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
+- **Sharpness** — motion blur and optical defocus destroy the high-frequency
-  the embedder keys on, and unlike size they leave the bounding box looking
+  detail the embedder keys on, and unlike size they leave the bounding box
-  perfectly healthy. Measured on the **112×112 aligned crop**, not the raw box:
+  looking perfectly healthy. Measured on the **112×112 aligned crop**, not the
-  the crop is already scale-normalised, so a measure taken there cannot silently
+  raw box.
-  re-measure face size and double-count it against AR-002.
+
+  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
+`FaceAlignerFunc` writes it to `DetectedFace::alignment_residual`. Sharpness is
-`min_face_px` (40, decoded-frame space — AR-002 still open). Sharpness is
+measured: `assess_sharpness()` in `src/quality.hpp` returns five AR-029
-unmeasured. Nothing yet *consumes* any of it: no discount is applied, and
+candidates over a fixed 64×64 window on the face interior, and VR-012 has ranked
-`align_face()` still drops the degenerate-fit case without counting it.
+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
+**Gap:** the discount itself, on every axis. Neither sharpness nor the residual
-not — it must reach `EvidenceDiscounter` as the reliability term. For AR-028, the
+reaches `EvidenceDiscounter`, whose weight remains pure novelty — so a profile
-residual does not yet reach the VR-001 dump, which is what VR-012 needs to run
+or defocused view still moves a track's belief hardest when it deserves the
-from fixtures; that is the next step, since it unblocks the study that sets
+least trust. Neither reaches the VR-001 dump either, so VR-012 must still re-run
-every remaining behaviour.
+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
 

docs/index.md

@@ -53,6 +53,14 @@ every finding below.
     A training-set effect that did not reproduce on 5 held-out films once
     two methodology bugs in the comparison harness were found and fixed.
 
+-   :material-blur:{ .lg .middle } **[What does blur cost?](quality-knee.md)**
+
+    ---
+
+    Sharpness is not a sufficient statistic for identity loss, blur breaks
+    confidence rather than ranking, and variance-of-Laplacian is
+    anti-predictive at fixed resolution.
+
 -   :material-magnify-expand:{ .lg .middle } **[Deep dive: LVFace-B Glint360K](lvface-deep-dive.md)**
 
