docs: archive the July 2026 report; new methodology for the opencv5 run

The July report (4-model ArcFace/LVFace bake-off, pre-opencv5 framework,
3-film training + held-out validation) is superseded by the opencv5 build:
single-model LVFace-B, a 6-knob DE sweep over all 9 films, flood-fill
presence, and the registry/decode fixes. Rather than overwrite it, archive
it date-suffixed and start the current report fresh.

- Rename the six July result pages to *-2026-07.md, rewrite their
  intra-archive cross-links, and add an "Archived (July 2026)" banner to each.
- mkdocs nav: current report at top, the July set under an Archive section.
- New docs/methodology.md for the opencv5 run: corrects the withdrawn
  anneal_sec/extinction_sec presence bridging (windows are now
  [first_seen, last_seen], AR-012/013), documents the two presence modes
  (track_extent / flood), and records that every eval scores all 9 films.

The current experiment log (model-bakeoff.md) and Home rewrite land once
the DE sweep converges and the final optimum is known.
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# How we score against X-Ray
Every number in this report, every F1 and misID count, comes from one
comparison. The comparison has a mismatch at its core that shapes nearly
every finding in this report: the ground truth is scene-level, the
pipeline's output is per-second, and the two do not mean the same thing.
This page documents that comparison once, so the findings pages can rely on
it without re-explaining it.
Every number in this report comes from one comparison, and that comparison
has a mismatch at its core: the ground truth is scene-level, the pipeline's
output is per-second, and the two do not mean the same thing. This page
documents the comparison once so the findings can rely on it.
## What Amazon X-Ray records
@@ -13,12 +11,12 @@ X-Ray ships three tables per film: `scenes.csv` (a list of `[start, end]`
timespans), `people_in_scenes.csv` (which actors are credited in each
scene), and `people.csv` (actor identities). There is no per-frame or
per-second annotation anywhere in X-Ray. A scene might run 45 seconds, and
X-Ray records one cast list for the entire span, not "on screen from
second 12 to second 30."
X-Ray records one cast list for the entire span, not "on screen from second
12 to second 30."
To compare this against per-second predictions, `second_score.py` expands
every scene into per-second ground truth by copying the whole scene's cast
list onto every second inside it:
To compare against per-second predictions, `second_score.py` expands every
scene into per-second ground truth by copying the whole scene's cast list
onto every second inside it:
```python
for sn, (t0, t1) in spans.items():
@@ -27,48 +25,46 @@ for sn, (t0, t1) in spans.items():
timeline[t] = cast
```
That is the entire mechanism. If X-Ray credits five actors to a 30-second
scene, all five count as ground truth present for all 30 seconds, including
seconds where only one of them is on screen. This is not a simplification
introduced by the pipeline; it is the only reading of X-Ray's data that is
possible, because X-Ray itself does not record anything finer-grained.
If X-Ray credits five actors to a 30-second scene, all five count as ground
truth present for all 30 seconds, including seconds where only one is on
screen. This is not a simplification the pipeline introduces; it is the only
reading X-Ray's data supports, because X-Ray records nothing finer.
## Why an offscreen name can be scored correct
## How the pipeline reports presence
A name listed under Offscreen with a correct (green) label is not the
pipeline guessing or padding its score. It is the pipeline correctly
answering the question X-Ray actually asks: is this actor part of this
scene. It answers that question using a presence window (`[start, end]`,
held open across cuts by `anneal_sec` and `extinction_sec`), which matches
X-Ray's scene-level semantics more closely than a raw per-frame detection
would.
A presence claim is one actor owning one time window. How that window is
derived is a tunable choice — a knob the optimizer weighs — with two modes:
A system that only reported "this actor is visible in this exact frame"
would score worse against X-Ray's scene-level ground truth, producing a
false negative every time the camera cuts away from a character who is
still present in the scene. Not because it is wrong about the world, but
because it would be answering a stricter, different question than the one
X-Ray's data supports. The presence-window design exists specifically to
answer X-Ray's actual question.
- **`track_extent` (default).** A claim is exactly `[first_seen, last_seen]`
of a track the actor owned (AR-012), ending at the last sighting and never
after (AR-013). There is no keep-alive: the withdrawn `anneal_sec` and the
scene-tracker `extinction_sec` — which the July report's windows were held
open by — are **gone**. A track that survives its own gaps needs no bridge;
a gap after the final sighting is never claimed.
- **`flood`.** Each claim is snapped to the shot it sits in, so an actor seen
once anywhere in a shot is reported for the whole shot
`[prev_boundary, next_boundary]`. Boundaries come from TransNetV2 shot
detection when available, otherwise from the always-on histogram cut
detector (`is_cut`). This trades precision for recall against X-Ray's
scene-level granularity, and the optimizer decides per run whether it pays.
## What this resolves and what it does not
Do not confuse the surviving `track_extinction_sec` with the withdrawn
scene `extinction_sec`: the former bounds how long a lost track stays
available for **re-association** (a tracking question), and never extends a
presence claim.
This resolves the semantic mismatch between a scene and an instant. It does
not resolve two other limitations, both discussed in the
