Valeri's decisions (1) competitors from the library, (2) variant a. (1) Chandrupatla is scipy.optimize.elementwise.find_root, Brent is brentq, no more per-problem best_of. Our Chandrupatla stopped on a bracket twice as wide as every other method (the factor the protocol corrects for ITP); fixed (+0.5*xtol) in baselines.py and in the two copies found by grep (stage16_blend, stage19_modern) and the traced mirror, and pinned: ours == library on every problem. Regenerating sweep_full.json changed exactly three rows - chandrupatla APS 2785->2787, CHA 2423->2441, brent CHA 4099->4115 - and nothing else (diff_sweeps.py: 0 unexpected).

(2) The proposed method is again the scheduled projection (2806/3220/6026), its budget multiplier DECLARED as the one free constant: the canonical total is the same for every multiplier 1.25-4, the wide-bracket one is not. The earned projection is the constant-free variant (3568/3020/6588). What the old tables hid: problem by problem the proposed rule beats Chandrupatla on the canonical set 87/24/42, exact sign test p = 9e-5, while trailing him by 0.7% in total; on the wide-bracket set he is genuinely stronger (26 to 9, p = 0.006). Gap decomposition recomputed with the library: 244 (was 262), 160 of it on the flat families (two thirds), fun1 58 (a quarter, not a third as the abstract said).

Also corrected on the way: the 'subsumed by Chandrupatla' pin claimed he is better on the worst single problem - true only with the looser tolerance (84 against the credit rule's 83 once fixed); §8.3 still called zero slack degenerate, which Table 1a refutes; §5.2's Brent figure is our pure-Python implementation and now says so. verify_article.py 286/286, 802 tests pass twice.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

diag_cha_phases.py

@@ -16,6 +16,7 @@ from __future__ import annotations
 import math
 from collections import defaultdict
 
+from simroot.baselines import CHANDRUPATLA_BASELINE
 from simroot.baselines import chandrupatla, chandrupatla_scipy
 from simroot.pure import pure_root
 from stage4_why import phase_boundary, traced
@@ -26,7 +27,7 @@ from sweep_full import ACC, MAXFEV, XTOL, load_problems
 # total as 2993 instead of 1487 and manufactured a 1299-evaluation "endgame win" for us
 # (caught 2026-09-21). Each implementation is traced separately and the cheaper split kept,
 # which is what best_of means for cost.
-CHA_IMPLS = (chandrupatla, chandrupatla_scipy)
+CHA_IMPLS = (CHANDRUPATLA_BASELINE,)   # the library alone (2026-09-21)
 
 
 def ours(f, a, b, xtol, maxfev):