diff --git a/figs/working_set/glm5_fp8_tp8_b300.png b/figs/working_set/glm5_fp8_tp8_b300.png
index 92edd2f..e3ba68c 100644
Binary files a/figs/working_set/glm5_fp8_tp8_b300.png and b/figs/working_set/glm5_fp8_tp8_b300.png differ
diff --git a/scripts/working_set_analysis.py b/scripts/working_set_analysis.py
index d1d46ad..70a684b 100644
--- a/scripts/working_set_analysis.py
+++ b/scripts/working_set_analysis.py
@@ -105,8 +105,10 @@ def _series(starts, ends, grid):
     return np.searchsorted(s, grid, side="right") - np.searchsorted(e, grid, side="right")
 
 
-def compute_working_set(ids, ts, taus):
-    """Return dict with appearance stats + per-tau Denning peaks + oracle/all."""
+def compute_working_set(ids, ts, taus, series_taus=()):
+    """Return dict with appearance stats + per-tau Denning peaks + oracle/all.
+
+    For each T in series_taus, also return the full W(t) time series on `grid`."""
     A = len(ids)
     order = np.lexsort((ts, ids))
     ids_s, ts_s = ids[order], ts[order]
@@ -127,6 +129,7 @@ def compute_working_set(ids, ts, taus):
     oracle_peak = _sweep_peak(first[seen], last[seen])
 
     rows = []
+    series = {}
     for T in taus:
         enter = ts_s[prev_gap > T]
         exit_ = ts_s[next_gap > T] + T
@@ -138,11 +141,15 @@ def compute_working_set(ids, ts, taus):
             "p50_blocks": float(np.percentile(ser, 50)),
             "apc": float((prev_gap <= T).sum() / A),
         })
+        if T in series_taus:
+            series[T] = ser
     return {
         "A": A, "n_unique": n_unique, "n_reuse": A - n_unique,
         "apc_ceiling": (A - n_unique) / A,
         "oracle_peak_blocks": oracle_peak,
         "span": float(ts.max() - ts.min()),
+        "grid_s": grid - grid.min(),
+        "series": series,
         "taus": rows,
     }
 
@@ -200,28 +207,26 @@ def plot(ws, hw, block_bytes, label, out_path):
     ax1.set_title("Benefit vs cost: APC per cluster size", fontweight="bold")
     ax1.grid(alpha=.3); ax1.grid(alpha=.15, which="minor"); ax1.legend(loc="center right")
 
-    # ===== panel 2: cost -- nodes needed to retain T seconds of reuse =====
-    ax2.plot(tau, nodes, "s-", color="#1f77b4", lw=2, ms=7, zorder=4)
-    ax2.axhline(1, ls="--", color="#2ca02c", lw=1.8, label="1 node (your budget)")
-    ax2.axhline(oracle_nodes, ls=":", color="#d62728", lw=1.8,
-                label=f"full ceiling = {oracle_nodes:.0f} nodes")
-    # where the curve crosses the 1-node budget
-    tau_at_1 = float(np.interp(1.0, nodes, tau))
-    ax2.scatter([tau_at_1], [1], s=90, facecolors="none", edgecolors="#ff7f0e", lw=2, zorder=6)
-    ax2.annotate(f"1 node only retains\n~{tau_at_1:.0f}s of reuse", (tau_at_1, 1),
-                 textcoords="offset points", xytext=(8, 14), fontsize=9, color="#ff7f0e")
-    for r, x, y in zip(rows, tau, nodes):
-        if y >= 1.5:
-            ax2.annotate(f"{r['tau']:g}s", (x, y), textcoords="offset points",
-                         xytext=(4, 5), fontsize=8.5)
-    ax2.set_xscale("log")
-    ax2.set_xticks([1, 2, 5, 10, 30, 60, 300])
-    ax2.set_xticklabels(["1s", "2s", "5s", "10s", "30s", "60s", "300s"])
+    # ===== panel 2: working set W(t) over time (steady -> peak ~ median) =====
+    apc_of = {r["tau"]: r["apc"] * 100 for r in ws["taus"]}
+    t_min = ws["grid_s"] / 60.0                      # minutes
+    colors = {2: "#2ca02c", 30: "#ff7f0e", 300: "#1f77b4"}
+    for T, ser in sorted(ws["series"].items()):
+        y = ser * bgb / pool
+        c = colors.get(T, "#777")
+        ax2.plot(t_min, y, lw=1.8, color=c, label=f"keep {T:g}s reuse  (APC {apc_of[T]:.0f}%)")
+        ax2.axhline(float(np.median(y)), ls=":", color=c, alpha=.6, lw=1)
+    ax2.axhline(1, ls="--", color="#2ca02c", lw=1.6, alpha=.8)
+    ax2.text(t_min.max(), 1, " 1-node budget", color="#2ca02c", fontsize=8.5, va="center")
+    ax2.axhline(oracle_nodes, ls="--", color="#d62728", lw=1.6, alpha=.8)
+    ax2.text(t_min.max(), oracle_nodes, " ceiling: 14 nodes", color="#d62728",
+             fontsize=8.5, va="center")
     ax2.set_ylim(0, XMAX); ax2.set_yticks(range(0, XMAX + 1, 2))
-    ax2.set_xlabel("retention window T  (how long-idle a session's KV we keep)")
-    ax2.set_ylabel("# nodes of GPU HBM needed")
-    ax2.set_title("Cost: nodes needed to retain T-seconds of reuse", fontweight="bold")
-    ax2.grid(alpha=.3, which="both"); ax2.legend(loc="upper left", fontsize=9)
+    ax2.set_xlim(0, t_min.max())
+    ax2.set_xlabel("wall-clock time into the trace  (min)")
+    ax2.set_ylabel("# nodes of GPU HBM resident  (W(t))")
+    ax2.set_title("Working set over time  (flat -> peak ~ median)", fontweight="bold")
+    ax2.grid(alpha=.3); ax2.legend(loc="center right", fontsize=9)
 
     fig.suptitle(label, fontsize=13, fontweight="bold")
     fig.tight_layout(rect=[0, 0, 1, 0.97])
@@ -258,8 +263,9 @@ def main():
     hw = {"gpus_per_replica": gpus_per_replica, "kv_pool_gb": kv_pool_gb, "gpu": a.gpu}
 
     taus = [1, 2, 5, 10, 30, 60, 300, 600, 1800]
+    series_taus = [2, 30, 300]                      # W(t) lines drawn in panel 2
     n, ids, ts = load_trace(a.trace, a.min_ts, a.max_ts)
-    ws = compute_working_set(ids, ts, taus)
+    ws = compute_working_set(ids, ts, taus, series_taus)
 
     label = a.label or f"{model['name']} {a.gpu} TP{a.tp}" + (f" EP{a.ep}" if a.ep else "")
     print("=" * 84)