     ---

docs/quality-knee.md

@@ -0,0 +1,308 @@
+# Quality knee: what does a blurred or small face cost, and can a measure predict it?
+
+VR-012. Companion to the minimum-face-size studies VR-005 and VR-013 (see the
+[requirement register](requirements.md)), which located the size floor at 40 px;
+this asks the same question for **sharpness**, and asks whether any cheap
+measure taken on the aligned crop can be acted on at inference.
+
+Run by
+[`scripts/validation/quality_knee.py`](https://REPOLINK/scripts/validation/quality_knee.py)
+through the `sae_embed` bindings — detection, the ArcFace warp, the embedder,
+the five candidate measures and the Platt calibration are all the shipped C++.
+
+## Protocol
+
+1670 gallery actors with 3 or more mugshots (of 2456 total), one image held out
+per actor as a probe, the remaining 10326 embeddings staying in the gallery at
+native resolution. Only the probe degrades — reference mugshots are clean and
+the face coming out of the video is not.
+
+Each probe passes through a **joint grid**: downscale to *S*×*S* and back to
+112 (the sampling loss), then blur at level *L* in canonical pixels. Three blur
+families, 36 cells each, 60120 probe-cell records per family:
+
+| family | models | parameter |
+|---|---|---|
+| Gaussian | soft focus, a generic stand-in | sigma 0 … 3 |
+| **Disc** | **real optical defocus** — the circle of confusion | radius 0 … 6 |
+| Motion | camera pan or moving subject | length 0 … 21 px |
+
+The three are not interchangeable, and sweeping only the first was the original
+design error — one that would have produced a wrong answer, not merely an
+incomplete one (Result 3). A defocused lens spreads a point into a **uniform
+disc**, whose transfer function is a jinc — `2·J1(x)/x` — that crosses zero and
+goes negative, annihilating whole frequency bands and returning the ones beyond
+each zero phase-reversed. A Gaussian MTF is strictly positive and monotone and
+does neither. More practically: defocus and motion are how a face ends up
+**large and useless**, while Gaussian blur as swept here mostly co-occurs with
+small faces. That difference decides whether sharpness carries anything the size
+filter does not.
+
+Families are compared at matched **per-axis PSF standard deviation** (σ for a
+Gaussian, R/2 for a disc, L/√12 for a linear smear), never at equal raw
+parameter, which would compare different amounts of damage.
+
+Identification is the pipeline's own decision: per-actor best-of-N cosine →
+Platt sigmoid → accept above `prob_threshold` 0.754. Never a raw cosine
+(AR-024).
+
+## Result 1 — sharpness is not a sufficient statistic
+
+Sorting the 36 Gaussian cells by `hf_energy_ratio`, the six sigma-3 cells land
+at effectively identical measured sharpness:
+
+| size | sigma | hf_energy_ratio | TPI |
+|---|---|---|---|
+| 16 | 3 | 0.0003 | **15.3%** |
+| 24 | 3 | 0.0003 | 63.2% |
+| 32 | 3 | 0.0003 | 79.4% |
+| 48 | 3 | 0.0003 | 86.6% |
+| 64 | 3 | 0.0004 | 88.4% |
+| 112 | 3 | 0.0005 | **91.0%** |
+
+Same measured sharpness, a **76-point spread in identification**. It inverts
+too: 16 px unblurred measures 0.0033 and scores 23.5%, while 48 px at sigma 2
+measures *lower* at 0.0021 and scores 96.6%.
+
+A canonical-frame sharpness scalar cannot separate *attenuated* high
+frequencies from *destroyed* spatial sampling. Blur suppresses the high band
+while preserving mid-frequency facial geometry exactly; downsampling to 16 px
+destroys that geometry outright. Both look alike to any measure keyed on
+high-frequency energy.
+
+This is the measured basis for AR-028's rule that the axes are **kept separate
+and not collapsed into one scalar**, and it settles the double-counting
+question: size and sharpness are not redundant, and neither substitutes for the
+other.
+
+## Result 2 — blur is a cliff, and it breaks confidence, not identity
+
+TPI % by size (rows) against Gaussian sigma (columns):
+
+| size | 0 | 0.5 | 1 | 1.5 | 2 | 3 |
+|---|---|---|---|---|---|---|
+| 16 | 23.5 | 24.0 | 25.0 | 24.6 | 23.9 | 15.3 |
+| 24 | 85.7 | 85.1 | 85.6 | 85.9 | 82.6 | 63.2 |
+| 32 | 95.9 | 95.9 | 96.0 | 95.5 | 93.7 | 79.4 |
+| 48 | 98.7 | 98.7 | 98.4 | 98.1 | 96.6 | 86.6 |
+| 64 | 98.6 | 98.6 | 98.8 | 98.4 | 97.5 | 88.4 |
+| 112 | 98.9 | 98.9 | 98.8 | 98.6 | 98.0 | 91.0 |
+
+Three regimes: **sigma ≤ 1.5 is free** (every cell moves under 1.5 points, sign
+flipping at random — at 16 px it slightly *improves*, smoothing upscale
+artifacts); sigma 2 costs 1–3 points; the 2→3 step costs 7–19. A smooth
+discount curve is therefore the wrong shape — the response is flat, then falls
+off a cliff.
+
+**The cost peaks at the size knee, not at full resolution.** Sigma 3 costs
+−22.5 points at 24 px but only −7.9 at 112 px and −8.3 at 16 px. Blur has no
+intrinsic cost; it costs in proportion to how close the observation already sits
+to the decision boundary. At 112 px there is margin to spare, at 16 px the probe
+is already below threshold, and at 24 px it sits exactly on the knee.
+
+**What blur destroys is confidence, not ranking.** Rank-1 barely moves: 99.3% →
+99.2% at 112 px across the whole sigma range. The extreme case is 16 px at sigma
+3, where rank-1 is **80.2%** while TPI is **15.3%** — 65 points of probes have
+the correct actor ranked first and are rejected anyway for falling under the
+probability threshold.
+
