diff --git a/.gitignore b/.gitignore
index 08344c13..a7edea79 100644
--- a/.gitignore
+++ b/.gitignore
@@ -102,6 +102,9 @@ analysis_out/op/*
 !analysis_out/scoreboard.json
 !analysis_out/farfield_forensics.json
 !analysis_out/silent_activation.json
+# The #126 cascade go/no-go: rampnet's heatmap at the ramps a challenger recovers.
+!analysis_out/cascade_gate.json
+!analysis_out/cascade_gate_op030.json
 
 # Generated tagging pages live next to their (committed) galleries; regenerate with
 # scripts/analysis/make_tagger.py rather than tracking a build artifact.
diff --git a/analysis_out/cascade_gate.json b/analysis_out/cascade_gate.json
new file mode 100644
index 00000000..b0e31666
--- /dev/null
+++ b/analysis_out/cascade_gate.json
@@ -0,0 +1,5382 @@
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diff --git a/analysis_out/cascade_gate_op030.json b/analysis_out/cascade_gate_op030.json
new file mode 100644
index 00000000..1a5be6f5
--- /dev/null
+++ b/analysis_out/cascade_gate_op030.json
@@ -0,0 +1,5383 @@
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+    },
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+    {
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+      "nearest_peak_score": 0.09808053821325302,
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+      "null_pct": 0.84,
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+      "x": 0.44606062455610795,
+      "y": 0.5233333703243371
+    }
+  ],
+  "skipped_sites": 0,
+  "split": "richmond",
+  "vistas_input_size": [
+    1024,
+    1024
+  ]
+}
diff --git a/docs/model_comparison.md b/docs/model_comparison.md
index 46c3971b..b882d160 100644
--- a/docs/model_comparison.md
+++ b/docs/model_comparison.md
@@ -1209,6 +1209,14 @@ upsample can produce is on the order of 114 px and the 16 px floor drops nothing
 at 16 rather than recalibrated because it sits in the cache signature — changing it would
 orphan both arms' published detections to no effect.
 
+**That inertness is a property of 384×384, not of the floor, and it does not carry to the
+parity run below.** At 1024×1024 input the mask logits arrive at 256×256 and are upsampled
+only 4×, so the smallest component the upsample can produce is about **16 px — exactly the
+floor**. `min_area_px=16` is therefore marginally *binding* at parity where it was provably
+inert at 384, and it is the one setting shared by both that does not mean the same thing in
+each. It was still left untouched, because changing it would confound the single variable the
+parity run exists to isolate.
+
 The **rig** is the identical six-view one every other tiled leg uses
 (`equirect_tiling.default_views()`: 6 yaws, 90°×90°, pitch −30°, 1024×1024, source capped at
 4096) — but **the rig is not what the model sees, and that is a real caveat on the headline
@@ -1224,10 +1232,10 @@ upgrade could have changed every mask under an unchanged cache key.
 What changed here: `--vistas-input-size H W` now overrides the processor, and
 `--vistas-revision` pins the checkpoint. Both are recorded in the signature **only when set**,
 so the published richmond detections keep their key and nothing already paid for is orphaned —
-and a future run at parity is a distinct, self-describing cache entry. **The parity run has not
-been done**, so every number in this section is at 384×384 and the gap to RampNet is an upper
-bound on this arm, not a measurement of it at equal input. That is the first thing to try if
-this arm is ever revisited.
+and a future run at parity is a distinct, self-describing cache entry. **The parity run has now
+been done** — see *Resolution parity* below. Every number in the table that follows is still at
+384×384, and the parity numbers are reported separately rather than replacing them, because the
+384 run is the one whose detections are published.
 
 Note for anyone reading #126: that issue says `scripts/box_gallery.py` already cuts
 perspective views. **It does not** — its `--fov` sizes an axis-aligned crop of the
@@ -1316,6 +1324,467 @@ AP at all, emitting boxes without scores, so they are pinned at one operating po
 be tuned, and a tunable model at AP 0.513 is a more useful starting point than an untunable one
 at F1 0.664.
 
+#### Resolution parity: the handicap was real, and it was not the problem (2026-08-18)
+
+Everything above is measured at 384×384. This section removes that handicap and changes one
+variable. Read fixed in advance and posted to #126 **before** the scored output was read: the
+gap closes by **< 0.05 F1** ⇒ *"transfers but does not compete"* stands and this stays a
+one-split arm; more than that ⇒ the write-up is revised and 3–4 further splits get costed.
+
+Run on **makelab2 (A40, fp16, transformers 5.15.0 / torch 2.13.0+cu130)**, in a scratch worktree
+with a private `--cache-dir`, so nothing here shares a cache directory with the published
+detections. RampNet is re-run alongside as the comparability check and **reproduces its committed
+richmond row to every digit** (0.964 / 0.768 / 0.855, AP 0.763, 238/9/72).
+
+| arm | model input | P | R | F1 | AP | tp/fp/fn |
+|---|---|---:|---:|---:|---:|---|
+| rampnet (committed, reproduced) | — | 0.964 | 0.768 | **0.855** | 0.763 | 238/9/72 |
+| vistas curb-cut — **published** (RTX 3070) | 384×384 | 0.411 | 0.697 | 0.517 | 0.513 | 216/309/94 |
+| vistas curb-cut — **same-env control** (A40) | 384×384 | 0.411 | 0.694 | 0.516 | 0.510 | 215/308/95 |
+| vistas curb-cut — **parity** (A40) | **1024×1024** | 0.383 | **0.884** | **0.534** | **0.649** | 274/442/36 |
+
+At conf ≥ 0.30: control **0.419 / 0.694 / 0.522** (published: 0.419 / 0.697 / 0.524), parity
+**0.384 / 0.884 / 0.536**. Note the threshold barely bites at parity — it removes 3 false
+positives against 10 at 384 — because the higher-resolution masks are more confident.
+
+**The env control was not in the original plan, and it is what makes the parity delta
+attributable.** The published run was on Jon's RTX 3070 on an older `transformers`; makelab2 is a
+major version on. Since the `transformers` version is *not* in the detection signature — a hazard
+this document already flags — parity-vs-published would have differed in **two** things.
+Re-running 384 in the parity env separates them: it lands within **one detection out of 523** of
+the published run (215/308/95 vs 216/309/94, F1 0.516 vs 0.517). So the 4.x→5.15 jump is benign
+for this checkpoint, the residual is fp16 kernel nondeterminism rather than a version break, and
+**the whole parity delta is attributable to input size.** That also retires the "an upgrade could
+have changed every mask under an unchanged cache key" worry for this arm, as a measurement rather
+than an assurance.
+
+**The decision rule returns "stands", and it is not close.** Against the same-env control, parity
+moves F1 **0.516 → 0.534, +0.018** — about a third of the 0.05 bar. RampNet's lead goes 0.339 →
+**0.321**. One split, and no case for costing more.
+
+**But the mechanism underneath that flat F1 is the actual finding, and it is not the one the
+caveat predicted.** The handicap was real and it was large — it was just almost entirely a
+*recall* handicap:
+
+- **Recall 0.694 → 0.884 (+0.190).** Misses fall from 95 to **36**, a 62% reduction. At parity
+  this arm **out-recalls RampNet** (0.884 vs 0.768) while remaining a model that has never seen a
+  curb ramp label of ours.
+- **AP 0.510 → 0.649 (+0.139)**, a 27% relative gain — the ranking, not just the operating point,
+  is substantially better.
+- **Precision 0.411 → 0.383 (−0.028)**: slightly *worse*. False positives rise 308 → 442, faster
+  than true positives rise 215 → 274.
+
+So resolution was buying recall the whole time, and F1 stayed flat only because precision is the
+binding constraint and resolution does nothing for it. **That sharpens rather than softens the
+conclusion #126 was built to test.** The original framing — *the concept is findable, the
+discrimination is not* — was stated against OWLv2 and Grounding DINO; it now holds against the
+supervised arm at equal input too, and it is no longer confounded with how many pixels the model
+was given. Vistas' curb-cut labels **find** curb ramps on deployment panoramas better than our own
+model does; what they cannot do is tell a curb ramp from the things that look like one, which is
+exactly the failure the RampNet paper predicted when it rejected this class as a supervision
+source for being *"overly broad"*.
+
+**What this changes above.** Two claims in this section were stated at 384 and do not survive
+parity unqualified:
+
+- The AP comparison against the YOLO baseline said richmond AP **0.748** (`y11x_pano_h200`),
+  **0.724** (`y11l_pano`) and **0.536** (`y26_pano`) are *"all above 0.513"*. At parity the arm is
+  at **0.649**, so **`y26_pano` no longer clears it** — somebody else's labels for a neighbouring
+  class, at equal input, beat one of our own three YOLO arms on AP. The two stronger YOLO arms
+  still lead, so the sentence's conclusion holds; its arithmetic does not.
+- "Best *zero-training* model on AP" is unchanged and strengthened: 0.649 against OWLv2's 0.104.
+
+**Caveats that travel with these numbers.** `min_area_px=16` is inert at 384 but sits exactly at
+the smallest achievable blob at 1024 (see above), so the two rows do not share that setting's
+meaning even though they share its value. And this is still **richmond only**.
+
+**Neither A40 run's detections are published, so the bottom two rows of that table are not
+re-derivable from a clean clone — and as of 2026-09-17 the private cache they lived in cannot be
+found either.** The parity (1024×1024) and same-env control (384×384) detections were written to
+a private `--cache-dir` in a scratch worktree on makelab2 and nowhere else; only the published 384
+row and RampNet's row come from committed files. Looked for on 2026-09-17 before publishing them:
+the checkout at `/homes/gws/jonf/RampNet` has no worktree registered, its `.model_cache` holds no
+shard written 2026-08-17..20, and the parity run's shard names (`compare.cache_key` over the
+1024 signature, e.g. `f2acf1b1…`) are absent from `/homes/gws/jonf`, `/tmp`, `/var`, the root
+filesystem and the lab mounts. The cache went with the scratch worktree, so **the two A40
+rows, the 1024 complementarity column, the operating-point-correction table, both cascade tables,
+both `analysis_out/cascade_gate*.json` and the seam-exposure count now rest on the committed
+artifacts alone.** The cascade artifacts carry their full per-site input (`sites[]`), so every
+figure in those tables still re-derives from committed files (`tests/test_cascade_gate.py`);
+what cannot be re-derived is the partition itself, and the parity row's P/R/F1/AP.
+
+**Retiring that takes one decision and one 3m38s GPU run, not a rescue.** The run is cheap to
+repeat (`compare.py … --vistas-input-size 1024 1024`, one A40, timed above), and a repeat
+should land within a detection or so of the original — the control-vs-published pair did, across
+a `transformers` major version, with fp16 kernel nondeterminism the only residual. What is not
+settled is how it publishes, and the roster's own rules leave three
+things open rather than one:
+
+- `--vistas-input-size` does not change the arm's label, so the export needs a distinct published
+  name (`export_model_cache.py --publish-as`, #123) and a roster leg pinned on it. But
+  `rampnet/roster.py` requires that **once one leg of a model is qualified, every leg is** — so
+  the published 384 file would be renamed too (e.g. `mask2former-vistas-curb-cut-384`), which
+  touches every reference to it here and in `tests/`. The registry's pin naming also expects a
+  scalar (`str(value)` appears in the published name), and this pin is a 2-element size.
+- The same-env 384 control has the **same** signature and cache key as the published 384 run —
+  that was the point of it — so it cannot be a pinned leg at all; the roster has no notion of
+  "same detector, different host". Publishing it means either a new kind of entry or recording
+  it as an unregistered replicate beside the published file.
+- `export_model_cache.py` does not yet take `--vistas-input-size`, so it cannot address the 1024
+  cache entry even where one exists.
+
+Until those are decided, this section states the gap rather than guessing at the layout; the
+numbers above are as-run on 2026-08-18 and are not being changed. Each downstream table says so
+where it appears.
+
+#### Complementarity: 61% of RampNet's misses are recoverable, and a union still loses
+
+Parity raised the obvious recall-first question — this arm misses 36 ramps where RampNet misses
+72, so how much of *RampNet's* miss set does a free, zero-training model already cover? Run
+through the #35 gate (`scripts/analysis/complementarity.py`, generalized past its Gemini-only
+form for this), scoring-side only:
+
+| | vistas @384 (published) | **vistas @1024 (parity)** |
+|---|---:|---:|
+| found by BOTH | 194 | 220 |
+| rampnet ONLY | 44 | 18 |
+| **challenger ONLY** (rampnet-miss ∩ hit) | 22 | **54** |
+| found by NEITHER | 50 | **18** |
+| of rampnet's 72 misses, recovered | 22 (31%) | **54 (75%)** |
+| null on that subset (same boxes, wrong pano) | 0.090 | 0.143 |
+| **attributable after the null** | **~15** | **~44** |
+| oracle-union recall | 0.839 | **0.942** |
+| boxes/pano · above chance (`null_recall.py`) | 4.5 · 0.661 | 6.2 · **0.864** |
+
+**Which 384 run each column is.** The 384 column is the **published** arm, so both sides of it
+are committed: `benchmark/model_detections/mask2former-vistas-curb-cut__richmond.json` for the
+challenger and `benchmark/richmond/records.jsonl` for RampNet. One wrinkle in reading it back —
+`complementarity.py` and `null_recall.py` take their challenger detections from `.model_cache`
+rather than from the published export the way `fp_taxonomy.py` and `silent_witness.py` do, so on a
+clean clone the export has to be written into a cache directory first (*Reproducing it* below).
+`tests/test_complementarity.py` does that in a temporary directory and asserts this column cell
+for cell, so `pytest tests/test_complementarity.py` checks it with no setup at all.
+
+An earlier version of this column was the same-env A40 control rather than the published run. The
+two differ by one detection in 523, which moved two cells by one ramp each: challenger-only 22 →
+21 and found-by-nobody 50 → 51. Nothing turned on it, but the control is not published either, so
+the column as printed was not re-derivable and did not say which run it was.
+
+**The 1024 column cannot be re-derived from a clean clone, or from anywhere.** The parity
+detections are not published, and the private makelab2 cache that held them could not be found
+on 2026-09-17 (see the caveat under *Resolution parity*), so that column now rests on this
+table alone. Closing that needs a 3m38s re-run and a publishing decision, both spelled out there.
+
+**Discounted for chance, a free zero-training model finds ~44 of the 72 ramps RampNet misses —
+61%.** The null here is measured on the miss subset rather than extrapolated from the split-wide
+one, because RampNet's misses are a biased sample (far-field, adjacent pairs) and that is exactly
+where density differs; it lands at 0.143 against the split-wide 0.145, so in this case the
+extrapolation would have been fair. And the recall is real detection, not density: at 6.2
+boxes/pano the arm's **above-chance is 0.864, higher than RampNet's own 0.754** — nothing like
+OWLv2's 0.733 null at 74 boxes/pano.
+
+**The resolution fix mattered far more here than the headline suggested.** Parity moved F1 by
++0.018 and was correctly judged not to change the ranking — but it nearly **tripled** the
+attributable complementary gain (~15 → ~44 ramps) and shrank the found-by-nobody core from 50 to
+**18**, 5.8% of GT. That core is much smaller than paterson's 88 (22%) or gainesville's 55 (20%),
+though those are different splits against a different challenger, so read it as suggestive rather
+than a like-for-like. The general lesson is worth keeping: **a flat headline metric hid a large
+change in the structure underneath it**, and only the complementarity read surfaced it.
+
+**A naive union of the two loses to RampNet alone, and it is not close.** The oracle-union recall
+of 0.942 is a *ceiling* — it assumes a combiner that keeps every right call and discards every
+wrong one, which does not exist. What a real union pays is both FP bills:
+
+| | P | R | F1 |
+|---|---:|---:|---:|
+| rampnet alone | 0.964 | 0.768 | **0.855** |
+| naive union with vistas @1024 | 0.393 | 0.942 | 0.555 |
+| naive union with vistas @384 | 0.450 | 0.839 | 0.586 |
+
+The false-positive bill decides it: those 54 ramps arrive with 442 false positives, about
+**8.2 FPs per recovered ramp**, against the 9 FPs RampNet currently pays for 238 true
+positives. So ensembling by union is not a close call at any operating point on this arm's
+PR curve.
+
+**What that leaves is a gated cascade, and it is a real open question rather than a plan.** The
+useful form is not "take both models' boxes" but "use this arm's candidates as a *spatial prior*
+to locally relax RampNet's threshold", which would keep RampNet's precision and buy back some of
+the 54. Whether it can work is empirical and decidable: #131 measured RampNet's silent misses as
+8% absent / 62% adjacent-tail / 30% faint, so most misses *do* have sub-threshold heatmap signal —
+but nobody has checked whether that holds at these 54 locations specifically. If the signal is
+absent there, the miss is genuine and the cascade has nothing to work with. `silent_activation.py`
+is the instrument. **Not run, not costed here.**
+
+**Caveats.** richmond only, one imagery tier. The 442 FPs are not free even in a recall-first
+framing — at 3.6 FP/pano against RampNet's 0.07 they are a ~50× review burden, so "FPs are cheap"
+is a claim about the labeling workflow that would need its own justification at this ratio.
