diff --git a/README.md b/README.md
index 0470fa7..457a947 100644
--- a/README.md
+++ b/README.md
@@ -108,7 +108,7 @@ watcher.distances(model_1, model_2)
 ## PEFT / LORA models  (experimental)
 To analyze an PEFT / LORA fine-tuned model, specify the peft option.
 
- - peft = True:  Forms the BA low rank matric and analyzes the delta layers, with 'lora_BA" tag in name
+ - peft = True:  Forms the BA low rank matrix and analyzes the delta layers, with 'lora_BA" tag in name
  
    ```details = watcher.analyze(peft='peft_only')```
 
@@ -439,7 +439,7 @@ details = watcher.distances(initial_model, trained_model)
 
 ---
 
-#### compatability with version 0.2.x
+#### Compatibility with version 0.2.x
 
 The new 0.4.x version of WeightWatcher treats each layer as a single, unified set of eigenvalues.
 In contrast, the 0.2.x versions split the Conv2D layers into n slices, one for each receptive field.
@@ -543,9 +543,14 @@ This tool is based on state-of-the-art research done in collaboration with UC Be
 <summary>
 WeightWatcher has been featured in top journals like JMLR and Nature:	
 </summary>
-#### Latest papers and talks
+	
+### Latest papers and talks
+
+- [Grokking and Generalization Collapse: Insights from HTSR theory (available upon request)]
 
-- [SETOL: A Semi-Empirical Theory of (Deep) Learning] (in progress)
+- [SETOL: A Semi-Empirical Theory of (Deep) Learning (draft)] (https://github.com/CalculatedContent/setol_paper/blob/main/setol_draft.pdf)
+
+- [Temperature Balancing, Layer-wise Weight Analysis, and Neural Network Training (NeurIPS 2023 Spotlight Paper)(https://arxiv.org/abs/2312.00359)
 
 - [Post-mortem on a deep learning contest: a Simpson's paradox and the complementary roles of scale metrics versus shape metrics](https://arxiv.org/abs/2106.00734)
 
@@ -591,7 +596,11 @@ and has been presented at Stanford, UC Berkeley, KDD, etc:
 - [KDD 2019 Workshop: Statistical Mechanics Methods for Discovering
   Knowledge from Production-Scale Neural Networks](https://dl.acm.org/doi/abs/10.1145/3292500.3332294)
 
-- [KDD 2019 Workshop: Slides](https://www.stat.berkeley.edu/~mmahoney/talks/dnn_kdd19_fin.pdf) 
+- [KDD 2019 Workshop: Slides](https://www.stat.berkeley.edu/~mmahoney/talks/dnn_kdd19_fin.pdf)
+
+#### NeurIPS 2023 
+- [Heavy-Tailed Self-Regularization in Deep Neural Networks](https://neurips.cc/virtual/2023/83033)
+
 	
 </details>
 
@@ -600,7 +609,7 @@ and has been presented at Stanford, UC Berkeley, KDD, etc:
 WeightWatcher has also been featured at local meetups and many popular podcasts
 </summary>
 	
-#### Popular Popdcasts and Blogs
+#### Popular Podcasts and Blogs
 
 - [This Week in ML](https://twimlai.com/meetups/implicit-self-regularization-in-deep-neural-networks/)
  
@@ -618,9 +627,26 @@ WeightWatcher has also been featured at local meetups and many popular podcasts
 
 - [Applied AI Community](https://www.youtube.com/watch?v=xLZOf2IDLkc&feature=youtu.be)
 
+- [UCL Financial Computing (2022)](https://www.youtube.com/watch?v=sOXROWJ70Pg)
+
 - [Practical AI](https://changelog.com/practicalai/194)
 
-- [Latest Results](https://www.youtube.com/watch?v=rojbXvK9mJg)
+- [AI Nation 2023](https://www.youtube.com/watch?v=rojbXvK9mJg)
+
+- [ICCF 2024](https://youtu.be/_c0-_ru0sZc)
+
+- [Data Science at Home (2025)](https://www.youtube.com/watch?v=iv7Pv3StHms)
+
+- [Cohere for AI 2025](https://www.youtube.com/watch?v=NXqO4nDNIwo)
+
+- [The FreeStyle Podcast](https://www.youtube.com/watch?v=hb0YrwQ3K2Q)
+
+- [This Week in ML AI Podcast](https://twimlai.com/podcast/twimlai/grokking-generalization-collapse-and-the-dynamics-of-training-deep-neural-networks/)
+
+
+  and many more 
+
+
 
 #### 2021 Short Presentations
 
@@ -628,6 +654,9 @@ WeightWatcher has also been featured at local meetups and many popular podcasts
 
 - [PyTorch2021 Poster  April 2021](presentations/pytorch2021_poster.pdf)
 
+#### TEDx Talk
+- [The Emergence of Signatures of Artificial General Intelligence ](https://www.youtube.com/watch?v=5dBEzqTlq-Y)
+
 #### Recent talk(s) by Mike Mahoney, UC Berekely
 
 - [IARAI, the Institute for Advanced Research in Artificial Intelligence](https://www.youtube.com/watch?v=Pirni67ZmRQ)