.

app.py

@@ -78,6 +78,16 @@ def build_fit_line_hovertemplate(label, a, b, c, raw_rmse, log_rmse):
     )
 
 
+def build_fit_summary_legend_text(label, a, b, c, raw_rmse, log_rmse, include_label=True):
+    parts = []
+    if include_label and label:
+        parts.append(label)
+    parts.append(format_fit_equation(a, b, c))
+    parts.append(f"Raw RMSE: {raw_rmse:.2f}")
+    parts.append(f"Log-RMSE: {log_rmse:.3f}")
+    return "<br>".join(parts)
+
+
 def read_about_md():
     with open("about.md", "r", encoding="utf-8") as f:
         return f.read()
@@ -950,14 +960,14 @@ def create_scaling_plot(data_manager: DataManager, period: str, use_pareto: bool
             x=fit_x.tolist(),
             y=fit_y.tolist(),
             mode="lines",
-            name=fit_type,
+            name=build_fit_summary_legend_text(fit_type, a, b, c, raw_rmse, log_rmse),
             line=dict(color=FIT_LINE_COLOR, width=2, dash="dash"),
             hovertemplate=build_fit_line_hovertemplate(fit_type, a, b, c, raw_rmse, log_rmse),
         )
     )
-
-    title_suffix = " (Pareto Frontier)" if use_pareto else ""
-    fig.update_layout(
+
+    title_suffix = " (Pareto Frontier)" if use_pareto else ""
+    fig.update_layout(
         title={"text": f"Compression Ratio Scaling Law{title_suffix}", "x": 0.5, "xanchor": "center", "yanchor": "top"},
         width=SCALING_PLOT_WIDTH,
         height=SCALING_PLOT_HEIGHT,
@@ -1040,8 +1050,7 @@ def create_category_scaling_plot(
     all_x_values = []
     all_y_values = []
     all_delta_values = []
-
-    if display_mode == "average":
+    if display_mode == "average":
         # 平均模式：计算选中数据集的平均值
         x_values = new_df["Params (B)"].astype(float).tolist()
         names = new_df["Name"].tolist()
@@ -1155,12 +1164,15 @@ def create_category_scaling_plot(
             # 添加拟合曲线
             fit_type = "Pareto Fit" if use_pareto else "Fit"
             fit_label = f"{datasets_label} ({fit_type})"
+            fit_legend_name = fit_label
+            if len(selected_datasets) == 1:
+                fit_legend_name = build_fit_summary_legend_text(fit_label, a, b, c, raw_rmse, log_rmse)
             fig.add_trace(
                 go.Scatter(
                     x=fit_x.tolist(),
                     y=fit_y.tolist(),
                     mode="lines",
-                    name=fit_label,
+                    name=fit_legend_name,
                     line=dict(color=FIT_LINE_COLOR, width=2, dash="dash"),
                     hovertemplate=build_fit_line_hovertemplate(fit_label, a, b, c, raw_rmse, log_rmse),
                 )
@@ -1301,12 +1313,15 @@ def create_category_scaling_plot(
             # 添加拟合曲线
             fit_type = "Pareto Fit" if use_pareto else "Fit"
             fit_label = f"{dataset} ({fit_type})"
+            fit_legend_name = dataset
+            if len(selected_datasets) == 1:
+                fit_legend_name = build_fit_summary_legend_text(dataset, a, b, c, raw_rmse, log_rmse)
             fig.add_trace(
                 go.Scatter(
                     x=fit_x.tolist(),
                     y=fit_y.tolist(),
                     mode="lines",
-                    name=dataset,
+                    name=fit_legend_name,
                     line=dict(color=color, width=2, dash="dash"),
                     hovertemplate=build_fit_line_hovertemplate(fit_label, a, b, c, raw_rmse, log_rmse),
                     legendgroup=dataset,