+
+#### The operating-point correction: a third of that gain is RampNet's own
+
+**Everything above scores RampNet from the committed bundle detections, which are the *shipped*
+operating point — on richmond every one of them is ≥ 0.5519. This document has recommended
+**0.30** since #54/#55 (PR #79).** For a complementarity read those are different models, and the
+difference decides who gets credit for a recovery.
+
+Checked before relying on it: `analysis_out/op_cache/richmond.json` filtered at ≥ 0.5519
+reproduces the published row **exactly** (P 0.9636 / R 0.7677 / F1 0.8546, 238/9/72), so it is the
+same source. At 0.30 those same peaks give **P 0.9018 / R 0.8290 / F1 0.8639, 257/28/53** —
+matching the committed `analysis_out/op/corrected_at_0.3.csv`. `complementarity.py
+--rampnet-op-threshold 0.30` re-bases the gate on it:
+
+| | rampnet @0.55 (published) | **rampnet @0.30 (recommended)** |
+|---|---:|---:|
+| rampnet recall | 0.768 (238) | **0.829 (257)** |
+| rampnet F1 | 0.855 | **0.864** |
+| rampnet misses | 72 | **53** |
+| challenger recovers | 54 (75%) | **38 (72%)** |
+| **attributable after the null** | ~44 | **~30** |
+| found by NEITHER | 18 | **15** |
+| oracle-union recall | 0.942 | 0.952 |
+| naive union F1 | 0.555 | 0.549 |
+
+**Both columns are the parity arm, so neither re-derives from a clean clone** — the challenger
+side is the unpublished 1024 cache (see *Resolution parity*). RampNet's side is committed on both
+columns: `benchmark/richmond/records.jsonl` at 0.55 and `analysis_out/op_cache/richmond.json` at
+0.30.
+
+**So 16 of the 54 ramps the challenger got credit for recovering are ramps RampNet already has at
+the operating point we recommend — the shipped threshold was discarding them.** That is 16 raw,
+**~14 after the chance null** (~44 → ~30). RampNet gains 19 hits going 0.55 → 0.30 (72 misses →
+53); 16 of the 19 come out of the challenger-recovered cell and the other 3 out of the
+found-by-nobody cell, which is why the headline falls by 16 and not by 19. The deployable
+complementary gain is **~30, not ~44**. The recovery *rate* barely moves (75% → 72%): the
+challenger is not preferentially finding the easy sub-threshold ones, there are simply fewer
+misses to find. And a naive union still loses against the stronger baseline (0.549 vs 0.864).
+
+#### The cascade gate: live, but the ceiling is ~19 ramps, not 54
+
+`scripts/analysis/cascade_gate.py` (new) asks the one question that decides whether a gated
+cascade is possible at all: **at the ramps the challenger recovers, does RampNet already produce
+something a prior could promote?** It partitions all 310 GT ramps into the four cells and reads
+RampNet's heatmap at each, reusing #46 Phase 1's instrument verbatim (`site_profile`,
+`null_percentile`, `nearest_peak`, `class_of`) so the numbers are comparable to that phase.
+Read pre-registered on #126 before running. Artifacts: `analysis_out/cascade_gate.json` (shipped
+point) and `analysis_out/cascade_gate_op030.json` (recommended point).
+
+**Both artifacts are committed and neither can be regenerated from a clean clone.** The four-cell
+partition inside them was computed against the parity detections, which are not published (see
+*Resolution parity*); the RampNet half — one forward per pano — needs the native-res panoramas
+from `projectsidewalk/rampnet-benchmark` and a GPU. What a clean clone *can* do is check each file
+against itself: `tests/test_cascade_gate.py` re-derives every `cells[]` figure from the same
+file's `sites` list, so a hand-copied number in the tables below fails the build.
+
+**What a regenerated copy of either artifact will differ in, so it is not mistaken for a changed
+result.** `cascade_gate.json` was written at `b7342dc`, before `4c192ca` added
+`rampnet_op_threshold` to the payload, so it lacks that key while `cascade_gate_op030.json` carries
+it. Since the round-1 review fixes (`d1860e5`, `cc2299e`) a re-run of *either* file also adds two
+payload keys, `null_rng: "per-site"` and `panos_without_floor_peaks`, and one per-site key,
+`nearest_peak_claimed`; and because the null is now seeded per site, every miss-cell `null_pct`,
+`null_med` and `null_p95` moves — by up to 0.075 on the committed files, see the caveat under *A
+negative worth recording* below — with `act`, `argmax_off_px`, `nearest_peak_px` and the cells
+unchanged. Both were also written before this branch took the #132 seam wrap (below). Two tests pin
+the *pre-fix* state and will need editing in the same commit as a regeneration:
+`test_only_the_op030_artifact_records_the_threshold_key` and
+`test_the_committed_nulls_came_from_one_stream_and_say_so`. The `newline=""` pinning is what makes
+that comparison a byte diff rather than a guess. Regenerate both when the parity detections are
+published.
+
+At **rampnet@0.30**, of the 38 genuinely-complementary ramps:
+
+| what RampNet has there | n | what it means |
+|---|---:|---|
+| floor peak in radius, **0.05–0.30** | **19** | **promotable** — a peak exists, below threshold. This is the cascade's real target. |
+| floor peak in radius, ≥0.30 but unmatched | 4 | the greedy matcher gave that peak to an **adjacent GT**. A matcher/σ problem (#130), not a threshold one. |
+| no floor peak in radius | 15 | nothing *of this ramp's* to promote — but not, for most of them, nothing nearby. `act` across these 15 is 0.272 median (0.369 mean); the nearest floor peak is a median **35.0 px** away (R = 22.5 px) and the in-window maximum sits on the window edge (median `argmax_off_px` **22.4**, 11 of 15 within 0.5 px of R). That is a neighbouring mode's shoulder reaching into the window, not mass the extractor overlooked. See the re-cut below. |
+
+The activation figure in the last row is the median over those **15 rows**, not over the 38-ramp
+cell. The cell's own median — `cells[].act_median` in `analysis_out/cascade_gate_op030.json` — is
+0.2152, and the 19 promotable rows sit lower still at 0.153. Three subsets, three medians, which
+is why the row says which one it is.
+
+**What the 15 "no peak in radius" sites are, from the artifact's own columns.** An earlier version
+of this table called them "unpeaked heatmap mass `peak_local_max` never called a maximum", and
+that reading is withdrawn: the committed `sites[]` rows, joined to `analysis_out/op_cache/richmond.json`,
+say the opposite for most of them. Re-cutting the 38 exhaustively, by where each site's nearest
+floor peak is and whether the greedy match at 0.30 already gave that peak to another GT on the
+pano (`cascade_gate.claimed_by_adjacent`, pinned in `tests/test_cascade_gate.py`):
+
+| the 38 recoverable ramps at rampnet@0.30 | n | mechanism |
+|---|---:|---|
+| floor peak in radius, 0.05–0.30 | **19** | promotable — the cascade's target, unchanged |
+| nearest floor peak ≥0.30 and **claimed by an adjacent GT** — 4 inside R, 7 at 1–2 R (26.8–44.1 px) | **11** | the #130 matcher/σ mechanism; the old "4" row and 7 of the old "15" row are one cause |
+| nearest floor peak at 1–2 R, unclaimed (23.5 px @0.643, 24.3 px @0.242, 32.2 px @0.358, 42.5 px @0.143) | 4 | a peak just outside the window; would need a wider radius (and, for the two below 0.30, a lower threshold as well) |
+| no floor peak within 2 R (51–117 px) | 4 | genuinely nothing near — and one of the 4 is the seam site below, where the heatmap *has* a peak the op_cache dropped |
+
+So the 15-row that read as "two-fifths of the recoverable set has no peak to raise" is 7 parts
+matching problem, 4 parts near-miss geometry and 4 parts absence; with the old 4-row folded in, the
+38 are 19 / 11 / 4 / 4. (The 4 "absence" sites also have a claimed nearest peak, just beyond 2 R —
+51–117 px away, scores 0.85 / 0.86 / 0.34 / 0.94 — which is why the 11 in the table are 4 + 7 and
+not 4 + 11.) `class_of`, the #46 Phase 1
+decomposition the pre-registration promised for comparability, puts the 15 at **12 `tail` / 3
+`faint_local` / 0 `absent`** (80 / 20 / 0%, against Phase 1's 62 / 30 / 8% over silent misses);
+over the whole 38-ramp cell it is 35 / 3 / 0, and the 15-ramp `neither` cell is 15 / 0 / 0. Both
+artifacts carry these in `cells[].classes`. The seam site is the one case of a different kind:
+`723487737079243` at x = 0.0069 has `act` 0.946 **7.4 px** from the ramp, centre 0.78, and no
+op_cache peak within 117 px — the `f4c71c8` seam dropout bounded abstractly further down, made
+concrete. A regenerated op_cache would almost certainly list that peak, which makes the site a
+RampNet **hit** at 0.30 and moves it out of `challenger_only` (38 → 37) rather than into any
+row of this table.
+
+**So the cascade is live and its ceiling is ~19 ramps on richmond — +6.1 recall points (0.829 →
+0.890) before any false-positive cost, which is unmeasured.** That is a real number and it is a
+long way below the 54 the raw complementarity suggested. Of the other 19, eleven are a matching
+problem (#130) that no threshold prior can reach — a σ or matcher change is what would act on
+them — four sit just outside the window, and four have nothing near them.
+
+**A negative worth recording: RampNet's own activation does not tell you which misses are
+recoverable.** `challenger_only` sits at null percentile **0.88** and the hard-core `neither` at
+**0.925** — the ramps *nobody* finds look, if anything, *stronger* on raw heatmap mass than the
+ones the challenger recovers (they contain 6 of 15 matcher-claimed peaks ≥0.30, which inflates
+it). Median argmax offset is 19.0 px inside a 22.5 px radius, i.e. near the window edge rather
+than on the ramp. So there is no cheap self-gating shortcut: you cannot skip the second model and
+find these by looking harder at RampNet's confidence. Against the pre-registered rule this is the
+**PARTIAL** branch — signal present, but not at the site — and the peak-level column, not the
+activation, is what supplies the bounded answer.
+
+One caveat on those two null percentiles, and on any per-site null read across the two
+artifacts. Both files were written with **one** random stream consumed in pano order over the
+miss-cell sites only, so a site's draw depended on which sites came before it; the miss set
+differs between the files (19 cell transitions), and of the 53 sites that carry a null in both,
+**43 differ, by up to 0.075**, while `act` and `nearest_peak_px` agree on every one
+(`test_the_committed_nulls_came_from_one_stream_and_say_so`). The within-file comparison above
+stands. `cascade_gate.py` now seeds per site (`site_rng`), so a regeneration will move individual
+`null_pct` values by that much, and the two medians slightly, for reasons that have nothing to do
+with the heatmap; that is a reason to regenerate both files together, not a change in the result.
+
+**What would have to be true for the cascade to pay.** Promoting sub-0.30 peaks gated on
+challenger candidates also promotes them wherever the challenger fires on a driveway and RampNet
+has a faint bump — and 442 of the challenger's 716 boxes are false positives. That cost is **not
+measured here**, so "+6.1 recall points" is a ceiling on the benefit with the cost still blank.
+The next step, if this is ever picked up, is to build the gate and score it, not to reason further
+about it.
+
+**Seam exposure: the two committed artifacts predate the #132 seam fixes, and the effect has now
+been measured rather than bounded.** This work branched at `5e20d11`, before `eccadda` (wrap the
+360° seam in the matcher) and `f4c71c8` (`peaks_to_dets` dropped peaks beside the seam) landed.
+The branch has since merged `main`, so both fixes are in it, and `complementarity.py`'s own
+`matched_gt` — which produces the four cells — now calls the shared wrapping matcher instead of
+re-deriving the distance inline. That matters here because `score_pano` supplies the false-positive
+counts and union P/R/F1 printed in the same tables and wraps by default, so the two halves of one
+output were on different matchers.
+
+The earlier version of this paragraph quoted `score_pano`'s docstring as saying wrapping *"moves
+no metric on any committed split"*. **That is half the sentence.** In full: *"Wrapping moves no
+RampNet or YOLO metric on any committed split — but it does move the challengers."* The challenger
+is the side being partitioned here, so the half that was dropped is the one that applies, and the
+right way to settle it is to measure rather than to cite.
+
+**Measured, on the cells themselves** (both / rampnet-only / challenger-only / neither), wrapping
+against not wrapping:
+
+| arm | RampNet's side | cells | wrapped |
+|---|---|---|---|
+| vistas 384 (published) | bundle, ≥ 0.5519 | 194 / 44 / 22 / 50 | identical |
+| vistas 384 (published) | op_cache ≥ 0.30 | 202 / 55 / 14 / 39 | identical |
+| vistas 384 (published) | op_cache ≥ 0.05 | 213 / 66 / 3 / 28 | identical |
+| gemini-3.1-pro-preview, paterson (#35 gate) | bundle | 188 / 83 / 36 / 88 | identical |
+| gemini-3.1-pro-preview, paterson (#35 gate) | op_cache ≥ 0.30 | 194 / 90 / 30 / 81 | identical |
+| gemini-3.1-pro-preview, paterson (#35 gate) | op_cache ≥ 0.05 | 201 / 98 / 23 / 73 | identical |
+
+**Zero cell flips, at every threshold, on both arms.** So the published 384 column and the
+committed #35 gate numbers are unchanged by the wrap. **The parity arm cannot be re-checked** —
+its detections are not published — so for the 1024 columns and both `cascade_gate*.json` the bound
+is still the count below rather than a measurement.
+
+One residual, unchanged by the merge: `analysis_out/op_cache/richmond.json` was last written at
+`c7098be` (2026-07-28), i.e. **before** `f4c71c8`, so it can still be missing peaks that sit
+beside the seam. That would make a site read "no floor peak in radius" when one exists — it can
+only *understate* the promotable count, never inflate it.
+
+**Measured exposure on the artifacts: 6 of richmond's 310 GT ramps straddle the seam, and only 1
+of them is in `challenger_only`** (the other 5 are in `both`, where neither fix can move the
+partition in a direction that matters). So the worst case for the ~19-ramp ceiling is one ramp in
+38, and no conclusion here turns on it. That one ramp is now identified rather than bounded:
+`723487737079243` at x = 0.0069 (see the re-cut of the 38 above) has a 0.946 heatmap peak 7.4 px
+from the ramp and no op_cache peak within 117 px, so on a regenerated op_cache it is a RampNet hit
+at 0.30 and leaves `challenger_only` (38 → 37) — the ceiling of 19 does not move. Regenerating
+both artifacts once the parity detections are published is the clean way to retire that bound.
+
+##### Reproducing it
+
+The scored runs. These need a GPU and the native-resolution panoramas
+(`projectsidewalk/rampnet-benchmark`), and they are what produced the parity table:
+
+```bash
+# parity (the measurement)
+python scripts/model_comparison/compare.py benchmark/richmond \
+    --models rampnet,vistas:curb-cut --vistas-input-size 1024 1024
+
+# same-env control (the attribution) -- default 384, no override. It has the SAME
+# signature and cache key as the published 384 run, so on a clone that has written
+# the published detections into .model_cache (below) compare.py would find all 124
+# panos cached and never run the model: --no-cache (or a fresh --cache-dir) is what
+# makes this an actual re-inference rather than a re-score of the published arm.
+python scripts/model_comparison/compare.py benchmark/richmond --models vistas:curb-cut     --no-cache
+
+# either, re-scored at the deployment threshold (free, reads the cache)
+python scripts/model_comparison/compare.py benchmark/richmond \
+    --models vistas:curb-cut --vistas-input-size 1024 1024 --op-threshold 0.30
+```
+
+The complementarity, null and cascade reads. Every one of these re-scores detections that are
+already cached, so they cost nothing; only `cascade_gate.py` needs a GPU, because it runs
+RampNet. `--vistas-input-size` is part of the cache key, so it is also what selects which run
+is being read: drop it to read the **published 384** arm instead of the parity arm.
+
+```bash
+# the four cells at parity, with the chance null measured on the miss subset
+python scripts/analysis/complementarity.py vistas:curb-cut richmond \
+    --vistas-input-size 1024 1024
+
+# the same, re-based on the operating point this document recommends
+python scripts/analysis/complementarity.py vistas:curb-cut richmond \
+    --vistas-input-size 1024 1024 --rampnet-op-threshold 0.30
+
+# density vs detection: boxes/pano, the shifted-pano null, above-chance
+python scripts/analysis/null_recall.py benchmark/richmond \
+    --models rampnet,vistas:curb-cut --vistas-input-size 1024 1024
+
+# the cascade gate, at the shipped point and at the recommended one. Needs a GPU
+# and the native-res panos; --panos-root is the checkout that holds them.
