Equal Weight per Instance Does Not Pay Alone: A Blob-Loss Auxiliary on Full-Resolution Cityscapes Buys Thin-Object IoU with Pedestrian Recall, and Only Pays as an Expert of a Mixture-of-Experts

We report a pre-registered, controlled evaluation of the **blob loss** of Kofler *et al.* [IPMI 2023] — an auxiliary term that gives every ground-truth instance the same weight regardless of its pixel count — ported to **exclusive multi-class semantic segmentation at the native Cityscapes resolution** (1024×2048, 19 classes, softmax regime). Arm **G** (CE + Dice + 0.5·blob) is compared against its exact paired reference **B** (CE + Dice): same ConvNeXt-V2-Base + UPerNet architecture, same 160-epoch recipe, same augmentation distribution, three shared seeds (42, 123, 456) — only the loss changes. The **pre-registered primary endpoint is null**: dataset-level official mIoU on the shared 500-image holdout, Δ(G−B) = **+0.170 pt**, 95 % CI [−0.233 ; +0.551], two-sided paired image-bootstrap p = **0.4032** (B = 10 000 replicates). Equal weight per instance does **not** improve global mIoU in this regime. What the term actually does is a **measured trade-off**. It *buys* thin-object pixel coverage — traffic light +0.98, pole +0.58, bicycle +0.57 IoU vs B (Holm-significant within the 19-class family), truck +3.78 (CI excluding zero), Boundary F1 (3 px) +0.63 — and it *pays* in pedestrian instance integrity: strict pedestrian recall **−4.34**, small-instance stratum (T1) **−5.65**, crowd-group instances **−5.74**, pedestrian pixel precision **−2.70** (all vs paired B, Holm = 0), and a general **fragmentation** of the masks: connected components rise from 615.1 to 933.9 per image (**×1.52**), up in **16 of 19 classes** against the paired arm B (14 Holm-significant rises; the only material decrease is terrain, −1.8) and in **19 of 19** against the control arm (person masks ×2.2) — a new per-class decomposition that *revises* the working mechanistic hypothesis of the program (the term does not prune small components; it creates holes). On the program's pre-registered versatility criterion (36 endpoints × 13 arms), G is a **dominated specialist**: mean percentile 41.9, worst rank 13/13, maximal damage −6.76 pt, 13 significant losses against 4 significant gains. The same verdict holds on a second dataset and a second probability regime: on BRATS 2023 (region-based sigmoid, MedNeXt, nnU-Net recipe, 5-fold CV, n = 1 196), the blob arm alone scored **−0.00376 Dice** vs baseline, yet as **expert 3 of the winning MoE-V3** it contributed **+0.00566 Dice, first of 29 arms** [DOI 10.5281/zenodo.22903668]. The Cityscapes companion mixture **MoE-V3-CS** (four experts initialised from B, D, Dp, G; top-2 patch-wise gate) tells the same story: it absorbs G's thin-object speciality — small-instance recall T1 +0.80 (significant), traffic light not degraded — without inheriting its damage (maximal damage −0.53 pt; ΔmIoU +0.45 pt, p = 0.0066 vs its paired control). **Contributions.** (1) A pre-registered **null primary** for equal-weight-per-instance auxiliary loss in full-resolution exclusive-softmax segmentation, reported as such. (2) A complete **trade-off diagnostic**: per-class IoU, seven business-metric families on pedestrian instances, and a **new per-class connected-component decomposition** (933.9 vs 615.1 components/image) that contradicts the initially hypothesised compaction mechanism — the paper reports what the tables show. (3) An **exact port** of Kofler's algebra (eq. 1) to the exclusive-softmax regime with precomputed instance packs: +1–2 % epoch time against +870 s/epoch for the naive path, naive↔precomputed parity locked by unit tests, and the horizontal-flip alignment pitfall documented. (4) Evidence, on two datasets and two probability regimes, that the term **pays as an initialised expert of a mixture** while null-or-harmful alone. (5) Public release of code, configs, tables, figures and regeneration scripts. ---