diff --git a/docs/cutoff/topk.md b/docs/cutoff/topk.md
index 94ebd99..f6f4e64 100644
--- a/docs/cutoff/topk.md
+++ b/docs/cutoff/topk.md
@@ -1,11 +1,29 @@
 # Top-K
 
-## Training
-
-```sh
-sh scripts/DirectTraining/tet/training.sh
-```
-## Evaluation
-```sh
-sh scripts/DirectTraining/tet/evaluation.sh
-```
\ No newline at end of file
+## Optimal Cutoff Timestep (OCT)
+
+$$
+g(\boldsymbol{X}) = \arg\min_{\hat{t}}\{  \forall \hat{t}_1 > \hat{t}: \mathbf{1}(f(\boldsymbol{X}[\hat{t}_1])= \boldsymbol{y})\}
+$$
+
+## Top-K Gap for Cutoff Approximation
+
+The defination of $Top_k(\boldsymbol{Y}(t))$ as the top-$k$ output occurring in one neuron of the output layer,
+
+$$
+Y_{gap}= Top_1(\boldsymbol{Y}(t)) - Top_2(\boldsymbol{Y}(t)),
+$$
+
+which denotes the gap of top-1 and top-2 values of output $\boldsymbol{Y}(t)$. Then, we let $ D\{\cdot\}$ denote the inputs in subset of $D$ that satisfy a certain condition. Now, we can define the confidence rate as follows:
+
+$$
+\textit{Confidence rate: } C(\hat{t}, D\{Y_{gap}>\beta\}) = \frac{1}{|D\{Y_{gap}>\beta\}|}\sum_{\boldsymbol{X}\in D\{Y_{gap}>\beta\}} (g(\boldsymbol{X}) \leq \hat{t}),
+$$
+
+The algorithm searches for a minimum $\beta \in \mathbb{R^+}$ at a specific $\hat t$, as expressed in the following optimization objective:
+
+$$
+\arg\min_{\beta} C(\hat t, D\{Y_{gap} > \beta\}) \geq 1-\epsilon,
+$$
+
+where $\epsilon$ is a pre-specified constant such that $1-\epsilon$ represents an acceptable level of confidence for activating cutoff, and a set of $\beta$ is extracted under different $\hat t$ using training samples.