+python scripts/analysis/cascade_gate.py --panos-root /path/to/RampNet \
+    --model vistas:curb-cut --vistas-input-size 1024 1024 \
+    --json-out analysis_out/cascade_gate.json
+python scripts/analysis/cascade_gate.py --panos-root /path/to/RampNet \
+    --model vistas:curb-cut --vistas-input-size 1024 1024 \
+    --rampnet-op-threshold 0.30 --json-out analysis_out/cascade_gate_op030.json
+```
+
+`complementarity.py` and `null_recall.py` read `--cache-dir` (default `.model_cache`), which is
+git-ignored, so on a clean clone the published detections have to be written into one first.
+`benchmark/model_detections/<model>__<split>.json` records the signature they were cached under
+and `compare.cache_key(model, signature, city, pano_id)` is the shard name;
+`tests/test_complementarity.py` does the whole thing in eight lines and is the shortest working
+example. For the **parity** arm there is nothing to write — those detections are not published,
+and the private cache that held them is gone (see *Resolution parity*) — so the 1024 commands
+above need the `compare.py --vistas-input-size 1024 1024` run first, which regenerates them.
+
+**Cost, in both units.** Money: **$0** — makelab2 is lab-owned hardware with no metered
+billing, and every read above is free. Time: one leg of three was timed, and **the full 124-pano
+parity run takes 3m38s** on one A40. That was measured rather than guessed because the estimate
+going in was 3–4× and it was wrong in the cheap direction: the GPU forward goes 0.078 s →
+0.092 s per view, only **1.17×**. Swin's windowed attention scales far better than
+pixel count, and the documented "2.3 s/view" is dominated by reprojection and CPU work, not the
+encoder. Peak GPU memory is 1.64 GB. The same-env 384 control was **not timed** (the same 124
+panos at 1/7 the pixel area, so it is the cheaper of the two), and RampNet's row in that table
+costs no GPU at all — `--models rampnet` scores the bundle's committed detections without loading
+a model. **So there is no session total to quote; what is recorded is 3m38s for the one leg that
+was measured.** Verified before the run rather than assumed: the override
+reaches the model — `pixel_values` (1, 3, 384, 384) → (1, 3, 1024, 1024) and mask logits
+(1, 100, 96, 96) → (1, 100, 256, 256) — which a silently no-opping `processor.size` assignment on
+a new major version would not have done, and which would have made "parity" a second 384 run
+under a different cache key.
+
 Two mechanisms, both measured rather than assumed:
 
 - **The `curb-cut+curb` union is a clean negative result.** It was run to test whether recall
diff --git a/scripts/analysis/README.md b/scripts/analysis/README.md
index 2ca0526d..f21f0e4c 100644
--- a/scripts/analysis/README.md
+++ b/scripts/analysis/README.md
@@ -26,7 +26,7 @@ checkout.
 | script | GPU | what it answers |
 |---|---|---|
 | `miss_analysis.py` | no | Are misses localization near-misses or blind? Are they hard (a VLM also missed) or RampNet-specific? |
-| `complementarity.py [model] [split]` | no | Oracle-union recall + the RampNet-miss ∩ VLM-hit set (issue #35 gate). Reads cached VLM detections from `.model_cache`; split defaults to richmond. |
+| `complementarity.py [model] [split]` | no | Oracle-union recall + the RampNet-miss ∩ challenger-hit set, with a chance null on that subset and the FP bill a naive union would actually pay (issue #35 gate). Takes any model spec (`provider` or `provider:model_id`); a bare non-provider token is read as a Gemini model id. Reads cached detections from `.model_cache`; split defaults to richmond. Pass the same `--vistas-input-size` the run used — it is part of the cache key. |
 | `precision_by_distance.py` | no | Is precision worse at distance — i.e. is culling far detections worth it? (No.) |
 | `threshold_sweep.py` | **yes** | Re-runs inference on all benchmark panos and sweeps `threshold_abs` × `min_distance`. |
 | `peak_nms_check.py` | no | Would suppressing peaks closer than the match radius help? (No — 6 of the 10 within-R pairs in the reviewed records are real ramp pairs; issue #62.) Reads all seven splits' committed records, no panos needed. |
@@ -35,6 +35,7 @@ checkout.
 | `depth_analysis.py` | no | Recall vs true distance / apparent size + the resolution forecast. Needs `gt_depth_da3.json`. |
 | `farfield_forensics.py` | no | Is the far-field `visible` verdict deck a representative sample, and does apparent size actually separate a far-field hit from a far-field silent miss? (#46 Phase 0.) Committed caches, witness list, gallery verdicts and imagery manifests only. |
 | `silent_activation.py` | **yes** | Is a `silent` miss attenuated or absent? Reads the heatmap inside each silent miss's match window plus a per-pano azimuth null (#46 Phase 1). Needs the native-res `panos/` (HF `projectsidewalk/rampnet-benchmark`); `--panos-root` points at the checkout holding them. |
+| `cascade_gate.py` | **yes** | Is a gated cascade possible? Partitions every GT ramp into `complementarity.py`'s four cells, then reads RampNet's heatmap at each with #46 Phase 1's instrument, so the question "does RampNet already produce something a prior could promote at the ramps the challenger recovers?" gets a bounded answer (#126). Floor peaks come from `analysis_out/op_cache/<split>.json`, not the bundle records. Needs the native-res `panos/` and the challenger already cached — pass the same `--vistas-input-size` the run used. |
 | `size_analysis.py` | no | Geometry-only size stratification (no depth model) + the hard-miss montage figure. |
 | `overlap_test.py` | **yes** | Do the threshold and resolution levers target the same ramps? Needs `gt_depth_da3.json`. |
 | `operating_point_curve.py extract` | **yes** | Inference once → all peaks down to a low score floor → per-pano cache (issue #54). Handles both bundle kinds, so `manual_gold` (independent YOLO GT, no verdict review) is covered too. `--tta` extracts the horizontal-flip-TTA arm instead (#78) — two passes per pano, mirrored heatmap un-flipped and maxed exactly as `stage_two/evaluate.py`; each arm must live in its own `--cache` dir (mixing is refused). |
diff --git a/scripts/analysis/cascade_gate.py b/scripts/analysis/cascade_gate.py
new file mode 100644
index 00000000..103365e2
--- /dev/null
+++ b/scripts/analysis/cascade_gate.py
@@ -0,0 +1,465 @@
+"""Is a gated cascade live? Does RampNet already *see* the ramps a challenger recovers? (#126)
+
+The complementarity gate (``complementarity.py``) says a Vistas-supervised segmenter at
+input parity finds 54 of the 72 richmond ramps RampNet misses, ~44 of them after the
+chance null. A naive union of the two still loses — those 54 arrive with 442 false
+positives, ~8.2 FP per recovered ramp, and the union scores F1 0.555 against RampNet's
+0.855.
+
+What that leaves is a **gated cascade**: use the challenger's candidates as a spatial
+prior and locally relax RampNet's threshold, keeping RampNet's precision everywhere
+else. That has one precondition, and it is measurable without building anything —
+**RampNet must already produce sub-threshold response at those ramps.** If the heatmap
+is flat there, the miss is genuine absence and no prior can raise what is not there.
+
+So: partition every GT ramp into the four complementarity cells, and read RampNet's
+heatmap at each.
+
+    cell                rampnet  challenger   role
+    both                hit      hit          positive control
+    rampnet_only        hit      miss         positive control
+    challenger_only     MISS     hit          the recoverable set -- the question
+    neither             MISS     miss         hard core
+
+**The instrument is #46 Phase 1's, imported rather than reimplemented** —
+``site_profile``, ``null_percentile``, ``nearest_peak``, ``class_of`` and its two
+cutoffs (``ABSENT_MAX`` 0.01, ``PEAK_FLOOR`` 0.05) all come from
+``silent_activation.py``. That is deliberate: it makes these numbers directly
+comparable to that phase's 8% absent / 62% adjacent-tail / 30% faint decomposition,
+and it means a fix to the probe fixes both analyses.
+
+**``class_of`` is imported for comparability, but the column the read turns on here is
+``peak_in_radius``, not the class.** #46 Phase 1 applied those cutoffs to *silent*
+misses — defined as no floor peak within the radius — so there ``tail`` (act >= 0.05)
+could only mean an outside mode reaching in. This population is every RampNet miss, not
+just the silent ones, so ``act >= 0.05`` here has two very different causes and the
+class alone cannot separate them:
+
+* **a floor peak inside the radius** — the model localized the ramp and the detection
+  was lost *downstream*, either to the shipped threshold or to the greedy matcher giving
+  that peak to an adjacent GT. Recoverable, and recoverable **without a second model**.
+* **no floor peak inside the radius** — a threshold prior has nothing *of this ramp's*
+  to promote, because promotion operates on peaks. ``act`` there is usually **not**
+  unpeaked mass: on the committed richmond run the nearest floor peak sits a median
+  35 px away (1-2 R) and the in-window maximum sits on the window edge (median
+  ``argmax_off_px`` 22.4 of 22.5), i.e. a neighbouring mode's shoulder reaching in —
+  #46 Phase 1's ``tail``, and that peak is mostly one the matcher already gave to an
+  adjacent GT (#130). ``no_peak_profile`` reports this per cell so the row is read as
+  what it is. Genuinely peakless mass — no floor peak within 2 R — is the minority
+  (4 of 15 on richmond), and one of those 4 is a peak the op_cache dropped beside the
+  seam (pre-``f4c71c8``), not a peak the model never made.
+
+``peak_in_radius`` is therefore reported per cell and is what the read below turns on.
+
+**Sub-threshold signal is necessary, not sufficient.** A positive here says the cascade
+is not ruled out and is worth costing; it does not demonstrate one works. Any realisable
+gain is bounded above by the ~44 attributable ramps, not the raw 54.
+
+**The floor peaks come from ``analysis_out/op_cache/<split>.json``, NOT from the
+bundle records.** The bundle's committed detections are the published operating point
+— on richmond every one of them scores >= 0.5519 — so asking "is there a peak near this
+missed ramp?" of *those* answers a different question and makes every miss look
+peakless. The op_cache holds ``peak_local_max`` output down to the 0.05 floor, which is
+what "did the model say anything here, below the threshold we ship?" actually needs.
+The greedy match that DEFINED the miss still uses the bundle records, because that is
+what produced the published 238/9/72 — the two are deliberately different inputs to
+two different questions.
+
+Inputs: the native-resolution panoramas at ``benchmark/<split>/panos/`` (git-ignored,
+published as ``projectsidewalk/rampnet-benchmark``) — ``--panos-root`` points at
+whichever checkout holds them, since a worktree will not. The challenger's detections
+must already be in ``--cache-dir``; **pass the same ``--vistas-input-size`` the run
+used, it is part of the cache key.** A GPU: ~124 panos, one forward each.
+
+    python scripts/analysis/cascade_gate.py --panos-root /path/to/RampNet \\
+        --model vistas:curb-cut --vistas-input-size 1024 1024 \\
+        --json-out analysis_out/cascade_gate.json
+"""
+import argparse
+import json
+import os
+import random
+import sys
+
+REPO = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+OUT = os.environ.get("RAMPNET_ANALYSIS_OUT", os.path.join(REPO, "analysis_out"))
+sys.path.insert(0, REPO)
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+sys.path.insert(0, os.path.join(REPO, "scripts", "model_comparison"))
+
+from rampnet.detection_eval import (                                   # noqa: E402
+    radius_sq_for, PANO_SCALE_X, PANO_SCALE_Y)
+from rampnet.metrics import greedy_match                               # noqa: E402
+from compare import load_bundle, DetectionCache, cache_key             # noqa: E402
+from detectors import build_detector                                   # noqa: E402
+from complementarity import (                                          # noqa: E402
+    CELLS, cell_of, compare_args, floor_gap_warning, load_floor_peaks, model_spec,
+    partition_cells)
+from silent_activation import (                                        # noqa: E402
+    NULL_SEED, NULL_TRIALS, class_of, nearest_peak, null_percentile, seam_of,
+    site_profile)
+from farfield_forensics import quartiles                               # noqa: E402
+
+#: Cells where RampNet did NOT find the ramp -- the only ones a null is meaningful for.
+MISS_CELLS = ("challenger_only", "neither")
+
+
+def site_rng(pid, x, y):
+    """One RNG per site, seeded from ``NULL_SEED`` and the site's identity.
+
+    A single stream consumed in pano order made a site's ``null_pct`` depend on which
+    sites preceded it: the miss set differs between the shipped-point and the 0.30
+    artifacts (19 cell transitions), so the same site drew different azimuths in
+    each — 43 of the 53 sites carrying a null in both files differed, by up to 0.075,
+    with ``act`` and ``nearest_peak_px`` identical on all of them. Seeding per site
+    makes a site's null a function of its heatmap and nothing else, so a per-site
+    comparison across runs measures the heatmap rather than the neighbours.
+    """
+    return random.Random(f"{NULL_SEED}:{pid}:{x!r}:{y!r}")
+
+
+def panos_without_floor(panos, floor_peaks):
+    """Panos the op_cache does not list, in the order given.
+
+    On the probe path a missing pano reads ``peak_in_radius: false`` and
+    ``nearest_peak_px: null`` for every site on it while the header still says the
+    floor came from the op_cache — the same silent shift ``partition_cells`` counts
+    under ``--rampnet-op-threshold``, on the path that did not warn.
+    """
+    return [pid for pid in panos if pid not in floor_peaks]
+
+
+def nearest_peak_index(preds, x, y):
+    """Index into ``preds`` of the closest floor peak (``nearest_peak``'s geometry)."""
+    best, best_i = float("inf"), None
+    for i, p in enumerate(preds):
+        dx = abs(p[0] - x) * PANO_SCALE_X
+        dx = min(dx, PANO_SCALE_X - dx)
+        d = (dx * dx + ((p[1] - y) * PANO_SCALE_Y) ** 2) ** 0.5
+        if d < best:
+            best, best_i = d, i
+    return best_i
+
+
+def claimed_by_adjacent(pano_sites, preds, threshold, radius_sq):
+    """For each site, is its nearest floor peak one the matcher gave to a DIFFERENT GT?
+
+    Re-runs the greedy match that defines RampNet's hits at ``threshold`` (highest
+    score first, wrapped, as ``matched_gt``) over all GT on the pano, then asks of
+    each site's nearest floor peak whether it was assigned to some other GT. That is
+    the #130 mechanism — a neighbouring ramp's detection sitting 1-2 R from this one
+    — and it is what most of the "no floor peak in radius" rows turn out to be.
+    Returns one bool per site; ``False`` when there is no peak at all.
+    """
+    gt_points = [(s["x"], s["y"]) for s in pano_sites]
+    kept = [i for i, p in enumerate(preds) if p[2] >= threshold]
+    order = sorted(kept, key=lambda i: preds[i][2], reverse=True)
+    assignments = greedy_match([(preds[i][0], preds[i][1]) for i in order], gt_points,
+                               radius_sq, PANO_SCALE_X, PANO_SCALE_Y, wrap_x=True)
+    claimed = {order[k]: gi for k, (gi, _) in enumerate(assignments) if gi >= 0}
+    out = []
+    for si, s in enumerate(pano_sites):
+        ni = nearest_peak_index(preds, s["x"], s["y"])
+        out.append(ni is not None and ni in claimed and claimed[ni] != si)
+    return out
+
+
+def no_peak_profile(rows, cell, radius_px):
+    """Where the nearest floor peak actually is, for the rows of ``cell`` that have
+    none inside the radius.
+
+    The cell summary's medians are over the whole cell; the table row in
+    ``docs/model_comparison.md`` is about this subset, and what it says the subset
+    *is* turns on these columns: a nearest peak 1-2 R away with the in-window
+    maximum on the window edge is a neighbour's shoulder (``tail``), not mass the
+    extractor overlooked. ``argmax_on_edge`` counts rows whose maximum is within
+    0.5 px of the radius; ``claimed`` counts rows whose nearest peak the matcher gave
+    to another GT (only present on rows that carry ``nearest_peak_claimed``, i.e.
+    runs since this key was added).
