docs(VR-012): the quality knee, and what it retires

Sharpness is not a sufficient statistic for identity loss. Six cells at
effectively identical measured sharpness span 15.3% to 91.0% TPI,
ordered entirely by source size, because a scalar keyed on
high-frequency energy cannot separate attenuated high frequencies from
destroyed spatial sampling. AR-028's "kept separate, not collapsed into
one scalar" now rests on a measurement rather than an argument -- and
the reasoning it used to rest on, that the aligned crop is
scale-normalised so a measure there cannot re-measure size, was wrong
and is corrected in place.

Variance of Laplacian -- the most widely used blur metric there is -- is
anti-predictive at fixed degradation on all three blur families. The
decile it calls sharpest is 2.6x less identifiable than the decile it
calls blurriest, monotone across ten bins, because within a cell its
residual variance is native contrast rather than detail, and hard
shadows and JPEG ringing raise it while making a face harder to match.
Gating on it would preferentially discard the more identifiable faces.

Blur breaks confidence, not identity: rank-1 holds at 80.2% where TPI is
15.3%, and FPI never left 0.1% in any of the 108 cells. Degradation
produces abstention, never a wrong name. That is also why sharpness
fails as a compute gate -- even a visually destroyed face stays 46.9%
identifiable, so a gate discards recoverable evidence at three times the
cost of the free size filter. Discount, do not gate; the rule AR-028
already stated now has evidence for why it is right rather than merely
cautious.

Records the shape a discount must have (flat, then a cliff between sigma
2 and 3), that its cost scales with proximity to the decision boundary
rather than with blur, and that the pose half of VR-012 has not been
run.

TRACES: VR-012, AR-028, AR-029, AR-030 | SR-002

docs/SPEC.md

@@ -248,11 +248,29 @@ the same response.
 - **Size** — already AR-002, floor at 40×40 px in original resolution, measured
   end to end by VR-013. It is the precedent for the other two: the
   threshold was *located*, not chosen.
-- **Sharpness** — motion blur and soft focus destroy the high-frequency detail
-  the embedder keys on, and unlike size they leave the bounding box looking
-  perfectly healthy. Measured on the **112×112 aligned crop**, not the raw box:
-  the crop is already scale-normalised, so a measure taken there cannot silently
-  re-measure face size and double-count it against AR-002.
+- **Sharpness** — motion blur and optical defocus destroy the high-frequency
+  detail the embedder keys on, and unlike size they leave the bounding box
+  looking perfectly healthy. Measured on the **112×112 aligned crop**, not the
+  raw box.
+
+  An earlier version of this clause argued the crop is scale-normalised and so a
+  measure taken there "cannot re-measure face size and double-count it against
+  AR-002". **That reasoning is wrong and VR-012 measured it wrong.** The
+  normalisation is geometric, not informational: a 40 px face upscaled into the
+  canonical frame genuinely carries less high-frequency content than a 400 px
+  one downscaled into it, so every candidate measure *does* respond to source
+  size. What the crop yields is **effective resolution in canonical space** —
+  the union of "was small" and "was blurred", not blur alone.
+
+  The conclusion survives, for a better reason. VR-012 sorted its grid by
+  measured sharpness and found the six cells at effectively identical sharpness
+  (0.0003–0.0005) spanning **15.3% to 91.0% TPI**, ordered entirely by source
+  size. Sharpness is therefore not a sufficient statistic for identity loss: a
+  scalar keyed on high-frequency energy cannot separate *attenuated* high
+  frequencies from *destroyed* spatial sampling, because blur preserves
+  mid-frequency facial geometry exactly while downsampling destroys it. The two
+  axes are not redundant and neither substitutes for the other — which is what
+  "not collapsed into one scalar" above now rests on.
 - **Visibility** — extreme pose or occlusion means the face presents fewer of the
   features the embedding assumes are present. The measure is the **residual of
   the AR-005 alignment fit**: the RMS landmark error, in canonical 112×112
@@ -333,18 +351,63 @@ hand-chosen cutoff on an uncalibrated measure is the same unfalsifiable magic
 number AR-024 retired for similarity, and it would fail the same way: meaning
 something different for every detector, every embedder and every film.
 
