updated README

README.md

@@ -227,3 +227,25 @@ uv run python -m part_6.clc_experiment \
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 > **Note:** The LID experiment is memory-heavy. On a Tesla V100, `--train-n 100` can cause OOM — start with the default `--train-n 20` and scale up carefully.
+
+## Related: Extended Analysis — Ablation, Clustering & Synergy
+
+**[VSmague/NLP](https://github.com/VSmague/NLP)** — Extended experiments by Valentin Smague covering ablation studies, feature clustering, and cross-language synergy analysis built on top of the v-scores from this repo.
+
+That repo covers four additional directions:
+
+| Analysis | Script / Notebook | What it does |
+|---|---|---|
+| Feature ablation | `ablation.py`, `SNLP_ablation_clean.ipynb` | Ablates top language-specific SAE features and measures the effect on model behavior; produces per-language specificity plots |
+| Language clustering | `compute_clusters.py`, `compute_matrix.py` | Clusters languages by their v-score feature overlap using MDS and similarity matrices |
+| Cross-language synergy | `cross_language_synergy.py` | Measures how much top features for one language also activate on other languages (feature sharing / synergy) |
+| Visualization | `visualisation.py`, `reprod.py` | Reproduces v-score bar charts (Figure 1 style) and generates additional plots |
+
+Key outputs stored in the repo:
+
+- `v_scores.png` — reproduced v-score figure
+- `ablation_fr.png`, `ablation_specificity.png` — ablation results for French
+- `clustering_best.png`, `clustering_comparison.png`, `clustering_mds.png` — language clustering visualizations
+- `plots/`, `plots_interaction/`, `plots_synergy/` — full plot collections
+- `sae_features/` — saved SAE feature data
+- `figures_section5/` — figures for section 5 of the report