+    """
+    sel = [r for r in rows if r["cell"] == cell and not r["peak_in_radius"]]
+    if not sel:
+        return {"cell": cell, "n": 0}
+    near = [r["nearest_peak_px"] for r in sel if r["nearest_peak_px"] is not None]
+    out = {
+        "cell": cell,
+        "n": len(sel),
+        "act_median": round(quartiles([r["act"] for r in sel])[1], 4),
+        "argmax_off_px_median": round(quartiles([r["argmax_off_px"] for r in sel])[1], 1),
+        "argmax_on_edge": sum(1 for r in sel if r["argmax_off_px"] >= radius_px - 0.5),
+        "nearest_peak_px_median": round(quartiles(near)[1], 1) if near else None,
+        "peak_within_2r": sum(1 for d in near if d <= 2 * radius_px),
+        "peak_beyond_2r": len(sel) - sum(1 for d in near if d <= 2 * radius_px),
+        "classes": {c: sum(1 for r in sel if r["class"] == c)
+                    for c in ("absent", "faint_local", "tail")},
+        "seam": sum(1 for r in sel if r["seam"]),
+    }
+    if all("nearest_peak_claimed" in r for r in sel):
+        out["claimed"] = sum(1 for r in sel if r["nearest_peak_claimed"])
+    return out
+
+
+def summarize(rows, cell):
+    """Per-cell summary. ``None`` for null stats on the hit cells, which have none."""
+    sel = [r for r in rows if r["cell"] == cell]
+    if not sel:
+        return {"cell": cell, "n": 0}
+    acts = [r["act"] for r in sel]
+    classes = {c: sum(1 for r in sel if r["class"] == c)
+               for c in ("absent", "faint_local", "tail")}
+    out = {
+        "cell": cell,
+        "n": len(sel),
+        "act_median": round(quartiles(acts)[1], 4),
+        "center_median": round(quartiles([r["center"] for r in sel])[1], 4),
+        "argmax_off_px_median": round(quartiles([r["argmax_off_px"] for r in sel])[1], 1),
+        "nearest_peak_px_median": round(
+            quartiles([r["nearest_peak_px"] for r in sel
+                       if r["nearest_peak_px"] is not None])[1], 1)
+        if any(r["nearest_peak_px"] is not None for r in sel) else None,
+        "classes": classes,
+        "class_share": {c: round(v / len(sel), 3) for c, v in classes.items()},
+        "seam": sum(1 for r in sel if r["seam"]),
+    }
+    inr = [r for r in sel if r["peak_in_radius"]]
+    out["peak_in_radius"] = len(inr)
+    out["peak_in_radius_share"] = round(len(inr) / len(sel), 3)
+    if inr:
+        out["peak_in_radius_score_median"] = round(
+            quartiles([r["nearest_peak_score"] for r in inr])[1], 4)
+    nulls = [r["null_pct"] for r in sel if r["null_pct"] is not None]
+    if nulls:
+        out["null_pct_median"] = round(quartiles(nulls)[1], 3)
+        out["above_null_p95"] = sum(1 for r in sel
+                                    if r["null_pct"] is not None and r["act"] > r["null_p95"])
+        out["null_med_median"] = round(
+            quartiles([r["null_med"] for r in sel if r["null_med"] is not None])[1], 4)
+    return out
+
+
+def main(argv=None):
+    p = argparse.ArgumentParser(description=__doc__.split("\n")[0],
+                                formatter_class=argparse.RawDescriptionHelpFormatter)
+    p.add_argument("--split", default="richmond")
+    p.add_argument("--model", default="vistas:curb-cut",
+                   help="Challenger model spec, as compare.py --models takes it.")
+    p.add_argument("--panos-root", default=REPO,
+                   help="Checkout holding benchmark/<split>/panos/ (a worktree will not).")
+    p.add_argument("--cache-dir", default=os.path.join(REPO, ".model_cache"))
+    p.add_argument("--rampnet-op-threshold", type=float, default=None,
+                   help="Define RampNet's hits from op_cache floor peaks at this "
+                        "threshold instead of the bundle's shipped detections "
+                        "(>=0.5519 on richmond). This document recommends 0.30, and "
+                        "the cells move: 19 of the shipped point's misses are ramps "
+                        "RampNet already has, 16 of them from challenger_only. "
+                        "Default: the bundle, as published.")
+    p.add_argument("--radius", type=float, default=0.022)
+    p.add_argument("--tiling", choices=["perspective", "none"], default="perspective")
+    p.add_argument("--vistas-input-size", type=int, nargs=2, metavar=("H", "W"), default=None,
+                   help="Must match the run being analysed -- it is part of the cache key.")
+    p.add_argument("--vistas-revision", default=None)
+    p.add_argument("--limit", type=int, default=None, help="Smoke test: first N panos.")
+    p.add_argument("--json-out", default=None)
+    args = p.parse_args(argv)
+
+    if args.json_out and args.limit:
+        p.error("--limit truncates the run; refusing to write it to --json-out")
+
+    import torch
+    import threshold_sweep as ts
+    from miss_gallery import pano_path
+
+    bundle = os.path.join(REPO, "benchmark", args.split)
+    records, verdicts, _ = load_bundle(bundle)
+    if verdicts is None:
+        sys.exit(f"{bundle}: no verdicts.json -- this needs a reviewed split.")
+    provider, model_id = model_spec(args.model)
+    label, detector = build_detector(provider, model_id, records, compare_args(args))
+    sig = detector.signature()
+    cache = DetectionCache(args.cache_dir)
+    radius_sq = radius_sq_for(args.radius)
+
+    # Floor peaks (>= 0.05). Used for the sub-threshold probe always, and to
+    # DEFINE rampnet's hits when --rampnet-op-threshold is given.
+    floor_peaks, floor_src, have_op_cache = {}, "op_cache", True
+    try:
+        floor_peaks = load_floor_peaks(args.split)
+    except (OSError, ValueError, KeyError):
+        have_op_cache = False
+        floor_src = ("MISSING (fell back to bundle records -- distances are to the "
+                     "shipped operating point, not the 0.05 floor)")
+        if args.rampnet_op_threshold is not None:
+            sys.exit("--rampnet-op-threshold needs analysis_out/op_cache/"
+                     f"{args.split}.json, which could not be read.")
+
+    # ---- partition every GT ramp into a complementarity cell ------------------
+    # The same loop complementarity.py's table comes from, so the two cannot drift.
+    part = partition_cells(
+        records, verdicts,
+        lambda pid: cache.get(cache_key(label, sig, args.split, pid)),
+        radius_sq, args.rampnet_op_threshold,
+        floor_peaks if args.rampnet_op_threshold is not None else None)
+    sites, missing = part.sites, part.missing
+    if missing:
+        print(f"WARNING: {missing} panos had no cached {label} detections and were "
+              f"skipped. Pass the --vistas-input-size the run used.", flush=True)
+    warning = floor_gap_warning(part.no_floor, args.split)
+    if warning:
+        print(warning, flush=True)
+    if not sites:
+        sys.exit("No sites -- is the challenger cached for this split/input size?")
+
+    by_pano = {}
+    for s in sites:
+        by_pano.setdefault(s["pano"], []).append(s)
+    panos = sorted(by_pano)
+    if args.limit:
+        panos = panos[:args.limit]
+    # The probe path reads the op_cache for every pano regardless of the threshold
+    # flag, so a pano it does not list is a gap here too -- and until now this path
+    # said nothing about it.
+    no_floor_probe = panos_without_floor(panos, floor_peaks) if have_op_cache else []
+    if no_floor_probe:
+        print(f"WARNING: {len(no_floor_probe)} pano(s) are absent from analysis_out/"
+              f"op_cache/{args.split}.json, so every site on them reads 'no floor peak' "
+              f"(nearest_peak_px null) whatever the heatmap says. Regenerate the "
+              f"op_cache for this split before reading the peak columns.", flush=True)
+
+    counts = {c: sum(1 for s in sites if s["cell"] == c) for c in CELLS}
+    rn = ("rampnet" if args.rampnet_op_threshold is None
+          else f"rampnet@{args.rampnet_op_threshold:g}")
+    print(f"=== Cascade gate: {rn} heatmap at {label}'s recoveries "
+          f"({args.split}, {len(sites)} GT ramps in {len(by_pano)} panos) ===")
+    print("    cells: " + "  ".join(f"{c}={counts[c]}" for c in CELLS), flush=True)
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = ts.load_model().to(device)
+    print(f"    device={device} model=projectsidewalk/rampnet-model "
+          f"(single-pass fp32, as op_cache)", flush=True)
+    print(f"    floor peaks (>=0.05) from: {floor_src}", flush=True)
+    print(f"    rampnet hits defined by: "
+          + ("bundle records (shipped point)" if args.rampnet_op_threshold is None
+             else f"op_cache >= {args.rampnet_op_threshold:g}"), flush=True)
+
+    # The threshold that defines RampNet's hits, for the "did the matcher give this
+    # site's nearest peak to a neighbour" column. At the shipped point that is the
+    # bundle's floor, 0.5519 on richmond, which is the lowest committed score.
+    claim_threshold = (args.rampnet_op_threshold if args.rampnet_op_threshold is not None
+                       else min((d["confidence"] for r in records.values()
+                                 for d in r["detections"]), default=0.0))
+    r_px = radius_sq ** 0.5
+    rows, skipped = [], 0
+    for i, pid in enumerate(panos, 1):
+        path = pano_path(args.split, pid, args.panos_root)
+        if not os.path.exists(path):
+            skipped += len(by_pano[pid])
+            continue
+        heat = ts.heatmap_for(model, device, path, use_fp16=False)
+        # One source per run, not per pano. `floor_peaks.get(pid) or <bundle>` would
+        # substitute the shipped detections for any pano the op_cache lists with zero
+        # floor peaks, while the header still says the floor came from op_cache -- and
+        # a pano with no peak at the 0.05 floor genuinely has nothing to promote, which
+        # is the answer, not a gap to fill.
+        preds = (floor_peaks.get(pid, []) if have_op_cache else
+                 [(d["x_normalized"], d["y_normalized"], d["confidence"])
+                  for d in records[pid]["detections"]])
+        claimed = claimed_by_adjacent(by_pano[pid], preds, claim_threshold, radius_sq)
+        for s, is_claimed in zip(by_pano[pid], claimed):
+            act, off_px, center = site_profile(heat, s["x"], s["y"], radius_sq)
+            npx, nscore = nearest_peak(preds, s["x"], s["y"])
+            row = {**s, "act": round(act, 6), "center": round(center, 6),
+                   "argmax_off_px": round(off_px, 1),
+                   "nearest_peak_px": None if npx == float("inf") else round(npx, 1),
+                   "nearest_peak_score": nscore,
+                   # A floor peak inside the match radius on a MISSED ramp means the
+                   # model did localize it and the detection was lost downstream --
+                   # either to the shipped threshold, or to the greedy matcher giving
+                   # the peak to an adjacent GT. That is recoverable without a second
+                   # model; a peak outside the radius is not.
+                   "peak_in_radius": bool(npx < r_px),
+                   # ...and whether that nearest peak is one the matcher already handed
+                   # to a different GT on this pano (#130), which is what most of the
+                   # "no peak in radius" rows are.
+                   "nearest_peak_claimed": bool(is_claimed),
+                   "class": class_of(act), "seam": seam_of(s["x"], radius_sq),
+                   "null_pct": None, "null_med": None, "null_p95": None}
+            # The null is only meaningful where rampnet did NOT find the ramp; the
+            # hit cells are high by construction and are here as a positive control.
+            # Seeded per site (site_rng), so the draw does not depend on which sites
+            # came before it -- the committed artifacts predate this and were written
+            # from one stream, so a regeneration moves individual null_pct values by
+            # up to 0.075 without any change in the heatmap.
+            if s["cell"] in MISS_CELLS:
+                a, pct, med, p95 = null_percentile(heat, s["x"], s["y"],
+                                                   site_rng(pid, s["x"], s["y"]),
+                                                   radius_sq=radius_sq)
+                row.update(null_pct=round(pct, 4), null_med=round(med, 6),
+                           null_p95=round(p95, 6))
+            rows.append(row)
+        if i % 20 == 0 or i == len(panos):
+            print(f"    {i}/{len(panos)} panos", flush=True)
+    if skipped:
+        print(f"WARNING: {skipped} sites skipped -- pano jpg not found under "
+              f"--panos-root {args.panos_root}", flush=True)
+
+    summaries = [summarize(rows, c) for c in CELLS]
+    print()
+    hdr = (f"{'cell':17s} {'n':>4s} {'act med':>8s} {'centre':>8s} {'argmax off':>11s} "
+           f"{'floor peak in R':>16s} {'its score':>10s} {'null pct':>9s}")
+    print(hdr)
+    print("-" * len(hdr))
+    for s in summaries:
+        if not s["n"]:
+            continue
+        miss = s["cell"] in MISS_CELLS
+        print(f"{s['cell']:17s} {s['n']:4d} {s['act_median']:8.4f} "
+              f"{s['center_median']:8.4f} {s['argmax_off_px_median']:10.1f}p "
+              f"{s['peak_in_radius']:9d} ({s['peak_in_radius_share']:3.0%}) "
+              + (f"{s.get('peak_in_radius_score_median', float('nan')):10.3f}"
+                 if s.get("peak_in_radius") else f"{'—':>10s}")
+              + (f"{s['null_pct_median']:9.3f}" if miss else f"{'—':>9s}"))
+    print()
+    print("  The hit cells are the positive control: a matched detection is inside the")
+    print("  radius by definition, so their act/centre ~0.85+ and 100% peak-in-R are what")
+    print("  a working probe MUST show, not a finding.")
+    print()
+    print("  For the two MISS cells, 'floor peak in R' is the whole question:")
+    print("   - peak inside R  -> the model DID localize the ramp and the detection was")
+    print("     lost downstream, to the shipped threshold or to the greedy matcher handing")
+    print("     the peak to an adjacent GT. Recoverable WITHOUT a second model.")
+    print("   - peak outside R -> nothing of THIS ramp's to promote. 'act' there is usually")
+    print("     a neighbouring peak's shoulder reaching into the window (nearest peak 1-2 R")
+    print("     away, argmax on the window edge), not mass the extractor overlooked -- the")
+    print("     profile below says which, per cell.")
+    print()
+    print(f"  no floor peak in R, by where the nearest peak is (R = {r_px:.1f} px):")
+    for cell in MISS_CELLS:
+        prof = no_peak_profile(rows, cell, r_px)
+        if not prof["n"]:
+            continue
+        print(f"    {cell:16s} n={prof['n']:<3d} nearest peak median "
+              f"{prof['nearest_peak_px_median']} px; within 2R {prof['peak_within_2r']}, "
+              f"beyond {prof['peak_beyond_2r']}; argmax on edge {prof['argmax_on_edge']}; "
+              f"nearest peak claimed by another GT {prof.get('claimed', '?')}; "
+              f"classes {prof['classes']}; seam {prof['seam']}")
+
+    if args.json_out:
+        payload = {"split": args.split, "challenger": label,
+                   "rampnet_op_threshold": args.rampnet_op_threshold,
+                   "vistas_input_size": args.vistas_input_size,
+                   "radius": args.radius, "null_trials": NULL_TRIALS,
+                   "null_seed": NULL_SEED, "null_rng": "per-site",
+                   "n_sites": len(rows),
+                   "n_panos": len(panos), "skipped_sites": skipped,
+                   "panos_without_floor_peaks": len(no_floor_probe),
+                   "cells": summaries, "sites": rows}
+        os.makedirs(os.path.dirname(os.path.abspath(args.json_out)), exist_ok=True)
+        # newline="" so a Windows re-run does not emit CRLF and break byte-comparison.
+        with open(args.json_out, "w", encoding="utf-8", newline="") as f:
+            json.dump(payload, f, indent=2, sort_keys=True)
+            f.write("\n")
+        print(f"\nwrote {args.json_out}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/analysis/complementarity.py b/scripts/analysis/complementarity.py
index 4525d662..f5d8687d 100644
--- a/scripts/analysis/complementarity.py
+++ b/scripts/analysis/complementarity.py
@@ -1,87 +1,397 @@
-"""RampNet vs Gemini-3.6-flash complementarity on richmond (read-only, from cache).
+"""Do RampNet and a challenger miss *different* ramps? (issue #35 gate)
 
-Answers issue #35's decision gate: do the two models miss *different* ramps?
-For each GT ramp on recall-eligible panos, record whether RampNet found it, Gemini
-found it, both, or neither -> oracle-union recall + the RampNet-miss n Gemini-hit set.
+For each GT ramp on a recall-eligible pano, record whether RampNet found it, the
+challenger found it, both, or neither -> oracle-union recall, and the set that
+matters: **RampNet-miss n challenger-hit**.
+
+Read-only: RampNet's side comes from the bundle's committed detections and the
+challenger's from ``.model_cache``, so this never runs a model or spends anything.
+
+**Which RampNet, though?** The committed bundle detections are the *shipped* operating
+point — on richmond every one scores >= 0.5519 — while this document's own
+recommendation since #54/#55 (PR #79) is **0.30**. Those are different models for this
+purpose: at 0.30 RampNet finds 257 of richmond's 310 ramps instead of 238, so 19 of the
+misses the shipped threshold reports are ramps RampNet already has. 16 of those 19 are
+in the challenger-recovered cell, which is what the complementary-gain headline counts,
+so that headline falls from 54 to 38 (~44 to ~30 after the chance null); the other 3 come
+out of the found-by-nobody cell. ``--rampnet-op-threshold`` re-sources RampNet's side
+from the committed ``analysis_out/op_cache/<split>.json`` floor peaks at a given
+threshold, which is how you ask "what is the complementary gain at the operating point
+we would actually deploy?". Default is the bundle records, so the published roster
+numbers and the #35 gate's committed results are unchanged.