+**A discount curve on sharpness must be flat, then steep.** VR-012 measured the
+response as a cliff rather than a gradient: Gaussian sigma up to 1.5 costs under
+1.5 points of TPI in every cell — at 16 px it is very slightly *positive*,
+smoothing upscale artifacts — sigma 2 costs 1–3, and the 2→3 step costs 7–19. A
+linear or sigmoid discount over the measure would penalise the whole flat region
+where blur demonstrably costs nothing.
+
+**Which blur is modelled is a first-order decision, not a detail.** VR-012 swept
+three families at matched per-axis PSF spread, and at σ=3 px on a 112 px face
+they cost 9%, 18% and **53%** error for Gaussian, motion and optical defocus
+respectively. Defocus is the destructive one because its disc PSF has a jinc
+transfer function with **exact zeros** — bands annihilated rather than
+attenuated — where a Gaussian merely rolls off. It is also the case AR-002
+cannot catch, since a defocused face is large and confidently detected. Any
+future study that sweeps blur states its family and its justification; a
+Gaussian-only sweep understated the effect by a factor of five and would have
+retired this axis as not worth its cost.
+
+**The cost of blur is proportional to proximity to the decision boundary, not to
+blur itself.** Sigma 3 costs −22.5 points at 24 px, but only −7.9 at 112 px
+(margin to spare) and −8.3 at 16 px (already below threshold). This is why the
+axes must combine multiplicatively in `EvidenceDiscounter` rather than each
+gating independently.
+
+**Sharpness discounts; it must never gate.** VR-012 tried the gate directly, as
+a compute saving: skipping the embed below a sharpness threshold costs 15.1% of
+true identifications to save 20% of the work, against the size filter's 4.7% at
+16.7% — three times the damage, from a measure that needs the warped crop plus a
+DFT where size is a bbox dimension available for free. The reason is a ceiling
+no measure can beat: **at 112 px with defocus radius 6 — visually destroyed —
+46.9% of faces still identify correctly, and rank-1 is still 94.8%.** Apparent
+blur does not determine the outcome. The size filter wins only because smallness
+destroys identity more completely than blur does (16 px succeeds 23.5% of the
+time), and that asymmetry is the measured justification for the rule above:
+**failing sharpness discounts the observation, failing size may drop it.**
+
 **Current:** visibility is measured and carried — `estimate_alignment()` in
 `src/face_utils.hpp` returns the residual alongside the transform, and
-`FaceAlignerFunc` writes it to `DetectedFace::alignment_residual`. Size is
-`min_face_px` (40, decoded-frame space — AR-002 still open). Sharpness is
-unmeasured. Nothing yet *consumes* any of it: no discount is applied, and
-`align_face()` still drops the degenerate-fit case without counting it.
+`FaceAlignerFunc` writes it to `DetectedFace::alignment_residual`. Sharpness is
+measured: `assess_sharpness()` in `src/quality.hpp` returns five AR-029
+candidates over a fixed 64×64 window on the face interior, and VR-012 has ranked
+them — `var_laplacian` and `tenengrad` are disqualified as discounts (see
+AR-029), leaving `hf_energy_ratio` as the only correctly-signed survivor. Size
+is `min_face_px` (40, decoded-frame space — AR-002 still open). All three are
+exposed to studies through `sae_embed`. Nothing yet *consumes* any of it: no
+discount is applied, and `align_face()` still drops the degenerate-fit case
+without counting it.
 
-**Gap:** AR-029 entirely. For AR-030, the measure exists but the discount does
-not — it must reach `EvidenceDiscounter` as the reliability term. For AR-028, the
-residual does not yet reach the VR-001 dump, which is what VR-012 needs to run
-from fixtures; that is the next step, since it unblocks the study that sets
-every remaining behaviour.
+**Gap:** the discount itself, on every axis. Neither sharpness nor the residual
+reaches `EvidenceDiscounter`, whose weight remains pure novelty — so a profile
+or defocused view still moves a track's belief hardest when it deserves the
+least trust. Neither reaches the VR-001 dump either, so VR-012 must still re-run
+video rather than replay fixtures. VR-012's **pose half is not started**: the
+AR-030 residual has no arm in the grid, so whether the 5-point proxy suffices or
+a dedicated landmark model is needed remains open. And the sharpness result is
+weak enough (best within-cell AUC 0.530) that whether AR-029 earns a discount at
+all is still a judgement, not a measurement.
 
 ## AR-007, AR-008 — Tracking