vvsandClaude Opus 5
862feaff86
Point 1. The base step's order is Muller's 1.839, not 2: the derivative of the parabola through
the last three points errs by f'''/6 (x_n - x_{n-1})(x_n - x_{n-2}) = O(e_{n-1} e_{n-2}).
order_high_precision.py (3000 digits, to 1e-2900): 1.840 on cos x - x, e^x - 2, x^3 - x - 2,
sin x - 1/2; 2.000 only on x^2 - 2; two-point 1.618. The double-precision medians (2.000, 1.94)
are removed from the text.
Point 3. Every protocol number is re-scored under the criterion of section 9 - a sign change
within 1e-6 or f(x) == 0. rescore_uniform.py re-runs all 2779 distinct configurations of the 25
protocol sweeps on APS and CHA (the runs did not keep x) -> sweep_uniform.json, same row format;
it reproduces the APS/CHA lines of Table 10 exactly (built-in check). verify_article.py,
audit_similarity_places.py and the gap diagnostics read the re-scored data; the multiplier and
endowment measurements score by the same criterion. Table 7 (two criteria side by side) is gone;
7.3 now explains why the criterion is a sign change. Consequences stated in the text: the base
rule 2634 / 2357 against Chandrupatla 2595 / 1505 (1.5% behind on APS, no longer "level"); A'
2259 / 1074 (12.9% and 28.6% below Chandrupatla); the defect reading 2351 is second to the
shape reading 2337, not the record; the remembered analogy is level with no regime (2633 vs
2634) under this criterion; Table 9a's range is 19% to 425%.
Point 8. report_sweep.head_to_head counts every problem, failures charged (it counted only the
problems both methods solved, which is why Table 8 rows summed to 153 and 40). Family-level
comparisons added (A' vs Chandrupatla 8/1/6 of 15 and 7/0/2 of 9 families, not significant), and
an independence paragraph in section 9.
Point 11. sweep_itp.py: ITP (recommended constants and per-problem oracle) on all six corpora ->
Table 10; behind A' on every corpus, 0 bound violations.
Points 7, 9, 10 and the internals: the 2x bound is ours, not the rivals' promise; the law
predicts the group order only; "outside the proven bracket" explained (narrowest bracket of all
observed signs); 7.4's fun1 paragraph corrected (five costs under safe; the unguarded numbers
belong to fun1.4, fun1.5 and to fun3.1, fun5.1 separately); Table 1's last cell; the corpus-4
composition and odd multiplicities stated, also in the abstract.
build_tex.py: pdflatex output decoded with errors="replace" (a UTF-8 character cut by TeX's log
wrap crashed the build). Table 10 column widths rebalanced.
Checks: verify_article on the server 500 OK / 0 BAD (467 existing with the new values, 33 new);
regression x2 on the server 1232 passed, 0 failed; PDF 28 pages, 0 errors, 0 undefined, 0 overfull.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
73 lines
2.4 KiB
Python
73 lines
2.4 KiB
Python
"""Revision point 11 (2026-09-22): ITP in Table 10 - at the authors' recommended constants and as
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the per-problem oracle of section 4 - on all six corpora, under the uniform criterion of
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section 9, with the harness of sweep_fourth.py (same problems, same seeding, same charging).
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python -u sweep_itp.py run -> sweep_itp.json, then the table
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python -u sweep_itp.py --report
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"""
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from __future__ import annotations
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import json
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import sys
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from multiprocessing import Pool
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import sweep_fourth
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from sweep_oos import sign_p
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sweep_fourth.CONFIGS["itp"] = ("base", "itp")
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sweep_fourth.CONFIGS["itp_oracle"] = ("base", "itp_oracle")
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SETS = ("APS", "CHA", "NEW", "RJL", "APP", "D")
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def job(j):
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name, which = j
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return name, which, sweep_fourth.evaluate((name, which))[1]
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def cost(v):
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return v[0] if v[1] else 200
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def report(R):
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seen = json.load(open("sweep_fourth_seen.json", encoding="utf8"))
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four = json.load(open("sweep_fourth_fourth.json", encoding="utf8"))
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ap = dict(seen["Aprime"])
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ap.update(four["Aprime"])
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print("corpus A' ITP (viol) ITP oracle (viol) A' vs ITP A' vs oracle")
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tot = {"A": 0, "itp": 0, "itp_oracle": 0}
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for s in SETS:
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a = ap[s]
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row = [f"{s:<5} {sum(cost(v) for v in a.values()):6d}"]
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tot["A"] += sum(cost(v) for v in a.values())
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h = []
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for k in ("itp", "itp_oracle"):
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r = R[k][s]
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t = sum(cost(v) for v in r.values())
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tot[k] += t
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row.append(f"{t:6d} ({sum(v[2] for v in r.values())})")
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w = sum(cost(a[p]) < cost(r[p]) for p in a)
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l = sum(cost(a[p]) > cost(r[p]) for p in a)
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h.append(f"{w}/{len(a) - w - l}/{l} p={sign_p(w, l):.1g}")
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print(" ".join(row) + " " + " ".join(h))
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print(f"all {tot['A']:6d} {tot['itp']:6d} {tot['itp_oracle']:6d}")
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def main():
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if "--report" not in sys.argv:
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jobs = [(n, w) for n in ("itp", "itp_oracle") for w in ("seen", "fourth")]
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with Pool(len(jobs)) as pool:
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out = pool.map(job, jobs)
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R = {}
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for name, _which, res in out:
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R.setdefault(name, {}).update(res)
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with open("sweep_itp.json", "w", encoding="utf8") as fh:
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json.dump(R, fh)
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with open("sweep_itp.json", encoding="utf8") as fh:
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R = json.load(fh)
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report(R)
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print("itp done")
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if __name__ == "__main__":
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main()
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