diff --git a/scripts/plot_dynamics.py b/scripts/plot_dynamics.py
index 3b1cfca..3860fc6 100644
--- a/scripts/plot_dynamics.py
+++ b/scripts/plot_dynamics.py
@@ -213,6 +213,19 @@ def _onset(steps: np.ndarray, hack: np.ndarray) -> int | None:
     return int(steps[nz[0]]) if len(nz) else None
 
 
+def _mark_if_sparse(ax, x: np.ndarray, y_raw: np.ndarray, color, alpha=1.0) -> None:
+    """Dot the REAL measured points when a series is mostly-NaN. route2's plotted
+    hack/solve is the DEPLOY eval (hk_dep), sampled every eval_ablate_every steps and
+    EMA-held flat between -- without the dots the held line looks per-step-dense and
+    oversells route2's smoothness vs the per-step-sampled training curves (Tufte:
+    the rendering must not imply more data than was measured). Dense series (every
+    step finite, e.g. training hack_s, cos sep/leak) stay unmarked to avoid clutter."""
+    finite = np.isfinite(y_raw)
+    if finite.sum() and finite.mean() < 0.5:
+        ax.plot(x[finite], y_raw[finite], ls="", marker="o", ms=2.5,
+                color=color, alpha=alpha, zorder=4)
+
+
 def _ema(y: np.ndarray, span: int = 5) -> np.ndarray:
     """Causal EMA, span=5. Less lag than a trailing SMA(5) since it weights
     recent steps more. NaNs hold the previous smoothed value (don't reset it)."""
@@ -240,6 +253,7 @@ def _series_panel(ax, runs, cols, colors, ylim, label_series=False):
         for r in present:
             ys = _ema(r[col])
             ax.plot(r["steps"], ys, color=color, lw=0.7, alpha=0.35, solid_capstyle="round")
+            _mark_if_sparse(ax, r["steps"], r[col], color, alpha=0.5)
             stacked.append(ys)
         # mean over seeds of the smoothed series (runs share the step grid within an arm)
         L = min(len(y) for y in stacked)
@@ -332,11 +346,16 @@ def _overlay_panel(ax, by_arm, arms, key, *, label, with_onset):
         for r in rs:
             ys = _ema(r[key])
             ax.plot(r["steps"], ys, color=color, lw=0.6, alpha=0.25, solid_capstyle="round")
+            _mark_if_sparse(ax, r["steps"], r[key], color, alpha=0.4)
             stacked.append(ys)
         L = min(len(y) for y in stacked)
         ym = np.nanmean(np.stack([y[:L] for y in stacked]), axis=0)
         xm = rs[0]["steps"][:L]
         ax.plot(xm, ym, color=color, lw=2.0, solid_capstyle="round")
+        # dot the bold mean at its REAL (pre-EMA) measured steps -- ym is EMA-held so
+        # it would otherwise read as per-step-dense (see _mark_if_sparse)
+        raw_mean = np.nanmean(np.stack([r[key][:L] for r in rs]), axis=0)
+        _mark_if_sparse(ax, xm, raw_mean, color)
         if with_onset:
             onsets = [s for r in rs if (s := _onset(r["steps"], r["hack_s"])) is not None]
             if onsets: