diff --git a/readme.md b/readme.md
index 3d22960..b6fe320 100644
--- a/readme.md
+++ b/readme.md
@@ -1,26 +1,26 @@
-### Readme
+# Benchmark Source Code for ACORNS
 
-This contains all of the code used to generate JSON benchmarks and graphs for ACORNS: An easy-to-use Code Generator for Gradients and Hessians
+This repository contains all of the code used to generate JSON benchmarks and graphs for ["ACORNS: An easy-to-use Code Generator for Gradients and Hessians"](https://arxiv.org/abs/2007.05094).
 
-### About acorns
+### About ACORNS
 
-Please refer to the following repo for the package: https://github.com/deshanadesai/acorns
+Please refer to the following repo for the usage of the ACORNS package: https://github.com/deshanadesai/acorns
 
-This is NOT the repository for the acorns library. And is not maintained. This repository only contains code for the benchmarks for comparisons with other methods.
+This is NOT the repository for the ACORNS library and is not actively maintained. This repository only contains code for the benchmarks for comparisons with other methods.
 
 ### Prerequisites
 
-In order to run the benchmarking you need Pytorch installed, Adept installed, Java1.8 or higher and GCC9.
+In order to run the benchmarking you need PyTorch, Adept, Java (1.8 or higher) and GCC-9 installed.
 - To intall PyTorch simply using pip you can simply type `pip install torch`
 - To install Adept follow the instructions found [here](http://www.met.reading.ac.uk/clouds/adept/documentation.html)
   -   *NOTE*: Adept has been installed ONLY with `./configure` and the scripts rely on the location of the binaries.
-- On a mac you can find a working version of GCC9 with Homebrew and install it with `brew install gcc@9`
-- In order to graph you must have `matplotlib` installed which can be installed through pip.
+- On a mac you can find a working version of GCC-9 with Homebrew and install it with `brew install gcc@9`
+- In order to generate the graphs, you need `matplotlib` installed which is available through `pip install matplotlib`.
 
 ### Benchmarking Overview
 
 This hosts all of the code used to generate the data and graphs for [ACORNS](https://arxiv.org/abs/2007.05094)
-There are 5 main functions that generate the data:
+There are 5 main functions that are used to generate the data:
 1. `tests/updated_test_suite.py` - This runs ACORNS against PyTorch, Adept, Mitsuba, Enoki and Tapenade with respect to 2 hardcoded functions:
   - `((k*k+3*k)-k/4)/k+k*k*k*k+k*k*(22/7*k)+k*k*k*k*k*k*k*k*k`
   - `sin(k) + cos(k) + pow(k, 2)`
@@ -45,7 +45,7 @@ We graph our runtimes against all of the competitors in `./tests/graph/` folder.
 
 ### Real World Example
 
-In our paper we outline how we used this in a PolyFEM application. This was very complex and the C code was too big to include here. As such we simple calculated the times in JSON which can be found in the `./tests/complex/data`
+In our paper we outline how we used this in a [PolyFEM](https://polyfem.github.io/) application. We do not include all the external codebase here. However, we recorded the times in JSON, which can be found in the folder `./tests/complex/data`
 
 1. To graph file generation time run `./tests/complex/graph_file_gen.py`
 2. To graph file sizes run `./tests/complex/graph_file_sizes.py`