Alternate base result (tuned, ≤4 layers)

Tuned shallow (depth ∈ [1,3], i.e. ≤4 effective layers counting the linear read-out head) comparison of the monotone constructions on the five paper datasets. Each flavor is tuned per-dataset with its own Optuna HP search (activation included), plain only, at the paper’s per-dataset trial counts (heart/auto = 200, compas/blog/loan = 50) under the repo’s stability-aware CV objective. mixed tunes convex_fraction; mixed-fixed holds it at 0.5. Bold = best per dataset (🥇).

dataset	rows	flavor	IQM	mean ± std	act	layers	width	lr	wdec	drop	lrdec	batch	cvxf	done
heart (acc ↑)	242	split	0.909	0.909 ± 0.004	relu	3	32	0.0086	0.131	0.40	0.866	8	·	✅
		mixed 🥇	0.910	0.909 ± 0.006	relu	2	16	0.0152	0.096	0.43	0.991	8	0.78	✅
		mixed-fix	0.906	0.906 ± 0.003	relu	3	64	0.0118	0.090	0.39	0.895	32	0.50	✅
		alternate	0.906	0.906 ± 0.001	softplus	3	8	0.0215	0.059	0.15	0.899	32	·	✅
auto (MSE ↓)	314	split	10.90	10.88 ± 0.49	relu	2	64	0.0066	0.017	0.03	0.942	8	·	✅
		mixed	10.46	10.44 ± 0.28	elu	3	21	0.0838	0.043	0.01	0.919	8	0.54	✅
		mixed-fix 🥇	10.13	10.18 ± 0.28	elu	3	8	0.0743	0.034	0.02	0.926	8	0.50	✅
		alternate	10.14	10.13 ± 0.19	softplus	2	32	0.0389	0.020	0.05	0.926	16	·	✅
compas (acc ↑)	4,937	split	0.730	0.729 ± 0.003	softplus	3	16	0.0172	0.000	0.07	0.998	32	·	✅
		mixed	0.704	0.704 ± 0.002	softplus	2	16	0.0007	0.190	0.00	0.896	64	0.77	✅
		mixed-fix	0.721	0.721 ± 0.002	elu	4	32	0.0004	0.004	0.08	0.986	8	0.50	✅
		alternate 🥇	0.730	0.730 ± 0.002	selu	3	16	0.0106	0.000	0.46	0.903	8	·	✅
blog (RMSE ↓)	47,302	split 🥇	0.177	0.177 ± 0.002	softplus	2	21	0.0226	0.001	0.08	1.000	2048	·	✅
		mixed	0.178	0.178 ± 0.001	elu	2	21	0.0386	0.000	0.39	0.986	1024	0.38	✅
		mixed-fix	0.182	0.182 ± 0.001	softplus	2	32	0.0146	0.003	0.06	0.859	1024	0.50	✅
		alternate	0.186	0.186 ± 0.001	selu	2	21	0.0064	0.013	0.27	0.879	2048	·	✅
loan (acc ↑)	418,697	split	0.704	0.704 ± 0.001	selu	3	16	0.0002	0.001	0.04	0.914	512	·	✅
		mixed	0.704	0.704 ± 0.000	elu	2	16	0.0036	0.000	0.13	0.999	512	0.72	✅
		mixed-fix 🥇	0.708	0.708 ± 0.001	elu	3	16	0.0436	0.000	0.21	0.880	512	0.50	✅
		alternate	0.703	0.703 ± 0.000	elu	2	8	0.0028	0.000	0.14	0.896	512	·	✅

convex_fraction ablation — searched (mixed) vs fixed 0.5 (mixed-fixed)

dataset	mixed (searched cvxf)	mixed-fixed (0.5)	better
auto	10.46 (cvxf 0.54)	10.13	fixed
blog	0.1778 (cvxf 0.38)	0.1817	searched
heart	0.9096 (cvxf 0.78)	0.9056	searched
compas	0.7037 (cvxf 0.77)	0.7211	fixed
loan	0.7037 (cvxf 0.72)	0.7084	fixed

Fixing convex_fraction = 0.5 is the better default — it wins 3/5 (auto, compas, loan), substantially on compas (+0.017 acc) and auto (−0.33 MSE); searching is only marginally better on heart/blog (~0.004). Searching the knob mostly hurt on the mid/large datasets and complicates the init (any fraction ≠ 0.5 triggers a gain/bias fixed-point in absolute_init_params). This motivates fixing it (spec follow-up 6).

Verdict — alternate vs best-of-others (all non-alternate flavors)

Bootstrap CI on the per-fold alternate − best-other gap, where best-other is the strongest of split / mixed / mixed-fixed.

dataset	Δ (alt − best-other)	95% CI	verdict
auto	-0.015	[-0.145, +0.185]	matches (CI straddles 0) (vs mixed-fixed-plain)
blog	-0.009	[-0.010, -0.008]	alternate loses (vs split-plain)
heart	-0.004	[-0.007, -0.000]	alternate loses (vs mixed-plain)
compas	+0.000	[-0.001, +0.004]	matches (CI straddles 0) (vs split-plain)
loan	-0.005	[-0.006, -0.005]	alternate loses (vs mixed-fixed-plain)

Bottom line — does alternate beat the best non-alternate flavor at ≤4 tuned layers? No, not decisively. It reaches a clean co-lead on compas (0.730, tied with split, ahead of both mixed variants) and sits within noise on auto (10.14 vs mixed-fixed 10.13) and heart (0.906 vs 0.910); it loses outright on blog and loan. So in the regimes where mixed was dominating, tuned alternate closes the gap to parity on the mid-size classification task but does not overtake — and the single biggest gain in this sweep came not from alternate but from fixing convex_fraction in mixed (see the ablation above).

Caveat (auto). On tiny auto (314 rows) the 200-trial budget meta-overfits the CV objective — searched mixed/split degrade vs a lighter budget — so auto should be read cautiously; a search-sensitivity study (separate spec) quantifies this.