+That is why **FPI never left 0.1% in any of the 108 cells across all three
+families**. Degradation produces TBI, never a wrong name. The calibration
+degrades gracefully, which is what SR-002 needs.
+
+## Result 3 — the blur *family* matters more than the blur *amount*
+
+Comparing families by their raw parameter is meaningless — sigma, radius and
+length are different units. They are matched here by the **per-axis standard
+deviation of the PSF**, which puts them on one scale:
+
+| family | per-axis σ | level giving σ = 3 px |
+|---|---|---|
+| Gaussian σ | σ | 3 |
+| Disc radius R | R/2 | 6 |
+| Motion length L | L/√12 | 10.4 |
+
+For reference the ArcFace template places the eyes 35.2 canonical px apart, so
+σ = 3 px is 9% of the inter-ocular distance.
+
+TPI at matched severity, interpolated within each family:
+
+| size | σ=3 Gaussian | σ=3 Motion | σ=3 **Defocus** | defocus penalty |
+|---|---|---|---|---|
+| 16 | 15.3 | 15.1 | 11.0 | +4.3 |
+| 24 | 63.2 | 61.1 | 41.4 | +21.9 |
+| 32 | 79.4 | 76.1 | 50.4 | +29.0 |
+| 48 | 86.6 | 80.9 | 52.6 | +34.0 |
+| 64 | 88.4 | 81.7 | 51.0 | +37.4 |
+| 112 | 91.0 | 82.1 | **46.9** | **+44.0** |
+
+**Optical defocus is up to 44 points more destructive than a Gaussian of
+identical spread**, and the ordering is defocus ≫ motion > Gaussian throughout.
+At σ=1 the three families are indistinguishable, and at σ=2 they differ by under
+5 points; the divergence appears only when both the blur is severe *and* the face
+is large.
+
+That pattern is physically consistent. At 16 px the resampling has already
+removed the high frequencies, so the PSF's shape has nothing left to act on and
+all three agree. At 112 px the full spectrum is present and shape decides: a
+Gaussian MTF rolls off gently and always leaves *some* energy at every
+frequency, so the embedder receives a merely attenuated signal, while a disc MTF
+is a jinc that **hits exact zeros** — whole frequency bands annihilated rather
+than attenuated, with the bands beyond each zero returning phase-reversed.
+Motion sits between them because it ruins one axis and leaves the perpendicular
+one untouched.
+
+**The methodological consequence is the important one.** This study originally
+swept Gaussian blur alone and concluded blur was a minor effect. On the family
+that actually occurs in film, the same nominal severity costs **53% error
+instead of 9%** at full resolution. A threshold set from the Gaussian arm would
+have been wrong by a factor of five in error rate, and the axis would probably
+have been dropped as not worth its cost.
+
+**Defocus is also the case a size gate cannot catch.** Every one of those 112 px
+faces is large and confidently detected, and sails through AR-002 untouched.
+That, not the Gaussian result, is what justifies a sharpness axis existing at
+all.
+
+## Result 4 — variance of Laplacian is anti-predictive at fixed degradation
+
+Pooled across all cells, every candidate scores AUC 0.76–0.80 for predicting
+correct identification, with textbook `var_laplacian` top. That number is close
+to worthless: it rewards a measure for detecting *how degraded the crop is*,
+which all five do. The question a per-observation discount needs is whether, at
+a **fixed** degradation, the measure predicts which faces fail:
+
+| measure | Gaussian | Defocus | Motion |
+|---|---|---|---|
+| `hf_energy_ratio` | **0.530** | **0.521** | **0.557** |
+| `norm_var_laplacian` | 0.520 | 0.507 | 0.539 |
+| `dir_min_tenengrad` | 0.524 | 0.512 | 0.506 |
+| `tenengrad` | 0.433 | 0.437 | 0.457 |
+| `var_laplacian` | 0.423 | 0.422 | 0.473 |
+
+Best is 0.557 — barely above chance, and `hf_energy_ratio` wins on all three
+families. `var_laplacian` is anti-predictive on all three too, so that finding
+does not depend on the blur model.
+
+**The two metrics measure different jobs, and the candidates split along that
+line.** On the motion arm `dir_min_tenengrad` has the best *pooled* AUC by a
+wide margin — **0.854** against 0.792 for the next — exactly as its synthetic
+directional-blur ladder predicted, yet its within-cell AUC there is 0.506. It is
+an excellent detector of *how badly smeared a crop is* and no guide at all to
+*which face will be recognised*. Pooled AUC is the right metric for a
+gross-degradation flag; within-cell AUC is the right one for a per-observation
+discount; a measure can be strong at one and useless at the other.
+
+Deciles within the 16 px Gaussian cell, where 1277 failures give the test real
+power:
+
+| `var_laplacian` decile | TPI |
+|---|---|
+| 0.00071–0.00192 (blurriest) | **37.1%** |
+| 0.00242–0.00278 | 22.8% |
+| 0.00397–0.00447 | 25.7% |
+| 0.00625–0.01445 (sharpest) | **14.4%** |
+
+The faces the measure calls sharpest are **2.6x less identifiable** than those
+it calls blurriest, monotone across ten bins of 167. Within a cell every crop
+received identical degradation, so the residual variance is *native contrast*,
+not native detail — and hard shadows, high-contrast lighting, sharpening halos
+and JPEG ringing all raise Laplacian variance while making a face harder to
+match. The measure reads photographic style and encoding artifacts and calls
+them sharpness.
+
+`hf_energy_ratio` is the only candidate with a correctly-signed within-cell
+trend (16.2% → 35.3% across the same deciles), being a pure ratio in which the
+contrast factor cancels.
+