[LVFace deep dive](lvface-deep-dive.md).
## The two limits this does not resolve
**The face-vs-presence ceiling.** X-Ray credits scene membership regardless
of whether a face is ever visible: background crew, characters shot from
behind, voice-only presence. No amount of bridging recovers a face that
never appears on screen. This is a hard ceiling on recall, not a defect.
behind, voice-only presence. No face pipeline can recover a face that never
appears, so recall against X-Ray is a structural ceiling, not a defect.
**Extinction bridging can overshoot.** The same presence-window mechanism
that correctly answers "still in this scene" during a normal cut can also
bridge across a scene boundary it has no way to detect. A hard cut into a
different scene with no faces, such as closing credits, carries the
previous scene's identities forward until the window expires. This is the
mechanism behind Downton Abbey's recall collapse, documented in the deep
dive.
**Flood-fill can overshoot.** Snapping to a shot correctly answers "still in
this scene" through an intra-scene cut, but a shot boundary is not a scene
boundary: on a film with sparse cuts, flood-fill can carry an actor across a
long "shot" they only briefly appeared in. This is why flood-fill is a knob,
not a default — its value depends on the film's cut density.
## Precision, recall, and the misID weighting
@@ -77,49 +73,46 @@ Per sampled second `t`:
**TPI** (true positive instances): actors both X-Ray and the pipeline agree
are present.
**FPI** (false positive instances): actors the pipeline reports that are
not in X-Ray's cast for this second. Split into two categories:
**FPI** (false positive instances): actors the pipeline reports that are not
in X-Ray's cast for this second, split into:
- **FPI_incast**: the actor is in the film's cast, just not credited to
this particular scene. A timing or boundary slip.
- **FPI_incast**: the actor is in the film's cast, just not credited to this
scene. A timing or boundary slip.
- **FPI_misid**: the actor is not in the film's cast at all. A genuine
wrong-identity error, weighted 10x in the precision objective, because
naming someone who is not even in the film is a categorically worse
error than a few seconds of scene-boundary slop.
wrong-identity error, weighted **10×** in the precision objective, because
naming someone not even in the film is categorically worse than a few
seconds of scene-boundary slop.
!!! note "Every headline `P` and `F1` is misID-weighted"
The precision reported throughout this report, and therefore the F1
derived from it, puts each `FPI_misid` into the denominator **10 times**
Precision puts each `FPI_misid` into the denominator 10 times
(`precision = TPI / (TPI + FPI_incast + 10·FPI_misid)`,
[`second_score.py`](https://REPOLINK/scripts/optimizer/second_score.py)).
This is deliberate: the whole point is to punish naming an out-of-film
actor far harder than a scene-boundary slip. But it means the `P` column
is not raw precision, and a misID-heavy film's `P` is depressed
super-linearly. `second_score.py` also emits an unweighted `precision_raw`
(always ≥ the weighted `P`); where the gap matters, The Many Saints of
Newark, weighted `P` 54.7% vs. raw 78.4%, the [LVFace deep dive](lvface-deep-dive.md)
reports both. When comparing `P` across films, remember you are comparing a
quantity that penalizes misIDs, not just a hit rate.
This deliberately punishes naming an out-of-film actor far harder than a
boundary slip, so the `P` column is not raw precision and a misID-heavy
film's `P` is depressed super-linearly.
**FN** (false negatives): actors X-Ray lists that the pipeline never
reports, counted only for actors who have a gallery reference embedding.
Across the 9-film benchmark, coverage of X-Ray's credited cast ranges from
20% to 79% by film (see
[the full experiment log](model-bakeoff.md#gallery-coverage-per-film)); an
actor with no reference photo can never be recognized regardless of model
quality, and counting them as a miss would penalize gallery coverage, not
recognition accuracy.
**FN** (false negatives): actors X-Ray lists that the pipeline never reports,
counted **only** for actors who have a gallery reference embedding. An actor
with no reference photo can never be recognized, and counting them as a miss
would measure gallery coverage, not recognition accuracy.
Two further numbers are reported alongside F1:
Two further numbers accompany F1:
**agreement_rate**: mean per-second Jaccard overlap
(`|Pred ∩ GT| / |Pred GT|`), partial credit. Naming 2 of 3 present actors
scores 2/3, not 0.
(`|Pred ∩ GT| / |Pred GT|`) partial credit, so naming 2 of 3 present
actors scores 2/3, not 0.
**exact_match_rate**: the fraction of sampled seconds where the pipeline's
named set exactly equals X-Ray's, no partial credit. Far harsher, and
dominated by recall, since any single missed actor zeroes that second.
**exact_match_rate**: the fraction of seconds where the pipeline's named set
exactly equals X-Ray's no partial credit, dominated by recall.
## The benchmark set
Unlike the July report — which trained on a 3-film subset and validated on
held-out films to keep evaluations fast — this run scores **all 9 films on
every evaluation**. The registry one-clock fix and uncapped dumps made
full-set replay affordable, so the reported optimum is tuned against the
complete set rather than a training subset.
## Reproduce
@@ -129,6 +122,5 @@ python3 scripts/optimizer/second_score.py \
--gallery experiments/galleries/gallery_LVFace-B_Glint360K.h5
```
See also [the full experiment log](model-bakeoff.md) for how `pred.json` is
produced, and the [LVFace deep dive](lvface-deep-dive.md) for what these
mechanisms look like frame by frame.
See the [full experiment log](model-bakeoff.md) for how `pred.json` is
produced and where the shipped `src/config.hpp` defaults come from.