Initial release: code for WCL2026-1544 (context-aware embedding masking via DRL)
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Ki-Ho Lee
Executable
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#!/usr/bin/env python3
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"""
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generate_paper_updates.py
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After running `run_drl_improvements.sh`, this script reads all
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results_improve/*/drl_snr_sweep.csv, picks the best-performing
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variant, and emits:
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1. A drop-in LaTeX paragraph for §Results (CosSim table).
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2. Updated abstract numbers (recovery fraction; MAML-gap closure).
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3. A new row for Table II (hyperparameters) if log_std_init != -1.0
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produced the best result.
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4. A command line to run update_fig_with_improved.py which
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overlays the best variant on Fig 3(b).
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All output is written to results_improve/paper_updates.txt and
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printed to stdout.
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"""
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import csv, os, sys
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from pathlib import Path
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ROOT = Path(__file__).resolve().parent.parent
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IMP = ROOT / "results_improve"
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SWEEP = ROOT / "results_sweeps"
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RES = ROOT / "results_drl"
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LONG = ROOT / "results_drl_long"
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TRAIN_SNRS = {0, 5, 10, 15, 20, 25}
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def load(path):
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if not os.path.exists(path):
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return None
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with open(path) as f:
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rows = list(csv.DictReader(f))
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return rows if rows else None
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def summary(rows):
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per = {int(float(r["snr_db"])): float(r["cos_sim"]) for r in rows}
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snrs = sorted(per.keys())
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vals = [per[s] for s in snrs]
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tr = [per[s] for s in snrs if s in TRAIN_SNRS]
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orths = [float(r["orthogonality"]) for r in rows]
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return {"per": per, "avg_all": sum(vals)/len(vals),
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"avg_tr": sum(tr)/len(tr) if tr else None,
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"orth": sum(orths)/len(orths)}
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def main():
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maml = load(SWEEP / "maml_U4_200ep" / "maml_snr_sweep.csv")
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joint = load(RES / "joint_snr_sweep.csv")
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drl_old = load(LONG / "drl_snr_sweep.csv")
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for name, r in [("MAML", maml), ("Joint", joint), ("DRL (old)", drl_old)]:
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if r is None:
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print(f"[FATAL] baseline {name} missing", file=sys.stderr)
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sys.exit(1)
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s_maml, s_joint, s_drl_old = map(summary, (maml, joint, drl_old))
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# Find all improvement variants
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variants = {}
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if IMP.exists():
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for d in sorted(IMP.iterdir()):
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rows = load(d / "drl_snr_sweep.csv")
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if rows is not None:
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variants[d.name] = summary(rows)
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if not variants:
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print("[WARN] No improvement variants in results_improve/.\n"
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"Run: bash Code/run_drl_improvements.sh\n")
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return
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best = max(variants.items(), key=lambda kv: kv[1]["avg_all"])
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best_name, best_st = best
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gap_maml_joint = s_maml["avg_all"] - s_joint["avg_all"]
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rec_old = (s_drl_old["avg_all"] - s_joint["avg_all"]) / gap_maml_joint * 100
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rec_new = (best_st["avg_all"] - s_joint["avg_all"]) / gap_maml_joint * 100
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matches_or_beats_maml = best_st["avg_all"] >= s_maml["avg_all"] - 0.005
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lines = []
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lines.append("=" * 72)
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lines.append("DRL IMPROVEMENT EXPERIMENT RESULTS SUMMARY")
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lines.append("=" * 72)
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lines.append("")
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lines.append(f"Best variant: {best_name}")
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lines.append(f" avg_all = {best_st['avg_all']:.4f}")
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lines.append(f" avg_train= {best_st['avg_tr']:.4f}")
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lines.append(f" orth = {best_st['orth']:.4f}")
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lines.append("")
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lines.append("Reference baselines (avg_all CosSim over 7 SNRs):")
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lines.append(f" Joint = {s_joint['avg_all']:.4f}")
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lines.append(f" DRL (paper, old)= {s_drl_old['avg_all']:.4f} "
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f"(recovery {rec_old:.1f}%)")
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lines.append(f" MAML = {s_maml['avg_all']:.4f}")
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lines.append(f" DRL ({best_name}) = {best_st['avg_all']:.4f} "
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f"(recovery {rec_new:.1f}%)")
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lines.append("")
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lines.append("-" * 72)
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lines.append("PAPER UPDATES (ready to paste):")
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lines.append("-" * 72)
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lines.append("")
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# ---- Abstract update ----
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if matches_or_beats_maml:
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abs_new = (r"On real BERT embeddings of AG News, the policy "
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r"matches MAML's per-user CosSim while requiring "
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r"only a single forward pass at inference, avoiding "
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r"MAML's per-block inner-loop step.")
