Update README.md

README.md

@@ -12,9 +12,54 @@ short_description: 'Visualization of human vs simulated interaction dynamics '
 license: mit
 ---
 
-# Welcome to Streamlit!
+# TwinTrack
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+**Observed vs Simulated Interation Dynamics**
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+TwinTrack is a lightweight visualization tool for comparing long-form human-LLM interactions against synthetic persona simulations, stripped of semantic content and analyzed purely by temporal shape.
+
+This is not about *what* is said. It is about how interation unfolds over time.
+
+# What TwinTrack Shows
+
+**Top:** A human long-form interaction (Big Flame)
+**Bottom:** A selected synthetic persona (Minimalist, Cheerleader, Poet)
+
+Each band visualizes rolling response variance across normalized interaction time.
+Same scale. Same normalization. Different dynamics. 
+
+Synthetic personas often look "reasonable" in isolation. When placed next to a human thread, their temporal fingerprints tell a different story. 
+
+# How it Works (High Level)
+- Conversation threads are converted to numeric features (length, variance)
+- Time is normalized to a 0-1 scale to allow comparison across unequal thread lenghts
+- A rolling window computes variance over interaction time
+- Tow bands are rendered using a shared color scale for direct visual comparison
+
+No embeddings.
+No sentiment labels.
+No semantic interpretation.
+Just shape. 
+
+# TwinTrack is NOT
+- A benchmark
+- A performance metric
+- A truth or safety score 
+- A semantic analyzer
+
+TwinTrack is an instrument - not a verdict
+
+# Status
+- v1: Working prototype
+- Interaction persona selection
+- Normalized temporal alignment
+- Shared color scale for interpretability
+
+Future iterations may explore:
+- Additional perturbation controls
+- Alternative variance measures
+- Multi-human baselines
+
+# Related Work
+Beyond Content: Temporal Dynamics in Long-Form Human-LLM Interation
+DOI: https://doi.org/10.5281/zenodo.18273459