diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 5ef204b946..10fc8152f6 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -42,7 +42,7 @@ outlined on that page and do not file a public issue.
 
 ## Coding Style
 
-* 4 or 2 spaces for indentation in C++ (no tabs)
+* 4 spaces for indentation in C++ (no tabs)
 * 80 character line length (both for C++ and Python)
 * C++ language level: C++17
 
diff --git a/INSTALL.md b/INSTALL.md
index dd04511bd2..ee08d8d2cf 100644
--- a/INSTALL.md
+++ b/INSTALL.md
@@ -1,6 +1,6 @@
 # Installing Faiss via conda
 
-The recommended way to install Faiss is through [conda](https://docs.conda.io).
+The supported way to install Faiss is through [conda](https://docs.conda.io).
 Stable releases are pushed regularly to the pytorch conda channel, as well as
 pre-release nightly builds.
 
@@ -77,7 +77,7 @@ found to run on other platforms as well, see
 
 The basic requirements are:
 - a C++17 compiler (with support for OpenMP support version 2 or higher),
-- a BLAS implementation (we strongly recommend using Intel MKL for best
+- a BLAS implementation (on Intel machines we strongly recommend using Intel MKL for best
 performance).
 
 The optional requirements are:
diff --git a/README.md b/README.md
index 0db380b807..bd1cf33a68 100644
--- a/README.md
+++ b/README.md
@@ -49,11 +49,22 @@ The main authors of Faiss are:
 - [Lucas Hosseini](https://github.com/beauby) implemented the binary indexes and the build system
 - [Chengqi Deng](https://github.com/KinglittleQ) implemented NSG, NNdescent and much of the additive quantization code.
 - [Alexandr Guzhva](https://github.com/alexanderguzhva) many optimizations: SIMD, memory allocation and layout, fast decoding kernels for vector codecs, etc.
+- [Gergely Szilvasy](https://github.com/algoriddle) build system, benchmarking framework.
 
 ## Reference
 
-Reference to cite when you use Faiss in a research paper:
-
+References to cite when you use Faiss in a research paper:
+```
+@article{douze2024faiss,
+      title={The Faiss library},
+      author={Matthijs Douze and Alexandr Guzhva and Chengqi Deng and Jeff Johnson and Gergely Szilvasy and Pierre-Emmanuel Mazaré and Maria Lomeli and Lucas Hosseini and Hervé Jégou},
+      year={2024},
+      eprint={2401.08281},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+```
+For the GPU version of Faiss, please cite:
 ```
 @article{johnson2019billion,
   title={Billion-scale similarity search with {GPUs}},