Cheap first, escalate only when unsure
Every example runs the cheap silo (K²=16 attention pairs). If the silo's top-class probability clears a threshold τ, keep its answer. If not, escalate to the order-aware plain expert (+N²=64 pairs). The routing signal is the silo's own confidence — no regime cue, no learned router. The catch: confidence only tells you when to escalate if the silo is uncertain on the examples it gets wrong.
Same cascade, two tasks — calibration decides
Both tasks are cue-free and differ only in whether the hard regime is legible in the content. Read each card: the three accuracy bars (silo alone / plain alone / the cascade), the acc-vs-compute curve (the cascade swept over τ, between the silo and plain points), and the two diagnostics — ECE (miscalibration; lower is better) and conf-AUC (does confidence predict correctness; 0.5 = blind).
Why calibration is the whole game
ECE — expected calibration error
Average gap between how confident the silo is and how often it's right, over confidence bins. A calibrated model has ECE ≈ 0. The entangled silo (0.12) is confidently wrong on the order examples — high confidence, low accuracy. The telegraphed silo (0.05) knows what it doesn't know.
conf-AUC — does confidence predict correctness
AUROC of the silo's confidence as a detector of "this answer is right." 0.5 = blind (confidence tells you nothing). Entangled 0.59 is near-blind; telegraphed 0.90 ranks right-above-wrong. A cascade routes on this number — so it inherits exactly the silo's (mis)calibration.
What's real — and what it isn't
What's real
Both experts are genuinely trained from scratch (numpy Adam); selftest.py gradient-checks the hand-written backprop (< 1e-5) and asserts the core finding — the telegraphed silo really is better-calibrated (AUC 0.90 vs 0.59) and the cue-free cascade really closes ~100% of the silo→plain gap at 29% less compute than plain. Reproducible at seed 0 with python train.py.
What it isn't
A constructed contrast, not a claim about real data — the two tasks are built to isolate calibration; real workloads sit somewhere between. It is not "cascades beat routers": the cue-free cascade only matches plain when the regime is legible in the content. When it isn't (entangled), no cue-free method helps — the honest sequel to v5's catch. Tiny models, one seed, synthetic.
model.py (encoder + two experts + cascade + ECE/AUC, gradient-checked) · tasks.py (entangled + telegraphed, cue-free) · train.py → results.json · selftest.py. The hexalogy: build (v1) · measure (v2) · train one silo (v3) · two in parallel (v4) · route with a cue (v5) · route on confidence (v6).