v1_0-3ffb9-simple.tar](https://drive.google.com/file/d/17_saCsj_krKjlCBLOJEpNtzPXArMCqxU/view?usp=sharing) | [NATS-tss-v1_0-3ffb9-full](https://drive.google.com/drive/folders/17S2Xg_rVkUul4KuJdq0WaWoUuDbo8ZKB?usp=sharing) | [NATS-sss-v1_0-50262.pickle.pbz2](https://drive.google.com/file/d/1IabIvzWeDdDAWICBzFtTCMXxYWPIOIOX/view?usp=sharing) | [NATS-sss-v1_0-50262-simple.tar](https://drive.google.com/file/d/1scOMTUwcQhAMa_IMedp9lTzwmgqHLGgA/view?usp=sharing) | [NATS-sss-v1_0-50262-full](api.reload(index=12)) |
 
+These benchmark files (without pretrained weights) can also be downloaded from [Dropbox](https://www.dropbox.com/sh/gm0s1wrgcq10lhb/AAANoryEyR2Q9xHtSJJVE_bha?dl=0).
+
 
 1, create the benchmark instance:
 ```
diff --git a/lib/models/cell_searchs/search_model_darts_nasnet.py b/lib/models/cell_searchs/search_model_darts_nasnet.py
index 6f01186..6eb3278 100644
--- a/lib/models/cell_searchs/search_model_darts_nasnet.py
+++ b/lib/models/cell_searchs/search_model_darts_nasnet.py
@@ -80,6 +80,10 @@ class NASNetworkDARTS(nn.Module):
           for k, op_name in enumerate(self.op_names):
             if op_name == 'none': continue
             edges.append( (op_name, j, ws[k]) )
+        # (TODO) xuanyidong:
+        # Here the selected two edges might come from the same input node.
+        # And this case could be a problem that two edges will collapse into a single one
+        # due to our assumption -- at most one edge from an input node during evaluation.
         edges = sorted(edges, key=lambda x: -x[-1])
         selected_edges = edges[:2]
         gene.append( tuple(selected_edges) )