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@@ -153,6 +153,8 @@ $$f_\theta = -\nabla_h V_\theta = -\sum_{k=1}^{K} q_k(\xi, h) \cdot a_k(\xi) \od
 where \\(q_k\\) are the softmax responsibilities over the 8 quadratic wells. The \\(V_\phi\\) force still uses autograd (sparse, over top-k=8 neighbours only).
 ## How to Get Started
 ```python

 where \\(q_k\\) are the softmax responsibilities over the 8 quadratic wells. The \\(V_\phi\\) force still uses autograd (sparse, over top-k=8 neighbours only).
+For full derivations, all four structured variants (SQ1--SQ4), landscape compression analysis, attractor basin decoding, and hyperparameter selection strategies, see the companion note: [`Structured_VTheta_Design_and_Theory.md`](https://github.com/dimitarpg13/semsimula-paper/blob/main/companion_notes/Structured_VTheta_Design_and_Theory.md).
 ## How to Get Started
 ```python