Datasets:
File size: 5,198 Bytes
c9eb63c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | #!/usr/bin/env python3
"""verify.py — read every condensed table and print/assert each paper claim.
Run from the reviewer-pack root:
python verify.py
Exits with code 0 if all assertions pass (within a 2e-3 tolerance on
coefficient values), prints any mismatches otherwise.
"""
from __future__ import annotations
import sys
from pathlib import Path
import pandas as pd
ROOT = Path(__file__).resolve().parent
TABLES = ROOT / "tables"
TOL = 2e-3 # tolerance on coef/AUC equality checks
def banner(s: str) -> None:
print(f"\n{'═' * 78}\n {s}\n{'═' * 78}")
def claim(desc: str, actual: float, expected: float, tol: float = TOL) -> bool:
ok = abs(actual - expected) <= tol
tick = "PASS" if ok else "FAIL"
print(f" [{tick}] {desc}\n actual={actual:.4f} expected={expected:.4f} diff={abs(actual-expected):.4f}")
return ok
def main() -> int:
fails = 0
# ─── Table 2 (DML Spec B POOLED) ────────────────────────────────
banner("Table 2 — DML Spec B headline (mutually-controlled, 6 treatments)")
t2 = pd.read_csv(TABLES / "table2_dml_headline.csv")
print(t2.to_string(index=False))
# Sample assertions — the paper's headline numbers
def lookup(outcome: str, t_pretty: str) -> float:
sub = t2[(t2.outcome == outcome) & (t2.treatment == t_pretty)]
return float(sub.iloc[0]["coef"])
banner("DML claim checks")
fails += not claim("T5 topical comp. selected", lookup("selected","T5 topical competence"), +0.037, tol=0.005)
fails += not claim("T2a Q-headings selected", lookup("selected","T2a Q-headings"), +0.016, tol=0.005)
fails += not claim("T3 schema selected", lookup("selected","T3 schema (JSON-LD)"), -0.014, tol=0.005)
fails += not claim("T6 freshness selected", lookup("selected","T6 freshness"), -0.005, tol=0.005)
fails += not claim("T5 topical comp. rank_delta", lookup("rank_delta","T5 topical competence"), -0.530, tol=0.02)
fails += not claim("T2a Q-headings rank_delta", lookup("rank_delta","T2a Q-headings"), +0.136, tol=0.02)
fails += not claim("T3 schema post_rank", lookup("post_rank","T3 schema (JSON-LD)"), +0.095, tol=0.01)
# ─── Admission probe headline ───────────────────────────────────
banner("Admission probe — pre-commitment headline (mean pooling)")
adm = pd.read_csv(TABLES / "admission_probe_headline.csv")
pooled = adm[adm.pooling == "mean"].mean(numeric_only=True)
print(adm[adm.pooling == "mean"].round(4).to_string(index=False))
banner("Admission probe claim checks (variant-averaged)")
fails += not claim("Layer 0 ROC AUC", pooled["layer_0"], 0.671, tol=0.02)
fails += not claim("Peak ROC AUC", pooled["auc_peak"], 0.860, tol=0.02)
fails += not claim("L0 → peak gain", pooled["delta_L0_to_peak"], 0.190, tol=0.03)
# ─── Saliency headline ──────────────────────────────────────────
banner("Saliency — Llama vs Qwen on 4 treatments")
sal = pd.read_csv(TABLES / "saliency_summary.csv")
print(sal.to_string(index=False))
banner("Saliency claim checks")
def sal_ratio(model: str, t: str) -> float:
return float(sal[(sal.model == model) & (sal.treatment == t)].iloc[0]["saliency_ratio"])
fails += not claim("Qwen attends to T1b stats (>>1)", sal_ratio("Qwen-2.5-72B", "T1b_stats_density"), 1.93, tol=0.05)
fails += not claim("Llama ~baseline on T1b (<1)", sal_ratio("Llama-3.3-70B","T1b_stats_density"), 0.89, tol=0.05)
fails += not claim("Llama ignores T3 schema (<<1)", sal_ratio("Llama-3.3-70B","T3_structured_data_new"),0.19, tol=0.05)
fails += not claim("Qwen ignores T3 schema (<<1)", sal_ratio("Qwen-2.5-72B", "T3_structured_data_new"),0.40, tol=0.05)
# ─── Ablation headline ──────────────────────────────────────────
banner("Ablation — mean Δrank per (treatment, prompt) on full frame")
abl = pd.read_csv(TABLES / "ablation_summary.csv")
full_abl = abl[abl.frame == "full"]
print(full_abl.to_string(index=False))
banner("Ablation claim checks")
def abl_mean(treatment, prompt) -> float:
sub = full_abl[(full_abl.treatment == treatment) & (full_abl.prompt == prompt)]
return float(sub.iloc[0]["mean_delta_r"])
fails += not claim("T5 sign flip — biased (promotes URL)", abl_mean("T5_topical_comp","biased"), -0.167, tol=0.03)
fails += not claim("T5 sign flip — neutral (demotes URL)", abl_mean("T5_topical_comp","neutral"), +0.038, tol=0.03)
print()
print(f"{'═' * 78}")
if fails:
print(f" {fails} claim(s) FAILED — please inspect the printed values.")
return 1
print(" All paper claims VERIFIED against the tables.")
return 0
if __name__ == "__main__":
sys.exit(main())
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