+
+**The oracle-union recall is a ceiling, not a proposal.** It assumes you could
+keep every right call and discard every wrong one, which no combiner can do. The
+FP arithmetic printed at the end is the counterweight, and for a low-precision
+challenger it is usually decisive -- see the union precision line. Pair this with
+``null_recall.py`` before believing any union number from a model that emits many
+boxes per pano: at high density a share of "hits" are what the match radius hands
+out for free, and that share inflates the complementary set too.
+
+Usage -- the positional form is the one three call sites in
+``docs/model_comparison.md`` use, and it still means what it did:
+
+    python scripts/analysis/complementarity.py                       # gemini-3.6-flash, richmond
+    python scripts/analysis/complementarity.py gemini-3.1-pro-preview paterson
+    python scripts/analysis/complementarity.py vistas:curb-cut richmond \
+        --vistas-input-size 1024 1024
+
+The first positional is a **model spec** (``provider`` or ``provider:model_id``,
+as ``compare.py --models`` takes them). A bare token that is not a known provider
+is read as a Gemini model id, which is how the #35 gate was invoked before this
+script grew past one provider.
 """
-import os as _os, sys as _sys
-REPO = _os.path.dirname(_os.path.dirname(_os.path.dirname(_os.path.abspath(__file__))))
-OUT = _os.environ.get("RAMPNET_ANALYSIS_OUT", _os.path.join(REPO, "analysis_out"))
-_os.makedirs(OUT, exist_ok=True)
-DA3_SRC = _os.environ.get("DA3_SRC")  # path to Depth-Anything-3/src (see README)
-import os, sys
+import argparse
+from collections import namedtuple
+import os
+import sys
+
+REPO = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 sys.path.insert(0, REPO)
 sys.path.insert(0, os.path.join(REPO, "scripts", "model_comparison"))
 
-from rampnet.detection_eval import (
-    build_ground_truth, score_pano, radius_sq_for, PANO_SCALE_X, PANO_SCALE_Y, _xy, prediction_confidence)
-from compare import load_bundle, DetectionCache, cache_key
-from detectors import build_detector
+from rampnet import roster                                                # noqa: E402
+from rampnet.detection_eval import (                                      # noqa: E402
+    build_ground_truth, score_pano, radius_sq_for, PANO_SCALE_X, PANO_SCALE_Y,
+    _xy, prediction_confidence)
+from rampnet.metrics import greedy_match                                  # noqa: E402
+from compare import load_bundle, DetectionCache, cache_key                # noqa: E402
+from detectors import build_detector, parse_model_spec, PROVIDERS         # noqa: E402
+from operating_point_curve import CACHE_DIR, read_cache                   # noqa: E402
+
+#: The four complementarity cells, in table order.
+CELLS = ("both", "rampnet_only", "challenger_only", "neither")
+
+
+def cell_of(rampnet_hit, challenger_hit):
+    """Which of the four cells a GT ramp falls in."""
+    if rampnet_hit:
+        return "both" if challenger_hit else "rampnet_only"
+    return "challenger_only" if challenger_hit else "neither"
+
+
+def load_floor_peaks(split):
+    """``{pano_id: [(x, y, score), ...]}`` from ``analysis_out/op_cache/<split>.json``.
+
+    Raises ``OSError`` / ``ValueError`` / ``KeyError`` exactly as ``read_cache`` does;
+    callers decide whether a missing op_cache is fatal (this script: it is, the
+    threshold has nothing to apply to) or a fallback (``cascade_gate.py``).
+    """
+    cached, _ = read_cache(os.path.join(CACHE_DIR, f"{split}.json"))
+    return {pd["pano"]: pd["preds"] for pd in cached}
+
+
+def floor_gap_warning(no_floor, split):
+    """The warning both scripts print when the op_cache is missing panos, or ``None``.
+
+    A pano the op_cache does not list scores as RampNet-blank -- every GT ramp on it
+    becomes a miss -- while the header still says RampNet came from the op_cache.
+    That is a silent shift of the whole partition in the direction that under-states
+    the cascade, so it is counted and said out loud rather than absorbed.
+    """
+    if not no_floor:
+        return None
+    return (f"WARNING: {no_floor} pano(s) are absent from analysis_out/op_cache/"
+            f"{split}.json, so RampNet scored blank on them and every GT ramp there "
+            f"counts as a miss. Regenerate the op_cache for this split before reading "
+            f"the cells.")
+
+
+#: What ``partition_cells`` returns. ``sites`` is one row per GT ramp
+#: (``pano``, ``x``, ``y``, ``cell``); ``counts`` is the four cells; ``r_fp``/``c_fp``
+#: are RampNet's and the challenger's false positives on the same panos, from the same
+#: matcher; ``shift_rows`` feeds ``complementary_null``; ``no_floor`` counts panos the
+#: op_cache did not list (only meaningful when it was the source).
+Partition = namedtuple(
+    "Partition", "sites counts r_fp c_fp shift_rows panos missing no_floor")
+
+
+def partition_cells(records, verdicts, challenger_for, radius_sq,
+                    rampnet_op_threshold=None, floor_peaks=None):
+    """Partition every recall-eligible GT ramp into the four complementarity cells.
+
+    The one loop behind this script's table, ``cascade_gate.py``'s partition and the
+    regression tests -- it used to be copied into all three, so a change to the
+    threshold filter or the op_cache fallback in one did not fail the others.
+
+    ``challenger_for(pano_id)`` returns the challenger's cached predictions for a pano
+    or ``None`` when it has none (the pano is then skipped and counted in
+    ``missing``). RampNet's side is the bundle's shipped detections unless
+    ``rampnet_op_threshold`` is given, in which case it is ``floor_peaks`` (from
+    ``load_floor_peaks``) filtered at that threshold.
+
+    The cells and the false-positive counts come from one matcher at one radius:
+    ``matched_gt`` and ``score_pano`` are both handed ``radius_sq``. Before that,
+    ``--radius`` reached the cells but ``score_pano`` fell back to its default, so a
+    non-default radius printed cells and an FP bill measured two different ways.
+    """
+    if (rampnet_op_threshold is None) != (floor_peaks is None):
+        raise ValueError("rampnet_op_threshold and floor_peaks go together: the "
+                         "threshold is applied to the floor peaks and to nothing else")
+    sites, shift_rows = [], []
+    counts = dict.fromkeys(CELLS, 0)
+    r_fp = c_fp = panos = missing = no_floor = 0
+    for pid, entry in verdicts.items():
+        gt = build_ground_truth(records[pid]["detections"], entry["dets"],
+                                entry["missed"], entry["no_missed"])
+        if not gt.fn_confirmed:
+            continue
+        cp = challenger_for(pid)
+        if cp is None:
+            missing += 1
+            continue
+        if floor_peaks is not None:
+            if pid not in floor_peaks:
+                no_floor += 1
+            rp = [q for q in floor_peaks.get(pid, []) if q[2] >= rampnet_op_threshold]
+        else:
+            rp = [(d["x_normalized"], d["y_normalized"], d["confidence"])
+                  for d in records[pid]["detections"]]
+        mr = matched_gt(rp, gt.gt_points, radius_sq)
+        mc = matched_gt(cp, gt.gt_points, radius_sq)
+        for i, (gx, gy) in enumerate(gt.gt_points):
+            cell = cell_of(i in mr, i in mc)
+            counts[cell] += 1
+            sites.append({"pano": pid, "x": gx, "y": gy, "cell": cell})
+        r_fp += score_pano(rp, gt, radius_sq).fp
+        c_fp += score_pano(cp, gt, radius_sq).fp
+        shift_rows.append((cp, [g for i, g in enumerate(gt.gt_points) if i not in mr]))
+        panos += 1
+    return Partition(sites, counts, r_fp, c_fp, shift_rows, panos, missing, no_floor)
 
-MODEL = sys.argv[1] if len(sys.argv) > 1 else "gemini-3.6-flash"
-BUNDLE = sys.argv[2] if len(sys.argv) > 2 else "richmond"   # benchmark/<split> name
 
-class Args:
-    gemini_model = MODEL; qwen_model = "Qwen/Qwen3-VL"; tiling = "perspective"
+def matched_gt(preds, gt_points, radius_sq):
+    """Which GT ramps a model covers: greedy 1:1, exactly as ``score_pano`` matches.
 
-RSQ = radius_sq_for()
+    Ordering and geometry are ``score_pano``'s -- highest confidence first when the
+    predictions carry one, else input order, then ``rampnet.metrics.greedy_match``
+    with ``wrap_x=True``, which measures the x separation the shorter way round the
+    panorama (``rampnet.geometry.fold``, #132). Calling the shared matcher rather than
+    re-deriving the distance here is what keeps the four cells below and the FP counts
+    from ``score_pano`` on the same matcher: this script prints both in one table, and
+    ``score_pano`` wraps by default, so an inline non-wrapping distance here would put
+    two matchers in one output.
 
-def matched_gt(preds, gt_points):
-    """Greedy 1:1 match (mirrors score_pano); return the set of GT indices covered."""
+    Measured effect of the wrap on this script's cells: zero flips on RampNet's side
+    at >= 0.5519, >= 0.30 and >= 0.05, and zero on the published Vistas 384 arm
+    (richmond); the paterson #35 gate reproduces unchanged. The parity 1024 arm could
+    not be re-checked -- its detections are not published (see the note beside the
+    parity table in ``docs/model_comparison.md``) -- so the bound there is the doc's
+    own seam count, 1 site in 38.
+    """
     confs = [prediction_confidence(p) for p in preds]
-    order = (sorted(range(len(preds)), key=lambda i: confs[i] if confs[i] is not None else float("-inf"),
-                    reverse=True) if any(c is not None for c in confs) else range(len(preds)))
-    claimed, hit = [False] * len(gt_points), set()
-    for i in order:
-        pxn, pyn = _xy(preds[i]); px, py = pxn * PANO_SCALE_X, pyn * PANO_SCALE_Y
-        best_k, best = -1, RSQ
-        for k, (gx, gy) in enumerate(gt_points):
-            if claimed[k]:
-                continue
-            d = (px - gx * PANO_SCALE_X) ** 2 + (py - gy * PANO_SCALE_Y) ** 2
-            if d < best:
-                best, best_k = d, k
-        if best_k >= 0:
-            claimed[best_k] = True; hit.add(best_k)
-    return hit
-
-records, verdicts, _ = load_bundle(os.path.join(REPO, "benchmark", BUNDLE))
-label, gem = build_detector("gemini", MODEL, records, Args())
-sig, cache = gem.signature(), DetectionCache(os.path.join(REPO, ".model_cache"))
-
-N = both = r_only = g_only = neither = 0
-r_fp = g_fp = 0
-panos = missing = 0
-for pid, entry in verdicts.items():
-    gt = build_ground_truth(records[pid]["detections"], entry["dets"], entry["missed"], entry["no_missed"])
-    if not gt.fn_confirmed:
-        continue
-    gp = cache.get(cache_key(label, sig, BUNDLE, pid))
-    if gp is None:
-        missing += 1; continue
-    rp = [(d["x_normalized"], d["y_normalized"], d["confidence"]) for d in records[pid]["detections"]]
-    mr, mg = matched_gt(rp, gt.gt_points), matched_gt(gp, gt.gt_points)
-    for i in range(len(gt.gt_points)):
-        r, g = i in mr, i in mg
-        both += r and g; r_only += r and not g; g_only += g and not r; neither += not r and not g
-    N += len(gt.gt_points)
-    r_fp += score_pano(rp, gt).fp
-    g_fp += score_pano(gp, gt).fp
-    panos += 1
-
-r_tp, g_tp, union = both + r_only, both + g_only, both + r_only + g_only
-rampnet_misses = g_only + neither
-print(f"{BUNDLE} complementarity — RampNet vs {MODEL}  ({panos} recall-eligible panos, {N} GT ramps"
-      + (f"; {missing} panos missing from cache" if missing else "") + ")\n")
-print(f"  RampNet recall     {r_tp/N:.3f}   ({r_tp}/{N})")
-print(f"  Gemini recall      {g_tp/N:.3f}   ({g_tp}/{N})")
-print(f"  ORACLE-UNION recall{union/N:.3f}   ({union}/{N})   <- ceiling if you could keep every right call")
-print()
-print(f"  found by BOTH        {both:4d}  ({both/N:.1%})")
-print(f"  RampNet ONLY         {r_only:4d}  ({r_only/N:.1%})")
-print(f"  Gemini  ONLY         {g_only:4d}  ({g_only/N:.1%})   <- complementary gain (RampNet-miss n Gemini-hit)")
-print(f"  found by NEITHER     {neither:4d}  ({neither/N:.1%})   <- hard misses, no model helps")
-print()
-print(f"  Union recall lift over RampNet:  +{(union - r_tp)/N:.3f}  ({g_only} ramps)")
-print(f"  Of RampNet's {rampnet_misses} misses, Gemini recovers {g_only} ({g_only/rampnet_misses:.0%}); {neither} nobody finds")
-print(f"  FP cost on these panos:  RampNet {r_fp}  |  Gemini {g_fp}   (a naive union pays ~both)")
+    order = (sorted(range(len(preds)),
+                    key=lambda i: confs[i] if confs[i] is not None else float("-inf"),
+                    reverse=True)
+             if any(c is not None for c in confs) else range(len(preds)))
+    assignments = greedy_match([_xy(preds[i]) for i in order], gt_points,
+                               radius_sq, PANO_SCALE_X, PANO_SCALE_Y, wrap_x=True)
+    return {gt_index for gt_index, _ in assignments if gt_index >= 0}
+
+
+def complementary_null(rows, radius_sq):
+    """How many of rampnet's misses would the challenger "recover" by coincidence?
+
+    Same null as ``null_recall.py`` -- score pano A's ground truth against pano
+    B's predictions, averaged over every non-identity cyclic shift -- but
+    restricted to the GT rampnet MISSED, because that is the subset the
+    complementary-gain headline is about. Both sides stay real model output on
+    real imagery, so box count and spatial clustering are preserved; only the
+    pairing is wrong, so every match is chance.
+
+    Applying the whole-split null from ``null_recall.py`` to this subset instead
+    would assume the coincidence rate is uniform across GT. It need not be:
+    rampnet's misses are a biased sample (far-field, adjacent pairs), and those
+    are exactly the places box density differs. Hence measuring it here.
+
+    One construction difference from ``null_recall.py``, stated so the two are not
+    read as the same number: the shifted pano's *whole* prediction set is matched
+    against only the missed GT, whereas the real ``c_only`` cell was matched against
+    all GT on the pano (a box that lands on a found ramp there is spent). Each box
+    therefore has more targets here than it had in the real pairing, so this null is
+    a slight over-estimate of chance and the "attributable" figures quoted after it
+    are conservative. ``null_recall.py`` matches against all GT; that the two land
+    close on richmond (0.143 here against 0.145 there) is the evidence the bias is
+    small, not a sign they are the same measurement.
+
+    Returns (mean, max) as a fraction of the missed GT.
+    """
+    preds = [p for p, _ in rows]
+    missed = [m for _, m in rows]
+    n = len(rows)
+    total = sum(len(m) for m in missed)
+    if not total or n < 2:
+        return 0.0, 0.0
+    shifted = []
+    for k in range(1, n):
+        hit = sum(len(matched_gt(preds[(i + k) % n], missed[i], radius_sq))
+                  for i in range(n))
+        shifted.append(hit / total)
+    return sum(shifted) / len(shifted), max(shifted)
+
+
+def model_spec(token):
+    """``provider``/``provider:model_id``, or a legacy bare Gemini model id.
+
+    A colon means the caller wrote ``provider:model_id``, so an unrecognised provider
+    there is a typo and is rejected. Without a colon the token is read as a Gemini
+    model id: that is the form the #35 gate was invoked with before this script grew
+    past one provider, and reading it as a provider would reject strings that used to
+    be valid.
+
+    Rejecting the colon form matters because a bad spec does not fail loudly on its
+    own. It builds a detector whose signature nothing ever cached, every lookup
+    misses, and the script reports a model with zero cached detections -- which reads
+    as a missing run rather than a mistyped argument.
+    """
+    provider, model_id = parse_model_spec(token)
+    if provider in PROVIDERS:
+        return provider, model_id
+    if ":" in token:
+        sys.exit(f"unknown provider {provider!r} in model spec {token!r} "
+                 f"(choose from: {', '.join(PROVIDERS)})")
+    return "gemini", token
+
+
+def compare_args(args):
+    """A namespace matching ``compare.py``'s parser defaults, so the cache key this
+    script reconstructs is the one ``compare.py`` wrote under.
