f2b: replace top-1/5/10% bars with full CDF; align all docs to replay-trace numbers

The previous f2b_session_skew.png was a 3-bar chart (top 1/5/10%) computed
from the production trace summary (which is not present locally, only its
precomputed JSON). The new figure is a continuous CDF of cumulative
input-token mass vs session rank percentile, generated directly from the
replay trace traces/w600_r0.0015_st30.jsonl so any percentile is readable.

Headline numbers update accordingly:
  replay trace (n=274 sessions): top 1% = 24.3%, top 5% = 61.9%, top 10% = 75.8%
  production trace (n=1.3M):     top 1% = 46.5%, top 5% = 66.5%, top 10% = 74.6%

Both show extreme skew well above the y=x uniform reference; the replay
trace is less extreme at top-1% because n=274 makes that bucket only
~3 sessions. We standardize §2/§3 narrative on the replay-trace numbers
so motivation matches §5 evaluation; production numbers kept as a side
note for context.

- scripts/plot_session_skew_cdf.py: reproducible figure generator
- MEETING.md / PAPER_OUTLINE.md: update narrative + caption

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

scripts/plot_session_skew_cdf.py

@@ -0,0 +1,90 @@
+#!/usr/bin/env python3
+"""Plot a CDF of cumulative input-token mass by session rank.
+
+Reads a JSONL trace (chat_id, session_id, input_length, ...), aggregates
+per-session input_length, sorts sessions descending by total, and plots
+cumulative fraction of input-token mass vs session-rank percentile.
+
+The figure replaces the previous discrete top-1%/5%/10% bars with a
+continuous curve so any percentile can be read off directly.
+"""
+from __future__ import annotations
+
+import argparse
+import json
+from collections import defaultdict
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def load_session_input_tokens(trace_path: Path) -> dict[str, int]:
+    totals: dict[str, int] = defaultdict(int)
+    with trace_path.open() as f:
+        for line in f:
+            row = json.loads(line)
+            totals[row["session_id"]] += int(row["input_length"])
+    return dict(totals)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--trace",
+        default="traces/w600_r0.0015_st30.jsonl",
+        help="JSONL trace path",
+    )
+    parser.add_argument(
+        "--out",
+        default="figs/f2b_session_skew.png",
+        help="Output figure path",
+    )
+    args = parser.parse_args()
+
+    session_totals = load_session_input_tokens(Path(args.trace))
+    n_sessions = len(session_totals)
+    sorted_vals = np.sort(np.array(list(session_totals.values())))[::-1]
+    cum = np.cumsum(sorted_vals) / sorted_vals.sum()
+    rank_pct = np.arange(1, n_sessions + 1) / n_sessions * 100
+
+    marks = [1, 5, 10, 25, 50]
+    mark_idx = [int(np.ceil(n_sessions * p / 100)) - 1 for p in marks]
+
+    fig, ax = plt.subplots(figsize=(8, 5))
+    ax.plot(rank_pct, cum * 100, color="#2f6fab", lw=2.2,
+            label="cumulative input-token mass")
+    ax.plot([0, 100], [0, 100], color="#999", ls="--", lw=1,
+            label="uniform reference (y = x)")
+
+    for p, i in zip(marks, mark_idx):
+        y = cum[i] * 100
+        ax.scatter([p], [y], color="#c44e52", zorder=5, s=40)
+        ax.annotate(
+            f"top {p}% → {y:.1f}%",
+            xy=(p, y),
+            xytext=(p + 2, y - 5),
+            fontsize=9,
+            color="#333",
+        )
+
+    ax.set_xlim(0, 100)
+    ax.set_ylim(0, 102)
+    ax.set_xlabel("Session rank percentile (top → bottom by input-token mass)")
+    ax.set_ylabel("Cumulative % of input-token mass")
+    ax.set_title(
+        f"Session input-token mass CDF "
+        f"(n={n_sessions} sessions, "
+        f"total={sorted_vals.sum() / 1e6:.1f} M tokens)"
+    )
+    ax.grid(True, alpha=0.3)
+    ax.legend(loc="lower right", framealpha=0.9)
+
+    out_path = Path(args.out)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    fig.savefig(out_path, dpi=150, bbox_inches="tight")
+    print(f"wrote {out_path}")
+
+
+if __name__ == "__main__":
+    main()