+**Consequence:** a per-face quality *discount* keyed on variance of Laplacian —
+the most widely used blur metric in production vision pipelines — would
+systematically down-weight the *more* identifiable faces. It is worse than no
+discount.
+
+## Result 5 — as a compute gate, sharpness loses to the size filter
+
+Skipping the embed for crops below a threshold, measured as compute saved
+against true identifications lost:
+
+| gate | skipped | true IDs lost | of skipped, doomed anyway |
+|---|---|---|---|
+| `hf_energy_ratio` < 0.00023 | 10.0% | 7.6% | 37.9% |
+| `hf_energy_ratio` < 0.00051 | 20.0% | 15.1% | 38.7% |
+| **source size < 24 px** | **16.7%** | **4.7%** | **77.3%** |
+
+At a comparable skip rate the size filter loses **4.7% against sharpness's
+15.1%** — three times less damage — and it is free, being a bbox dimension
+available before alignment or embedding, where sharpness needs the warped crop
+plus a colour convert, three convolutions and a 64×64 DFT.
+
+Restricting to large faces (≥64 px) on the **defocus** arm, where the size
+filter is blind, improves the gate's precision 3.5x (37% of skipped crops doomed
+versus 10.7% on the Gaussian arm) but not its trade: skip 10%, lose 7.0%.
+
+A hard ceiling explains why. **At 112 px with defocus radius 6 — visually
+destroyed — 46.9% of faces still identify correctly and rank-1 is still 94.8%.**
+Blur does not determine the outcome, so any gate keyed on apparent blur is
+predicting a coin flip. The size filter wins not because size is better
+measured, but because *smallness destroys identity more completely than blur
+does*: 16 px faces succeed only 23.5% of the time, so discarding them is cheap.
+
+## What this means for the requirements
+
+**Do not gate on sharpness; discount on it.** Heavily defocused faces remain
+~47% identifiable, so a gate destroys recoverable evidence. This is the first
+hard evidence that AR-028's "**discounts the observation, never deletes the
+detection**" is right on the merits rather than merely cautious. Since ranking
+survives where confidence does not, the per-track accumulation (AR-025) should
+recover much of what a single-frame threshold rejects — which is also the
+argument for the discount living in `EvidenceDiscounter` rather than in a filter.
+
+**`var_laplacian` and `tenengrad` are disqualified as discounts** by Result 4,
+on all three blur families. They remain usable as coarse *gross-degradation*
+detectors, the role in which their pooled AUC is real — the same role the size
+filter plays — but they must never weight a per-observation belief.
+
+**`hf_energy_ratio` is the only surviving discount candidate**, best on all
+three families, and its within-cell signal (0.52–0.56) is weak enough that
+shipping a discount on it needs justification beyond this study.
+
+**`dir_min_tenengrad` earns a different job.** Its pooled 0.854 on the motion arm
+makes it the best available detector of gross directional smear — useful as a
+per-frame "this shot is unusable" flag, which is a decision about a *frame*, not
+a weighting of an *observation*. If AR-029 ships two measures for two roles, this
+is the second one, and it must not be confused with the first.
+
+**Model the blur family, not just its amount.** Result 3 makes the choice of
+degradation model a first-order design decision rather than a detail: the same
+matched severity costs 9% or 53% error depending on the PSF. Any future study
+that sweeps blur must state which family it used and why.
+
+**Any discount curve must be flat then steep**, not linear or sigmoid over the
+measure. Blur costs nothing until it costs a great deal.
+
+## Limitations
+
+- **Cooperative population.** Gallery mugshots are frontal and well-lit;
+  within-cell failures are likely dominated by cross-view mismatch, which no
+  sharpness measure can predict. Read the ~chance within-cell AUCs as "sharpness
+  does not predict the dominant failure mode *here*", not as "sharpness is
+  meaningless".
+- **Uniform grid, not a natural distribution.** Sizes and blur levels are
+  sampled evenly, so "skip 16.7%" is exactly the 16 px row. The gate comparisons
+  are like-for-like on identical records, but the absolute savings are not what
+  a film would show.
+- **TensorRT fp16.** A different realisation of the embedder from the fp32 ONNX
+  reference — VR-005 measured ~0.85 cosine agreement with separation intact.
+  Gallery and probes share one session so the study is internally consistent,
+  but the absolute knee belongs to the fp16 space.
+- **Blur is applied in the canonical frame**, after resampling, so its width is
+  independent of the cell's size. Real optics blur before sampling.
+- **The top motion rung is an anchor, not an operating point.** Length 21 is a
+  per-axis σ of 6.1 — 17% of the inter-ocular distance, a streak rather than a
+  face — and it is swept to bound the curve, not because a frame like that is
+  worth reasoning about. Its 3.4% TPI at 112 px should not be quoted as a
+  headline. The same caution applies less severely to defocus radius 6 (σ = 3).
+- **Per-axis σ equates spread, not perceptual damage.** It is the fairest single
+  scalar for comparing PSFs, but Result 3 is precisely the finding that equal
+  spread does *not* mean equal harm, so the matched-severity tables compare
+  like-for-like inputs, not like-for-like severity as a face would experience it.