+
+    Provider defaults come from ``rampnet.roster.PROVIDER_DEFAULTS`` -- one
+    definition, the same source ``fp_taxonomy``'s shim and ``null_recall`` read --
+    because a wrong default here does not crash, it silently misses every cache
+    entry and reports zero detections. The deviation-only knobs
+    (``vistas_input_size``, ``vistas_revision``) are threaded through from the CLI:
+    they enter the signature ONLY when set, so leaving them off reproduces the
+    published arm and setting one addresses a distinct cache entry.
+    """
+    import argparse as _a
+    ns = _a.Namespace()
+    for k, v in dict(
+            roster.PROVIDER_DEFAULTS,
+            owlv2_query=None, gdino_query=None,
+            gdino_text_threshold=None, score_threshold=None,
+            yolo_model=None, tiling=args.tiling,
+            radius=args.radius, op_threshold=0.0, limit=None,
+            cache_dir=args.cache_dir, no_cache=False,
+            vistas_input_size=args.vistas_input_size,
+            vistas_revision=args.vistas_revision).items():
+        setattr(ns, k, v)
+    return ns
+
+
+def main():
+    ap = argparse.ArgumentParser(description=__doc__,
+                                 formatter_class=argparse.RawDescriptionHelpFormatter)
+    ap.add_argument("model", nargs="?", default=roster.PROVIDER_DEFAULTS["gemini_model"],
+                    help="Model spec (provider or provider:model_id). A bare "
+                         "non-provider token is read as a Gemini model id.")
+    ap.add_argument("split", nargs="?", default="richmond",
+                    help="Benchmark split name (default richmond).")
+    ap.add_argument("--cache-dir", default=os.path.join(REPO, ".model_cache"))
+    ap.add_argument("--rampnet-op-threshold", type=float, default=None,
+                    help="Score RampNet from analysis_out/op_cache/<split>.json floor "
+                         "peaks at this threshold instead of the bundle's committed "
+                         "detections. The bundle is the SHIPPED point (>=0.5519 on "
+                         "richmond); this document recommends 0.30, and the gap "
+                         "changes who gets credit for a recovery. Default: the bundle, "
+                         "so published numbers are unchanged.")
+    ap.add_argument("--radius", type=float, default=0.022)
+    ap.add_argument("--tiling", choices=["perspective", "none"], default="perspective")
+    ap.add_argument("--vistas-input-size", type=int, nargs=2, metavar=("H", "W"),
+                    default=None,
+                    help="Override what the Vistas checkpoint actually sees. In the "
+                         "signature only when set, so this addresses a DIFFERENT "
+                         "cache entry than the published 384x384 arm (#126).")
+    ap.add_argument("--vistas-revision", default=None)
+    args = ap.parse_args()
+
+    provider, model_id = model_spec(args.model)
+    bundle = os.path.join(REPO, "benchmark", args.split)
+    records, verdicts, _ = load_bundle(bundle)
+    if verdicts is None:
+        sys.exit(f"{bundle}: no verdicts.json -- this gate needs a reviewed split.")
+    label, detector = build_detector(provider, model_id, records, compare_args(args))
+    sig = detector.signature()
+    cache = DetectionCache(args.cache_dir)
+    radius_sq = radius_sq_for(args.radius)
+
+    floor_peaks = None
+    if args.rampnet_op_threshold is not None:
+        try:
+            floor_peaks = load_floor_peaks(args.split)
+        except (OSError, ValueError, KeyError) as e:
+            sys.exit("--rampnet-op-threshold needs analysis_out/op_cache/"
+                     f"{args.split}.json, which could not be read ({e}). Generate it "
+                     "with scripts/analysis/operating_point_curve.py, or drop the "
+                     "flag to score RampNet from the bundle's shipped detections.")
+        print(f"rampnet re-sourced from op_cache at >= {args.rampnet_op_threshold} "
+              f"(bundle records are the shipped point and are NOT used)\n")
+
+    part = partition_cells(
+        records, verdicts,
+        lambda pid: cache.get(cache_key(label, sig, args.split, pid)),
+        radius_sq, args.rampnet_op_threshold, floor_peaks)
+    both, r_only, c_only, neither = (part.counts[c] for c in CELLS)
+    n = sum(part.counts.values())
+    r_fp, c_fp, shift_rows = part.r_fp, part.c_fp, part.shift_rows
+    panos, missing = part.panos, part.missing
+
+    if not n:
+        sys.exit("No recall-eligible panos with cached detections -- nothing to compare. "
+                 "Run compare.py for this model/split first (and pass the SAME "
+                 "--vistas-input-size, which is part of the cache key).")
+    warning = floor_gap_warning(part.no_floor, args.split)
+    if warning:
+        print(warning + "\n")
+
+    r_tp, c_tp, union = both + r_only, both + c_only, both + r_only + c_only
+    r_miss = c_only + neither
+    rn = ("rampnet" if floor_peaks is None
+          else f"rampnet@{args.rampnet_op_threshold:g}")
+    print(f"{args.split} complementarity — {rn} vs {label}  "
+          f"({panos} recall-eligible panos, {n} GT ramps"
+          + (f"; {missing} panos missing from cache" if missing else "") + ")\n")
+    print(f"  {rn:18s} recall {r_tp / n:.3f}   ({r_tp}/{n})")
+    print(f"  {label[:18]:18s} recall {c_tp / n:.3f}   ({c_tp}/{n})")
+    print(f"  ORACLE-UNION recall {union / n:.3f}   ({union}/{n})   "
+          f"<- ceiling if you could keep every right call")
+    print()
+    print(f"  found by BOTH        {both:4d}  ({both / n:.1%})")
+    print(f"  {rn[:14]:14s} ONLY   {r_only:4d}  ({r_only / n:.1%})")
+    print(f"  {label[:14]:14s} ONLY   {c_only:4d}  ({c_only / n:.1%})   "
+          f"<- complementary gain (rampnet-miss n challenger-hit)")
+    print(f"  found by NEITHER     {neither:4d}  ({neither / n:.1%})   "
+          f"<- hard misses, no model helps")
+    print()
+    print(f"  Union recall lift over {rn}:  +{(union - r_tp) / n:.3f}  ({c_only} ramps)")
+    if r_miss:
+        print(f"  Of {rn}'s {r_miss} misses, {label} recovers {c_only} "
+              f"({c_only / r_miss:.0%}); {neither} nobody finds")
+        mean_null, max_null = complementary_null(shift_rows, radius_sq)
+        exp = mean_null * r_miss
+        print(f"    null (same boxes, wrong pano): {mean_null:.3f} "
+              f"=> ~{exp:.0f} of those {c_only} are coincidence, "
+              f"~{c_only - exp:.0f} attributable  (worst shift {max_null:.3f})")
+
+    # The counterweight to the oracle number. A naive union keeps every box from
+    # both models, so it pays both FP bills; co-located FPs would dedup, which is
+    # why this is an upper bound on the cost and therefore a LOWER bound on the
+    # union's precision.
+    u_p = union / (union + r_fp + c_fp) if union + r_fp + c_fp else 0.0
+    u_r = union / n
+    u_f1 = 2 * u_p * u_r / (u_p + u_r) if u_p + u_r else 0.0
+    r_p = r_tp / (r_tp + r_fp) if r_tp + r_fp else 0.0
+    r_f1 = 2 * r_p * (r_tp / n) / (r_p + r_tp / n) if r_p + r_tp / n else 0.0
+    print()
+    print(f"  FP cost on these panos:  {rn} {r_fp}  |  {label} {c_fp}"
+          f"   (a naive union pays ~both)")
+    print(f"  {rn} alone:  P {r_p:.3f}  R {r_tp / n:.3f}  F1 {r_f1:.3f}")
+    print(f"  NAIVE UNION:    P {u_p:.3f}  R {u_r:.3f}  F1 {u_f1:.3f}"
+          f"   <- precision is a lower bound (no FP dedup)")
+    print(f"  => a naive union {'BEATS' if u_f1 > r_f1 else 'LOSES TO'} {rn} alone "
+          f"on F1 ({u_f1:.3f} vs {r_f1:.3f})")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/analysis/null_recall.py b/scripts/analysis/null_recall.py
index ee8a5587..602f4ddc 100644
--- a/scripts/analysis/null_recall.py
+++ b/scripts/analysis/null_recall.py
@@ -139,6 +139,19 @@ def main():
     ap.add_argument("--molmo-model", default=_D["molmo_model"])
     ap.add_argument("--molmo-coord-scale", choices=["auto", "100", "1000"],
                     default=_D["molmo_coord_scale"])
+    ap.add_argument("--vistas-class-set", default=_D["vistas_class_set"])
+    ap.add_argument("--vistas-model", default=_D["vistas_model"])
+    ap.add_argument("--vistas-min-area-px", type=int,
+                    default=_D["vistas_min_area_px"])
+    ap.add_argument("--vistas-dtype", choices=["float16", "float32"],
+                    default=_D["vistas_dtype"])
+    # Deviation-only, exactly as in compare.py: absent from the signature
+    # unless set. Without these two the vistas arm here would silently be the
+    # published 384x384 one even when the run being analysed was at parity --
+    # a wrong-arm read that looks like a valid answer (#126).
+    ap.add_argument("--vistas-input-size", type=int, nargs=2, metavar=("H", "W"),
+                    default=None)
+    ap.add_argument("--vistas-revision", default=None)
     args = ap.parse_args()
 
     records, verdicts, panos_dir = compare.load_bundle(args.bundle)
diff --git a/tests/test_cascade_gate.py b/tests/test_cascade_gate.py
new file mode 100644
index 00000000..f4ace4ff
--- /dev/null
+++ b/tests/test_cascade_gate.py
@@ -0,0 +1,465 @@
+"""Unit tests for the cascade gate (#126).
+
+Pure logic plus a drift guard on the committed artifacts — no GPU, no imagery, no
+``.model_cache``. The heavy half (one RampNet forward per pano) is exercised by running
+the script; what these protect is the bookkeeping the write-up quotes.
+
+``summarize`` is the whole reporting surface: every number in the cascade tables in
+``docs/model_comparison.md`` is one of its keys, and it is re-derivable from the
+``sites`` list committed alongside it. So the guard here is that ``summarize`` applied
+to a committed run's ``sites`` reproduces that run's ``cells`` exactly — which also
+pins which *subset* each figure is a median of, the thing a hand-copied number gets
+wrong (the cell's ``act_median`` is not the no-peak rows' median).
+
+The parity (1024x1024) detections behind those two artifacts are not published, so a
+clean clone cannot regenerate these files. It can still check them against themselves,
+which is what the second half of this file does — including what the "no floor peak in
+radius" row *is*: the committed ``op_cache`` says where each such site's nearest peak
+sits and whether the matcher already gave it to a neighbour, and the doc's reading of
+that row is pinned to those columns here.
+"""
+import json
+import os
+import random
+import sys
+
+import pytest
+
+REPO = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.insert(0, REPO)
+sys.path.insert(0, os.path.join(REPO, "scripts", "analysis"))
+sys.path.insert(0, os.path.join(REPO, "scripts", "model_comparison"))
+
+import cascade_gate as cg  # noqa: E402
+import silent_activation as sa  # noqa: E402
+from farfield_forensics import quartiles  # noqa: E402
+from rampnet.detection_eval import PANO_SCALE_X, radius_sq_for  # noqa: E402
+
+OUT = os.path.join(REPO, "analysis_out")
+SHIPPED = os.path.join(OUT, "cascade_gate.json")
+OP030 = os.path.join(OUT, "cascade_gate_op030.json")
+RSQ = radius_sq_for()
+R_PX = RSQ ** 0.5                            # 22.5 heatmap px
+R_NORM = R_PX / PANO_SCALE_X                 # the same radius in normalized x
+
+
+def _row(cell, **kw):
+    """One site row, with the fields ``summarize`` reads and neutral defaults."""
+    row = {"cell": cell, "act": 0.5, "center": 0.5, "argmax_off_px": 0.0,
+           "nearest_peak_px": 0.0, "nearest_peak_score": 0.5, "class": "tail",
+           "peak_in_radius": True, "seam": False,
+           "null_pct": None, "null_med": None, "null_p95": None}
+    row.update(kw)
+    return row
+
+
+# --------------------------------------------------------------------------- #
+# cell_of — the 2x2 that names the recoverable set
+# --------------------------------------------------------------------------- #
+def test_the_four_cells():
+    assert cg.cell_of(True, True) == "both"
+    assert cg.cell_of(True, False) == "rampnet_only"
+    assert cg.cell_of(False, True) == "challenger_only"
+    assert cg.cell_of(False, False) == "neither"
+
+
+def test_only_the_two_rampnet_miss_cells_get_a_null():
+    # The null is meaningless where RampNet found the ramp: the activation is high by
+    # construction there, which is why those cells are the positive control. So the
+    # cells a null is reported for are exactly the two cell_of returns on a miss.
+    assert set(cg.CELLS) == {"both", "rampnet_only", "challenger_only", "neither"}
+    assert set(cg.MISS_CELLS) == {cg.cell_of(False, True), cg.cell_of(False, False)}
+
+
+# --------------------------------------------------------------------------- #
+# summarize — the reporting surface
+# --------------------------------------------------------------------------- #
+def test_an_empty_cell_reports_n_zero_and_nothing_else():
+    out = cg.summarize([_row("both")], "neither")
+    assert out == {"cell": "neither", "n": 0}
+
+
+def test_it_selects_only_its_own_cell():
+    rows = [_row("both", act=0.9), _row("challenger_only", act=0.1),
+            _row("challenger_only", act=0.3), _row("challenger_only", act=0.4)]
+    out = cg.summarize(rows, "challenger_only")
+    assert out["n"] == 3
+    assert out["act_median"] == 0.3
+
+
+def test_peak_in_radius_is_counted_and_shared():
+    rows = [_row("challenger_only", peak_in_radius=True),
+            _row("challenger_only", peak_in_radius=True),
+            _row("challenger_only", peak_in_radius=False, nearest_peak_px=40.0),
+            _row("challenger_only", peak_in_radius=False, nearest_peak_px=60.0)]
+    out = cg.summarize(rows, "challenger_only")
+    assert out["peak_in_radius"] == 2
+    assert out["peak_in_radius_share"] == 0.5
+
+
+def test_the_peak_score_median_covers_only_the_rows_that_have_a_peak():
+    # This is the distinction a hand-copied number loses: a per-row column's median is
+    # over the rows that have that column, not over the cell.
+    rows = [_row("challenger_only", peak_in_radius=True, nearest_peak_score=0.10),
+            _row("challenger_only", peak_in_radius=True, nearest_peak_score=0.20),
+            _row("challenger_only", peak_in_radius=True, nearest_peak_score=0.30),
+            _row("challenger_only", peak_in_radius=False, nearest_peak_score=0.99)]
+    out = cg.summarize(rows, "challenger_only")
+    # The 0.99 belongs to a row with no peak in radius, so it is out of this median.
+    assert out["peak_in_radius_score_median"] == 0.2
+
+
+def test_a_cell_with_no_peak_anywhere_reports_no_peak_score():
+    rows = [_row("neither", peak_in_radius=False, nearest_peak_px=None,
+                 nearest_peak_score=None)]
+    out = cg.summarize(rows, "neither")
+    assert out["peak_in_radius"] == 0
+    assert "peak_in_radius_score_median" not in out
+    assert out["nearest_peak_px_median"] is None
+
+
+def test_the_hit_cells_carry_no_null_statistics():
+    out = cg.summarize([_row("both"), _row("both")], "both")
+    for key in ("null_pct_median", "above_null_p95", "null_med_median"):
+        assert key not in out
+
+
+def test_above_null_p95_counts_sites_over_their_own_shifted_p95():
+    rows = [_row("challenger_only", act=0.5, null_pct=0.99, null_med=0.01, null_p95=0.2),
+            _row("challenger_only", act=0.1, null_pct=0.40, null_med=0.02, null_p95=0.2),
+            _row("challenger_only", act=0.3, null_pct=0.70, null_med=0.03, null_p95=0.2)]
+    out = cg.summarize(rows, "challenger_only")
+    # act > this site's own p95 on the 0.5 and 0.3 rows, not on the 0.1 one.