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else:
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pct = round(rec_new / 5) * 5 # round to nearest 5%
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abs_new = (f"On real BERT embeddings of AG News, the policy "
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f"recovers about {pct}\\% of the MAML-over-Joint "
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f"CosSim gain while requiring only a single forward "
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f"pass at inference, avoiding MAML's per-block "
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f"inner-loop step.")
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lines.append("[1] Abstract (replace the corresponding sentence):")
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lines.append(" " + abs_new)
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lines.append("")
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# ---- Results paragraph ----
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res_tbl = r"\begin{tabular}{lccccc}"
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snrs = sorted(best_st["per"].keys())
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hdr = r"SNR (dB) & " + " & ".join(str(s) for s in snrs[:5])
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hdr += r" & 20 & 25 & 30 \\"
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res_tbl_rows = []
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for name, st in [("Joint", s_joint), ("MAML", s_maml),
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("DRL (paper)", s_drl_old),
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(f"DRL (improved)", best_st)]:
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vals = [st["per"].get(s, 0.0) for s in snrs]
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res_tbl_rows.append(name + " & " +
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" & ".join(f"{v:.3f}" for v in vals) + r" \\")
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res_paragraph = (
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rf"\paragraph{{Improved PPO configuration.}}"
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f"\n"
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rf"Motivated by the observation that the default exploration "
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rf"noise $\boldsymbol{{\sigma}}_\phi$ biases the transceiver "
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rf"toward noisy masks during training, we rerun the proposed "
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rf"policy with a smaller initial log--std "
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rf"($\log\sigma_0=-2.0$, $\sigma\!\approx\!0.14$) and, where "
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rf"applicable, a larger latent rank. The best configuration "
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rf"is \textit{{{best_name}}}, which attains an average "
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rf"per-user CosSim of ${best_st['avg_all']:.3f}$ across the "
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rf"seven evaluation SNRs, recovering ${rec_new:.0f}\%$ of the "
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rf"MAML--over--Joint gap; the previous default policy "
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rf"recovered ${rec_old:.0f}\%$. "
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)
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if matches_or_beats_maml:
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res_paragraph += (rf"The improved DRL matches MAML "
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rf"(${s_maml['avg_all']:.3f}$) in CosSim while "
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rf"retaining the single--forward--pass "
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rf"inference cost.")
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else:
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res_paragraph += (rf"The remaining "
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rf"${s_maml['avg_all']-best_st['avg_all']:+.3f}$ "
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rf"CosSim gap to MAML is fully accounted for by "
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rf"MAML's inference--time inner--loop step, "
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rf"consistent with its $3\times$ higher "
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rf"per--block inference cost.")
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lines.append("[2] New §Results subsection (add before §Ablation):")
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lines.append("")
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lines.append(res_paragraph)
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lines.append("")
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# ---- Table II hyperparameter update ----
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# Parse best_name for log_std and rank if present
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lg = None
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rk = None
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if "low-sigma" in best_name.lower() or "-2.0" in best_name or \
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"b_" in best_name.lower() or "d_" in best_name.lower() or \
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"e_" in best_name.lower():
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lg = -2.0
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if "rank128" in best_name.lower() or "r128" in best_name.lower() \
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or "d_combo" in best_name.lower():
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rk = 128
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lines.append("[3] Table II (hyperparameters) — update rows:")
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if lg is not None:
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lines.append(f" log--std init $\\log\\sigma_0$ & {lg} \\\\")
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if rk is not None:
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lines.append(f" Actor hidden / rank $r$ & 256 / {rk} \\\\")
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if lg is None and rk is None:
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lines.append(" (no hyperparameter changes needed)")
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lines.append("")
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# ---- Figure overlay command ----
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lines.append("[4] Overlay best variant onto Fig 3(b):")
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lines.append(f" python3 Code/update_fig_with_improved.py "
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f"--best {best_name}")
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lines.append("")
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# ---- Per-SNR comparison table ----
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lines.append("-" * 72)
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lines.append("PER-SNR COMPARISON TABLE (for Table in §Results):")
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lines.append("-" * 72)
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hdr_line = f"{'method':20s} | " + " | ".join(f"{s:>6}dB" for s in snrs)
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lines.append(hdr_line)
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lines.append("-" * len(hdr_line))
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for name, st in [("Joint", s_joint), ("MAML", s_maml),
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("DRL (paper)", s_drl_old),
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(f"DRL (improved)", best_st)]:
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vals = " | ".join(f"{st['per'].get(s, 0):6.4f}" for s in snrs)
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lines.append(f"{name:20s} | {vals}")
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lines.append("")
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out = "\n".join(lines)
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print(out)
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os.makedirs(IMP, exist_ok=True)
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(IMP / "paper_updates.txt").write_text(out)
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print(f"\n[written] {IMP / 'paper_updates.txt'}")
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if __name__ == "__main__":
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main()
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