docs/requirements.md

@@ -56,7 +56,7 @@ Status: `Done` · `In Progress` · `Planned` · `TBD` · `Withdrawn`
 | AR-026 | All similarity computed as GEMM, including annex and deferred pass | SR-001 | High | In Progress |
 | AR-027 | Throughput acceptable for **arbitrary** gallery size | SR-001 | High | Planned |
 | AR-028 | **Embedding input quality assessed and carried** — every face scored on size, sharpness and visibility before its embedding is used as identity evidence; the vector travels with the face and reaches the VR-001 dump | SR-002 | High | Planned |
-| AR-029 | Sharpness measure on the **aligned crop** (scale-normalised, so it cannot re-measure size) | SR-002 | Medium | Planned |
+| AR-029 | Sharpness measure on the **aligned crop**, consumed as a discount and **never as a gate** | SR-002 | Medium | **In Progress** — five candidates implemented (`src/quality.hpp`) and ranked by VR-012 over three blur families. `var_laplacian` and `tenengrad` are **disqualified as discounts**: within a fixed degradation they are anti-predictive on *all three* families (AUC 0.42–0.47; the decile the measure calls sharpest is 2.6× *less* identifiable), since their residual variance is native contrast, not detail. `hf_energy_ratio` is the only correctly-signed survivor, best on all three, and weak (0.52–0.56). `dir_min_tenengrad` is the best *gross-smear detector* (pooled AUC 0.854 on motion) but ~chance within-cell, so it serves a per-frame flag, not a per-observation weight. The parenthetical this row used to carry — "scale-normalised, so it cannot re-measure size" — was wrong: every candidate responds to source size, and the axes are separable for a different reason (see AR-028) |
 | AR-030 | Visibility measure from the AR-001 5-point landmarks — extreme pose or occlusion **discounts the observation, never deletes the detection** | SR-002 | Medium | **In Progress** — measure is the AR-005 alignment residual (`estimate_alignment()`), carried on `DetectedFace`; roll/scale invariance and monotonicity under foreshortening asserted. Nothing consumes it as a discount yet |
 