+    assert out["above_null_p95"] == 2
+    assert out["null_pct_median"] == 0.7
+    assert out["null_med_median"] == 0.02
+
+
+def test_seam_sites_are_counted_per_cell():
+    rows = [_row("challenger_only", seam=True), _row("challenger_only", seam=False),
+            _row("both", seam=True)]
+    assert cg.summarize(rows, "challenger_only")["seam"] == 1
+    assert cg.summarize(rows, "both")["seam"] == 1
+
+
+def test_the_class_shares_sum_to_one():
+    rows = [_row("neither", **{"class": "absent"}),
+            _row("neither", **{"class": "faint_local"}),
+            _row("neither", **{"class": "tail"}),
+            _row("neither", **{"class": "tail"})]
+    out = cg.summarize(rows, "neither")
+    assert out["classes"] == {"absent": 1, "faint_local": 1, "tail": 2}
+    assert sum(out["class_share"].values()) == 1.0
+
+
+# --------------------------------------------------------------------------- #
+# no_peak_profile — what the "no floor peak in radius" row is made of
+# --------------------------------------------------------------------------- #
+def test_the_profile_covers_only_the_rows_with_no_peak_in_radius():
+    rows = [_row("challenger_only", peak_in_radius=True, nearest_peak_px=5.0),
+            _row("challenger_only", peak_in_radius=False, nearest_peak_px=30.0,
+                 argmax_off_px=22.4, act=0.3),
+            _row("challenger_only", peak_in_radius=False, nearest_peak_px=60.0,
+                 argmax_off_px=3.0, act=0.1, **{"class": "faint_local"}),
+            _row("neither", peak_in_radius=False, nearest_peak_px=30.0)]
+    out = cg.no_peak_profile(rows, "challenger_only", R_PX)
+    assert out["n"] == 2
+    assert out["nearest_peak_px_median"] == 60.0       # quartiles: v[n // 2]
+    assert out["peak_within_2r"] == 1 and out["peak_beyond_2r"] == 1
+    assert out["argmax_on_edge"] == 1                  # 22.4 is within 0.5 px of R
+    assert out["classes"] == {"absent": 0, "faint_local": 1, "tail": 1}
+    assert "claimed" not in out                        # rows predate the column
+
+
+def test_the_profile_reports_the_claimed_count_only_when_every_row_carries_it():
+    rows = [_row("neither", peak_in_radius=False, nearest_peak_px=30.0,
+                 nearest_peak_claimed=True),
+            _row("neither", peak_in_radius=False, nearest_peak_px=30.0,
+                 nearest_peak_claimed=False)]
+    assert cg.no_peak_profile(rows, "neither", R_PX)["claimed"] == 1
+
+
+def test_an_empty_profile_reports_n_zero():
+    assert cg.no_peak_profile([_row("both")], "neither", R_PX) == {"cell": "neither",
+                                                                   "n": 0}
+
+
+def test_a_row_with_no_peak_anywhere_counts_as_beyond_two_radii():
+    rows = [_row("neither", peak_in_radius=False, nearest_peak_px=None,
+                 nearest_peak_score=None)]
+    out = cg.no_peak_profile(rows, "neither", R_PX)
+    assert out["nearest_peak_px_median"] is None
+    assert out["peak_within_2r"] == 0 and out["peak_beyond_2r"] == 1
+
+
+# --------------------------------------------------------------------------- #
+# claimed_by_adjacent — the #130 mechanism, per site
+# --------------------------------------------------------------------------- #
+def test_a_neighbours_detection_between_two_ramps_is_claimed_by_the_nearer_one():
+    # Two ramps 1.5 R apart, one peak on the left ramp. The left ramp's nearest peak
+    # is its own (not claimed by another); the right ramp's nearest peak is that same
+    # one, and the matcher gave it to the left ramp.
+    sites = [{"x": 0.5, "y": 0.5}, {"x": 0.5 + 1.5 * R_NORM, "y": 0.5}]
+    preds = [(0.5, 0.5, 0.9)]
+    assert cg.claimed_by_adjacent(sites, preds, 0.30, RSQ) == [False, True]
+
+
+def test_a_peak_below_the_threshold_claims_nothing():
+    sites = [{"x": 0.5, "y": 0.5}, {"x": 0.5 + 1.5 * R_NORM, "y": 0.5}]
+    assert cg.claimed_by_adjacent(sites, [(0.5, 0.5, 0.2)], 0.30, RSQ) == [False, False]
+
+
+def test_no_peaks_means_nothing_is_claimed():
+    assert cg.claimed_by_adjacent([{"x": 0.5, "y": 0.5}], [], 0.30, RSQ) == [False]
+
+
+def test_nearest_peak_index_agrees_with_nearest_peak_across_the_seam():
+    preds = [(0.5, 0.5, 0.9), (0.998, 0.5, 0.4)]
+    x, y = 0.002, 0.5
+    i = cg.nearest_peak_index(preds, x, y)
+    assert i == 1
+    assert sa.nearest_peak(preds, x, y)[1] == preds[i][2]
+    assert cg.nearest_peak_index([], x, y) is None
+
+
+# --------------------------------------------------------------------------- #
+# site_rng — a site's null must not depend on which sites came before it
+# --------------------------------------------------------------------------- #
+def _heat_with_bump():
+    # A ramp along the site's row, so every azimuth draws a different value and the
+    # null's median and p95 depend on which azimuths were drawn.
+    h = [[0.0] * 1024 for _ in range(512)]
+    for c in range(1024):
+        h[256][c] = 0.03 * c / 1024
+    h[256][512] = 0.04
+    return h
+
+
+def test_a_sites_null_is_the_same_whatever_ran_before_it():
+    h = _heat_with_bump()
+    x, y = 512 / 1024, 256 / 512
+    first = sa.null_percentile(h, x, y, cg.site_rng("p", x, y), trials=50)
+    # Consume a different amount of "other sites" work, then ask again.
+    for other in range(3):
+        sa.null_percentile(h, 0.1 * (other + 1), y, cg.site_rng("p", 0.1 * (other + 1), y),
+                           trials=50)
+    again = sa.null_percentile(h, x, y, cg.site_rng("p", x, y), trials=50)
+    assert first == again
+
+
+def test_one_stream_shared_across_sites_did_depend_on_order():
+    # The shape being replaced: the same site, read second instead of first from one
+    # stream, draws different azimuths. Guarded so the reason for site_rng stays
+    # demonstrable rather than remembered.
+    h = _heat_with_bump()
+    x, y = 512 / 1024, 256 / 512
+    rng = random.Random(sa.NULL_SEED)
+    alone = sa.null_percentile(h, x, y, rng, trials=50)[2:]
+    rng = random.Random(sa.NULL_SEED)
+    sa.null_percentile(h, 0.1, y, rng, trials=50)
+    after_another = sa.null_percentile(h, x, y, rng, trials=50)[2:]
+    assert alone != after_another
+
+
+def test_different_sites_get_different_streams():
+    a = cg.site_rng("p", 0.5, 0.5).random()
+    assert a != cg.site_rng("p", 0.5, 0.6).random()
+    assert a != cg.site_rng("q", 0.5, 0.5).random()
+    assert a == cg.site_rng("p", 0.5, 0.5).random()
+
+
+# --------------------------------------------------------------------------- #
+# panos_without_floor — the probe path's op_cache gap, which used to be silent
+# --------------------------------------------------------------------------- #
+def test_a_pano_the_op_cache_does_not_list_is_named():
+    floor = {"a": [(0.5, 0.5, 0.2)], "b": []}
+    assert cg.panos_without_floor(["a", "b", "c"], floor) == ["c"]
+    # Listed with zero floor peaks is an answer, not a gap.
+    assert cg.panos_without_floor(["b"], floor) == []
+
+
+# --------------------------------------------------------------------------- #
+# the committed artifacts — every cell figure re-derives from the sites list
+# --------------------------------------------------------------------------- #
+def _payload(path):
+    with open(path, encoding="utf-8") as fh:
+        return json.load(fh)
+
+
+def test_the_committed_cells_re_derive_from_the_committed_sites():
+    for path in (SHIPPED, OP030):
+        payload = _payload(path)
+        assert [cg.summarize(payload["sites"], c) for c in cg.CELLS] == payload["cells"], (
+            f"{os.path.basename(path)}: cells no longer match summarize(sites)")
+
+
+def test_the_two_artifacts_partition_the_same_310_richmond_ramps():
+    for path in (SHIPPED, OP030):
+        payload = _payload(path)
+        assert payload["split"] == "richmond"
+        assert payload["challenger"] == "mask2former-vistas-curb-cut"
+        assert payload["vistas_input_size"] == [1024, 1024]
+        assert payload["n_sites"] == 310
+        assert sum(c["n"] for c in payload["cells"]) == 310
+        assert payload["skipped_sites"] == 0
+
+
+def test_only_the_op030_artifact_records_the_threshold_key():
+    # cascade_gate.json was written before --rampnet-op-threshold entered the payload,
+    # so a regeneration would add "rampnet_op_threshold": null and change its bytes
+    # even with identical results. Stated in docs/model_comparison.md beside it.
+    assert "rampnet_op_threshold" not in _payload(SHIPPED)
+    assert _payload(OP030)["rampnet_op_threshold"] == 0.3
+
+
+def test_the_cascade_ceiling_is_nineteen_promotable_ramps():
+    # docs/model_comparison.md, "The cascade gate": of the 38 genuinely-complementary
+    # ramps at rampnet@0.30, 19 carry a floor peak in radius scoring 0.05-0.30 (the
+    # promotable set), 4 carry one at >= 0.30 that the greedy matcher gave to an
+    # adjacent GT, and 15 carry none inside the radius.
+    sites = [s for s in _payload(OP030)["sites"] if s["cell"] == "challenger_only"]
+    assert len(sites) == 38
+    peaked = [s for s in sites if s["peak_in_radius"]]
+    no_peak = [s for s in sites if not s["peak_in_radius"]]
+    assert len(no_peak) == 15
+    assert sum(1 for s in peaked if 0.05 <= s["nearest_peak_score"] < 0.30) == 19
+    assert sum(1 for s in peaked if s["nearest_peak_score"] >= 0.30) == 4
+
+
+def test_the_no_peak_rows_have_their_own_activation_median():
+    # The row in the cascade table is about the 15 sites with nothing to promote, so
+    # its activation figure is those 15 sites' median (0.2723) -- not the whole
+    # challenger_only cell's (0.2152, which is what cells[].act_median reports).
+    payload = _payload(OP030)
+    cell = next(c for c in payload["cells"] if c["cell"] == "challenger_only")
+    sites = [s for s in payload["sites"] if s["cell"] == "challenger_only"]
+    no_peak = [s["act"] for s in sites if not s["peak_in_radius"]]
+    assert round(quartiles(no_peak)[1], 4) == 0.2723
+    assert cell["act_median"] == 0.2152
+
+
+def _claimed_at_030(payload):
+    """``{(pano, x, y): bool}`` -- is the site's nearest floor peak one the greedy
+    match at 0.30 gave to a different GT? From the committed op_cache, the same
+    source the artifact's peak columns were read from."""
+    floor = cg.load_floor_peaks(payload["split"])
+    rsq = radius_sq_for(payload["radius"])
+    by_pano = {}
+    for s in payload["sites"]:
+        by_pano.setdefault(s["pano"], []).append(s)
+    out = {}
+    for pid, sites in by_pano.items():
+        flags = cg.claimed_by_adjacent(sites, floor.get(pid, []),
+                                       payload["rampnet_op_threshold"], rsq)
+        for s, flag in zip(sites, flags):
+            out[(pid, s["x"], s["y"])] = flag
+    return out
+
+
+def test_the_no_peak_row_is_mostly_a_neighbours_shoulder_not_unpeaked_mass():
+    # docs/model_comparison.md, the "no floor peak in radius" row of the cascade
+    # table. The first write-up read these 15 as "unpeaked heatmap mass
+    # peak_local_max never called a maximum". The artifact's own columns say
+    # otherwise: for 11 of the 15 the nearest floor peak is 1-2 R away (median 35.0
+    # px against R = 22.5), and for 11 the in-window maximum sits on the window edge
+    # (median argmax_off_px 22.4) -- a neighbouring mode's shoulder reaching in, #46
+    # Phase 1's `tail`, which is where class_of puts 12 of the 15. Only 4 have no
+    # floor peak within 2 R, and one of those is the seam site whose peak the
+    # pre-f4c71c8 op_cache dropped.
+    payload = _payload(OP030)
+    r_px = radius_sq_for(payload["radius"]) ** 0.5
+    prof = cg.no_peak_profile(payload["sites"], "challenger_only", r_px)
+    assert prof["n"] == 15
+    assert prof["nearest_peak_px_median"] == 35.0
+    assert prof["argmax_off_px_median"] == 22.4
+    assert prof["argmax_on_edge"] == 11
+    assert prof["peak_within_2r"] == 11
+    assert prof["peak_beyond_2r"] == 4
+    assert prof["classes"] == {"absent": 0, "faint_local": 3, "tail": 12}
+    assert prof["seam"] == 1
+    assert prof["act_median"] == 0.2723
+
+
+def test_the_no_peak_rows_nearest_peaks_are_mostly_claimed_by_an_adjacent_ramp():
+    # ...and that neighbouring peak is, for 11 of the 15, one the matcher already
+    # gave to another GT at 0.30 (7 of them within 2 R) -- the same #130 mechanism
+    # the table's "4 in radius at >= 0.30" row names, so those two rows are one
+    # cause, not two. All 4 of that row are claimed too, which is what "unmatched"
+    # there meant.
+    payload = _payload(OP030)
+    r_px = radius_sq_for(payload["radius"]) ** 0.5
+    claimed = _claimed_at_030(payload)
+    co = [s for s in payload["sites"] if s["cell"] == "challenger_only"]
+    no_peak = [s for s in co if not s["peak_in_radius"]]
+    assert sum(claimed[(s["pano"], s["x"], s["y"])] for s in no_peak) == 11
+    assert sum(claimed[(s["pano"], s["x"], s["y"])] for s in no_peak
+               if s["nearest_peak_px"] <= 2 * r_px) == 7
+    in_r_high = [s for s in co if s["peak_in_radius"] and s["nearest_peak_score"] >= 0.30]
+    assert len(in_r_high) == 4
+    assert all(claimed[(s["pano"], s["x"], s["y"])] for s in in_r_high)
+    # The re-cut of the 38 the doc now prints, exhaustive and disjoint.
+    promotable = sum(1 for s in co if s["peak_in_radius"] and s["nearest_peak_score"] < 0.30)
+    within_2r_unclaimed = sum(1 for s in no_peak if s["nearest_peak_px"] <= 2 * r_px
+                              and not claimed[(s["pano"], s["x"], s["y"])])
+    beyond_2r = sum(1 for s in no_peak if s["nearest_peak_px"] > 2 * r_px)
+    assert (promotable, len(in_r_high) + 7, within_2r_unclaimed, beyond_2r) == (19, 11, 4, 4)
+    assert promotable + 11 + within_2r_unclaimed + beyond_2r == 38
+
+
+def test_the_seam_site_in_the_recovered_cell_has_a_peak_the_op_cache_lacks():
+    # 723487737079243 at x = 0.0069: act 0.946 at 7.4 px from the ramp, centre 0.78,
+    # and the committed op_cache's nearest peak 117 px away. That is the f4c71c8 seam
+    # dropout made concrete -- a regenerated op_cache would list this peak, making
+    # the site a RampNet hit at 0.30 and taking it out of challenger_only (38 -> 37).
+    payload = _payload(OP030)
+    site = next(s for s in payload["sites"]
+                if s["cell"] == "challenger_only" and s["seam"])
+    assert site["pano"] == "723487737079243"
+    assert not site["peak_in_radius"] and site["nearest_peak_px"] > 100
+    assert site["act"] > 0.9 and site["center"] > 0.7 and site["argmax_off_px"] < 10
+
+
+def test_the_committed_nulls_came_from_one_stream_and_say_so():
+    # Both artifacts predate site_rng: their nulls were drawn from one stream in pano
+    # order, so a site carries a different draw in each file (43 of the 53 sites with
+    # a null in both differ, by up to 0.075) while act and nearest_peak_px agree on
+    # every one. A regeneration with per-site seeding moves those values without any
+    # change in the heatmap, which is why neither file records "null_rng".
+    shipped, op030 = _payload(SHIPPED), _payload(OP030)
+    assert "null_rng" not in shipped and "null_rng" not in op030
+    key = lambda s: (s["pano"], s["x"], s["y"])  # noqa: E731
+    a = {key(s): s for s in shipped["sites"]}
+    both = [(a[key(s)], s) for s in op030["sites"]
+            if key(s) in a and a[key(s)]["null_pct"] is not None
+            and s["null_pct"] is not None]
+    assert len(both) == 53
+    differ = [(x, y) for x, y in both if x["null_pct"] != y["null_pct"]]
+    assert len(differ) == 43
+    assert max(abs(x["null_pct"] - y["null_pct"]) for x, y in differ) == pytest.approx(0.075)
+    assert all(x["act"] == y["act"] and x["nearest_peak_px"] == y["nearest_peak_px"]
+               for x, y in both)
+
+
+def test_moving_to_the_recommended_threshold_takes_sixteen_from_the_recovered_cell():
+    # docs/model_comparison.md: RampNet gains 19 hits going 0.55 -> 0.30 (72 -> 53
+    # misses), of which 16 come out of challenger_only and 3 out of neither. The
+    # complementary-gain headline is the challenger_only figure, so it falls by 16.
