Update README.md

README.md

@@ -23,12 +23,12 @@ This repository explores the limits of sequence modeling, token predictability,
 
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-## 📌 Research Motivation: Can LLMs Learn $\pi$?
+## 📌 Research Motivation: Can LLMs Learn π?
 
 > **Core Hypothesis:**  
 > Can an ML model or LLM detect patterns in an arbitrary sequence of hexadecimal numerals of π and predict the subsequent digits?
 
-Because $\pi$ is conjectured to be a normal number in Base 16, its fractional expansion exhibits statistical properties virtually indistinguishable from an ideal pseudorandom sequence. Evaluating models on this data serves several key machine learning and data science purposes:
+Because π is conjectured to be a normal number in Base 16, its fractional expansion exhibits statistical properties virtually indistinguishable from an ideal pseudorandom sequence. Evaluating models on this data serves several key machine learning and data science purposes:
 
 1. **Synthetic Sequence Probing:** Testing the inductive biases and context-window dynamics of Transformers on truly non-periodic streams.
 2. **Memorization vs. Generalization:** Evaluating whether models are merely memorizing known chunks of π or attempting to synthesize algorithmic computation.