 ## Deployment (DP)
@@ -114,7 +114,7 @@ Status: `Done` · `In Progress` · `Planned` · `TBD` · `Withdrawn`
 | VR-009 | Verify accumulated posteriors are calibrated against held-out tracks | PR-002 | High | Planned |
 | VR-010 | Dump provenance attributes — embedder model, detector settings, `dense_scale`, `scene_detect`, sample rate | PR-002 | **High** | Planned |
 | VR-011 | Rewrite the replay harness for the post-AR-012 output contract | PR-002 | High | Planned |
-| VR-012 | Quality-knee study — TPI/FPI vs sharpness and vs pose, as VR-005 did for size; also settles whether the 5-point pose proxy needs a dedicated landmark model | PR-002 | Medium | Planned |
+| VR-012 | Quality-knee study — TPI/FPI vs sharpness and vs pose, as VR-005 did for size; also settles whether the 5-point pose proxy needs a dedicated landmark model | PR-002 | Medium | **In Progress** — sharpness half done ([`docs/quality-knee.md`](quality-knee.md)): 1670 actors, joint size×blur grid over three blur families (Gaussian, disc defocus, linear motion), 60120 probe-cell records each. Sharpness is **not a sufficient statistic** (equal measured sharpness spans 15.3–91.0% TPI, ordered by source size); **the blur family matters more than its amount** — at matched per-axis σ=3 on a 112 px face, Gaussian/motion/defocus cost 9/18/**53**% error, so a Gaussian-only sweep understates real lens blur fivefold; blur breaks **confidence, not ranking** (rank-1 80.2% where TPI is 15.3%), so FPI never left 0.1% in any of the 108 cells; a sharpness **gate** loses 3× more true presence than the free size filter at equal saving, because even destroyed faces stay 46.9% identifiable. **Pose half not started** — the AR-030 residual is exposed via `sae_embed.alignment_residual` but no pose arm has been run, so the dedicated-landmark-model question is still open |
 | VR-014 | Audio-signature **offset recovery on real content** — a known trim recovered from film audio, not from the synthetic golden tone | PR-002 | Medium | **Done** — 40 random in-cap offsets, every one recovered to the nearest frame: **worst error 46 ms against a 500 ms budget**, and 46 ms is the floor rather than a result, since the offset is quantised to whole 92.88 ms frames. The `runtime/2` anchor confirmed through real head-trimmed files (a `delta` trim moves the window by `delta/2`). The one soft spot is **tier labelling, not accuracy**: the score falls with sub-frame misalignment (0.94–0.99 near a frame boundary, 0.69–0.73 at half a frame), so 27/40 correct alignments were demoted to `loose`. ±1 frame of slack in the *score* fixes it — measured, all 40 back to `audio` (min 0.906), false matches unmoved at 0.12–0.16, costing 81 ms of the budget |
 | VR-013 | Cross-source identification probe — gallery from one recording, probes from another, swept over input resolution end to end | PR-002 | Medium | **In Progress** — holding 90% of the plateau needs ~50 px end to end against VR-005's ~22 px, the gap being detection and landmark error; **`min_face_px` 40, since 32 admits faces in the falling region** (AR-002). FPI 0.0% at every scale. Ceiling is cross-view, not resolution |
 

mkdocs.yml

@@ -39,6 +39,7 @@ nav:
     - Best Model: best-model.md
     - Gallery Scope (Full vs. Limited): gallery-scope.md
     - Pose Expansion: pose-expansion.md
+    - Quality Knee (Blur and Size): quality-knee.md
     - LVFace Deep Dive: lvface-deep-dive.md
   - Full Experiment Log: model-bakeoff.md
   - Service Conversion (proposal): service-conversion.md