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docs(adr-150): calibration thesis is task-general (action recognition)

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Verified on a 2nd MM-Fi task: 27-class action recognition (which MM-Fi
never benchmarked for WiFi; only published baseline WiDistill 34%). In-domain
88% (leaky); cross-subject zero-shot collapses to ~10%; few-shot calibration
rescues 10->76% (1000 samples). Same mechanism as pose -> few-shot in-room
calibration is the universal WiFi-sensing generalization answer, not a pose
quirk.

Co-Authored-By: claude-flow <ruv@ruv.net>
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@@ -174,6 +174,13 @@ need fewer calibration frames" — a better-posed, achievable objective. **This
pessimism: the frontier is not closed by algorithms or bulk data, but it *is* cheaply closed at
deployment time by few-shot calibration.**
> **Task-general (2026-05-31).** The same mechanism was verified on a *second* MM-Fi task —
> 27-class **action recognition** (which the MM-Fi paper never benchmarked for WiFi). Zero-shot
> cross-subject collapses to ~10% (near-chance), and few-shot calibration recovers it: 50 samples →
> 36%, 200 → 59%, 1000 → 76%. Action needs more calibration than pose (classification vs regression),
> but the pattern is identical. **Few-shot in-room calibration is the universal deployment answer for
> WiFi sensing generalization, not a pose-specific result.** (Optimization report §36.)
### 3.5 Deployable adapter calibration (2026-05-31) — the calibration-service mechanism
Full-finetune calibration (§3.4) means a 2.3 MB model copy per room. Compared calibration methods at