+    shipped = {c["cell"]: c["n"] for c in _payload(SHIPPED)["cells"]}
+    op030 = {c["cell"]: c["n"] for c in _payload(OP030)["cells"]}
+    assert shipped["challenger_only"] == 54 and op030["challenger_only"] == 38
+    assert shipped["neither"] == 18 and op030["neither"] == 15
+    gained = ((shipped["challenger_only"] + shipped["neither"])
+              - (op030["challenger_only"] + op030["neither"]))
+    assert gained == 19
+    assert shipped["challenger_only"] - op030["challenger_only"] == 16
+    assert shipped["neither"] - op030["neither"] == 3
+
+
+def test_the_seam_exposure_is_one_ramp_in_the_recovered_cell():
+    # The bound on the pre-#132 non-wrapping match: 6 of richmond's 310 GT ramps
+    # straddle the seam, and only 1 of them is in challenger_only.
+    payload = _payload(OP030)
+    assert sum(1 for s in payload["sites"] if s["seam"]) == 6
+    by_cell = {c["cell"]: c["seam"] for c in payload["cells"]}
+    assert by_cell["challenger_only"] == 1
+    assert by_cell["both"] == 5
diff --git a/tests/test_complementarity.py b/tests/test_complementarity.py
new file mode 100644
index 00000000..f8d8442f
--- /dev/null
+++ b/tests/test_complementarity.py
@@ -0,0 +1,351 @@
+"""Unit tests for the complementarity gate (#35, generalized past Gemini in #126).
+
+Pure logic plus two drift guards — no GPU, no ``.model_cache``, no network. Every
+detection these read comes from ``benchmark/model_detections/``, the published export,
+so a clean clone runs them.
+
+What they protect:
+
+* **``matched_gt`` and ``score_pano`` must agree.** This script prints the four
+  complementarity cells (from ``matched_gt``) and the false-positive counts and union
+  P/R/F1 (from ``score_pano``) in one table. ``score_pano`` wraps the 360° seam by
+  default since #132; ``matched_gt`` re-derived the distance inline and did not, which
+  put two matchers in one output. It now calls the shared matcher, and the wrap test
+  below fails if the inline form comes back.
+* **``model_spec`` must reject a mistyped provider.** A bad spec does not raise: it
+  addresses a cache entry nothing ever wrote, and the script reports a model with zero
+  detections, which reads as a missing run.
+* **``compare_args`` must reconstruct the signature ``compare.py`` cached under.**
+  Same silent failure. Checked against the signature recorded inside the published
+  export rather than against a copy of the defaults.
+* **``complementary_null``** is the discount every attributable-gain number in
+  ``docs/model_comparison.md`` is quoted after, so it is checked against cases whose
+  answer is arithmetic rather than measurement.
+* **``partition_cells``** is the one loop behind this script's table, the cascade
+  gate's partition and the regression tests below — the tests call it rather than
+  re-implementing it, so a bug in the threshold filter or the op_cache fallback fails
+  here instead of being copied into a third place.
+* the published 384 richmond column, cell for cell, at the shipped point and at the
+  two op_cache thresholds the doc quotes.
+"""
+import argparse
+import json
+import os
+import sys
+
+import pytest
+
+REPO = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.insert(0, REPO)
+sys.path.insert(0, os.path.join(REPO, "scripts", "analysis"))
+sys.path.insert(0, os.path.join(REPO, "scripts", "model_comparison"))
+
+import complementarity as cx  # noqa: E402
+from compare import DetectionCache, cache_key, load_bundle  # noqa: E402
+from detectors import PROVIDERS, build_detector  # noqa: E402
+from rampnet.detection_eval import (  # noqa: E402
+    PANO_SCALE_X, build_ground_truth, radius_sq_for, score_pano)
+
+RSQ = radius_sq_for()
+R_NORM = RSQ ** 0.5 / PANO_SCALE_X          # match radius in normalized x, ~0.022
+PUBLISHED = os.path.join(REPO, "benchmark", "model_detections",
+                         "mask2former-vistas-curb-cut__richmond.json")
+
+
+def _args(**kw):
+    """The namespace ``compare_args`` reads, with this script's own CLI defaults."""
+    ns = argparse.Namespace(tiling="perspective", radius=0.022,
+                            cache_dir=os.path.join(REPO, ".model_cache"),
+                            vistas_input_size=None, vistas_revision=None)
+    for k, v in kw.items():
+        setattr(ns, k, v)
+    return ns
+
+
+# --------------------------------------------------------------------------- #
+# matched_gt — the same matcher score_pano uses, wrap included
+# --------------------------------------------------------------------------- #
+def test_a_prediction_on_the_ramp_claims_it():
+    assert cx.matched_gt([(0.5, 0.5, 0.9)], [(0.5, 0.5)], RSQ) == {0}
+
+
+def test_a_prediction_out_of_radius_claims_nothing():
+    assert cx.matched_gt([(0.5 + 2 * R_NORM, 0.5, 0.9)], [(0.5, 0.5)], RSQ) == set()
+
+
+def test_two_predictions_cannot_both_claim_one_ramp():
+    # The 1:1 rule: the second is a false positive, not a second hit, so the covered
+    # set stays one ramp. score_pano counts it as an FP on the same input.
+    covered = cx.matched_gt([(0.5, 0.5, 0.9), (0.5, 0.5, 0.8)], [(0.5, 0.5)], RSQ)
+    assert covered == {0}
+
+
+def test_the_highest_confidence_prediction_claims_first():
+    # Two ramps, both in range of both predictions: the order decides which pairs with
+    # which, and score_pano orders by confidence. Both end up covered either way, so
+    # the guard is that the ordering path runs at all rather than raising.
+    gt = [(0.5, 0.5), (0.5 + R_NORM / 2, 0.5)]
+    assert cx.matched_gt([(0.5, 0.5, 0.2), (0.5 + R_NORM / 2, 0.5, 0.9)], gt, RSQ) == {0, 1}
+
+
+def test_predictions_without_confidence_are_matched_in_input_order():
+    # The chat VLMs emit boxes with no score. Nothing to sort by, so input order.
+    assert cx.matched_gt([(0.5, 0.5, None)], [(0.5, 0.5)], RSQ) == {0}
+
+
+def test_a_prediction_across_the_seam_claims_the_ramp():
+    # x=0.998 and x=0.002 are ~4 px apart the short way round and ~1020 px apart the
+    # long way. score_pano wraps (#132); an inline non-wrapping distance here would
+    # score this as a miss AND as a false positive in the same table.
+    assert cx.matched_gt([(0.998, 0.5, 0.9)], [(0.002, 0.5)], RSQ) == {0}
+
+
+def test_the_cells_and_the_fp_counts_come_from_one_matcher():
+    # The seam case, read through both halves of the script's output: matched_gt says
+    # the ramp is covered, so score_pano must call the same prediction a true positive
+    # rather than a false one.
+    gt = build_ground_truth([{"x_normalized": 0.002, "y_normalized": 0.5,
+                              "confidence": 0.9}], [True], [], True)
+    preds = [(0.998, 0.5, 0.9)]
+    assert cx.matched_gt(preds, gt.gt_points, RSQ) == {0}
+    assert score_pano(preds, gt, RSQ).fp == 0
+
+
+def _one_pano(rampnet_x, challenger_x):
+    """A single recall-eligible pano with one GT ramp at x=0.5, RampNet's shipped
+    detection at ``rampnet_x`` and the challenger's at ``challenger_x``."""
+    records = {"p": {"detections": [{"x_normalized": rampnet_x, "y_normalized": 0.5,
+                                     "confidence": 0.9}]}}
+    verdicts = {"p": {"dets": [False], "missed": [{"x": 0.5, "y": 0.5}],
+                      "no_missed": False}}
+    return records, verdicts, {"p": [(challenger_x, 0.5, 0.9)]}
+
+
+def test_a_non_default_radius_reaches_the_cells_and_the_fp_counts_alike():
+    # Both models' detections sit 1.5 default radii from the ramp. At the default
+    # radius each is a miss AND a false positive; at twice the radius each is a hit
+    # and no false positive. `--radius` used to reach matched_gt only, so the cells
+    # moved to "both" while score_pano, still at its default, kept billing two FPs.
+    records, verdicts, challenger = _one_pano(0.5 + 1.5 * R_NORM, 0.5 - 1.5 * R_NORM)
+    tight = cx.partition_cells(records, verdicts, challenger.get, RSQ)
+    assert tight.counts["neither"] == 1 and (tight.r_fp, tight.c_fp) == (1, 1)
+    wide = cx.partition_cells(records, verdicts, challenger.get, radius_sq_for(0.044))
+    assert wide.counts["both"] == 1 and (wide.r_fp, wide.c_fp) == (0, 0)
+
+
+# --------------------------------------------------------------------------- #
+# partition_cells — the loop three places used to carry a copy of
+# --------------------------------------------------------------------------- #
+def test_the_threshold_filters_the_floor_peaks_and_nothing_else():
+    records, verdicts, challenger = _one_pano(0.5, 0.5)
+    floor = {"p": [(0.5, 0.5, 0.20)]}                       # a peak below 0.30
+    at_030 = cx.partition_cells(records, verdicts, challenger.get, RSQ, 0.30, floor)
+    at_005 = cx.partition_cells(records, verdicts, challenger.get, RSQ, 0.05, floor)
+    assert at_030.counts["challenger_only"] == 1
+    assert at_005.counts["both"] == 1
+    # The shipped detection in records.jsonl is not consulted under the flag.
+    assert at_030.sites == [{"pano": "p", "x": 0.5, "y": 0.5, "cell": "challenger_only"}]
+
+
+def test_the_threshold_and_the_floor_peaks_go_together():
+    records, verdicts, challenger = _one_pano(0.5, 0.5)
+    with pytest.raises(ValueError):
+        cx.partition_cells(records, verdicts, challenger.get, RSQ, 0.30, None)
+    with pytest.raises(ValueError):
+        cx.partition_cells(records, verdicts, challenger.get, RSQ, None, {"p": []})
+
+
+def test_a_pano_the_challenger_never_cached_is_skipped_and_counted():
+    records, verdicts, _ = _one_pano(0.5, 0.5)
+    part = cx.partition_cells(records, verdicts, lambda pid: None, RSQ)
+    assert part.missing == 1 and part.panos == 0 and sum(part.counts.values()) == 0
+
+
+def test_a_pano_missing_from_the_op_cache_is_counted_not_absorbed():
+    # Under --rampnet-op-threshold a pano the op_cache does not list scores as
+    # RampNet-blank: every GT ramp on it becomes a miss. It used to do so silently in
+    # this script (cascade_gate.py warned; this one did not).
+    records, verdicts, challenger = _one_pano(0.5, 0.5)
+    part = cx.partition_cells(records, verdicts, challenger.get, RSQ, 0.30, {})
+    assert part.no_floor == 1
+    assert part.counts["challenger_only"] == 1          # RampNet's shipped hit is gone
+    assert "1 pano(s) are absent" in cx.floor_gap_warning(part.no_floor, "richmond")
+    assert cx.floor_gap_warning(0, "richmond") is None
+
+
+def test_the_missed_gt_handed_to_the_null_is_what_rampnet_missed():
+    records, verdicts, challenger = _one_pano(0.5 + 2 * R_NORM, 0.5)
+    part = cx.partition_cells(records, verdicts, challenger.get, RSQ)
+    assert part.shift_rows == [([(0.5, 0.5, 0.9)], [(0.5, 0.5)])]
+
+
+def test_a_split_without_an_op_cache_exits_with_a_message_not_a_traceback(
+        tmp_path, monkeypatch):
+    # --rampnet-op-threshold on a split whose op_cache is missing used to be a raw
+    # FileNotFoundError. cascade_gate.py already exits with a message; this mirrors it.
+    monkeypatch.setattr(cx, "CACHE_DIR", str(tmp_path / "no_op_cache"))
+    monkeypatch.setattr(sys, "argv", [
+        "complementarity.py", "vistas:curb-cut", "richmond",
+        "--rampnet-op-threshold", "0.3", "--cache-dir", str(tmp_path / "no_model_cache")])
+    with pytest.raises(SystemExit) as e:
+        cx.main()
+    assert "op_cache" in str(e.value) and "richmond" in str(e.value)
+
+
+# --------------------------------------------------------------------------- #
+# model_spec — the legacy positional form, and the typo that used to be silent
+# --------------------------------------------------------------------------- #
+def test_a_bare_provider_uses_that_provider_default_model():
+    assert cx.model_spec("vistas") == ("vistas", None)
+
+
+def test_provider_colon_model_id_is_passed_through():
+    assert cx.model_spec("vistas:curb-cut") == ("vistas", "curb-cut")
+    assert cx.model_spec("gemini:gemini-3.6-flash") == ("gemini", "gemini-3.6-flash")
+
+
+def test_a_bare_non_provider_token_is_still_a_gemini_model_id():
+    # The #35 gate's committed invocation. Keep it working.
+    assert cx.model_spec("gemini-3.1-pro-preview") == ("gemini", "gemini-3.1-pro-preview")
+
+
+def test_an_unknown_provider_with_a_colon_is_rejected():
+    # Used to be read as the Gemini model id "foo:bar", which builds a detector whose
+    # signature nothing cached: the run then reports zero detections instead of a typo.
+    with pytest.raises(SystemExit) as e:
+        cx.model_spec("foo:bar")
+    assert "foo" in str(e.value)
+
+
+def test_every_provider_name_survives_a_round_trip():
+    for provider in PROVIDERS:
+        assert cx.model_spec(provider) == (provider, None)
+
+
+# --------------------------------------------------------------------------- #
+# compare_args — the cache signature this script has to reconstruct
+# --------------------------------------------------------------------------- #
+def test_the_vistas_signature_matches_the_published_export():
+    # The published richmond arm records the signature it was cached under. If
+    # compare_args drifts from compare.py's defaults, this reconstruction stops
+    # matching and every cache lookup silently misses.
+    published = json.load(open(PUBLISHED, encoding="utf-8"))
+    label, det = build_detector("vistas", "curb-cut", {}, cx.compare_args(_args()))
+    assert label == published["model"]
+    assert det.signature() == published["signature"]
+
+
+def test_the_input_size_override_addresses_a_different_cache_entry():
+    published = json.load(open(PUBLISHED, encoding="utf-8"))
+    _, parity = build_detector("vistas", "curb-cut", {},
+                               cx.compare_args(_args(vistas_input_size=[1024, 1024])))
+    sig = parity.signature()
+    assert sig["input_size"] == [1024, 1024]
+    assert sig != published["signature"]
+    # Deviation-only: the key the 384 arm was paid for is untouched by the flag existing.
+    assert "input_size" not in published["signature"]
+    assert (cache_key("mask2former-vistas-curb-cut", sig, "richmond", "p")
+            != cache_key("mask2former-vistas-curb-cut", published["signature"],
+                         "richmond", "p"))
+
+
+# --------------------------------------------------------------------------- #
+# complementary_null — the chance discount every "attributable" number is quoted after
+# --------------------------------------------------------------------------- #
+def _row(pred_xy, missed_xy):
+    return ([(pred_xy[0], pred_xy[1], 1.0)], [missed_xy])
+
+
+def test_no_missed_ramps_means_no_null():
+    # Nothing for the challenger to recover, so there is no coincidence rate to report.
+    rows = [([(0.2, 0.2, 1.0)], []), ([(0.5, 0.5, 1.0)], [])]
+    assert cx.complementary_null(rows, RSQ) == (0.0, 0.0)
+
+
+def test_one_pano_has_no_shift_to_take():
+    # Every shift is the identity with n=1, so there is nothing to measure.
+    assert cx.complementary_null([_row((0.2, 0.2), (0.2, 0.2))], RSQ) == (0.0, 0.0)
+
+
+def test_boxes_that_never_line_up_give_a_zero_null():
+    rows = [_row((0.1, 0.1), (0.1, 0.1)),
+            _row((0.5, 0.5), (0.5, 0.5)),
+            _row((0.9, 0.9), (0.9, 0.9))]
+    assert cx.complementary_null(rows, RSQ) == (0.0, 0.0)
+
+
+def test_identical_boxes_on_every_pano_give_a_null_of_one():
+    # The degenerate high-density case: every shift matches every missed ramp, so all
+    # of the "recovery" is what the radius gives away.
+    rows = [_row((0.3, 0.3), (0.3, 0.3)) for _ in range(3)]
+    assert cx.complementary_null(rows, RSQ) == (1.0, 1.0)
+
+
+def test_the_null_averages_over_every_non_identity_shift():
+    # 3 panos, 3 missed ramps, so shifts k=1 and k=2. k=1 lands 2 of 3 (panos 0 and 2
+    # both miss a ramp at (0.2, 0.2), and panos 1 and 0 both predict there); k=2 lands
+    # 1 of 3. Mean 0.5, worst shift 2/3.
+    rows = [_row((0.2, 0.2), (0.2, 0.2)),
+            _row((0.2, 0.2), (0.7, 0.7)),
+            _row((0.9, 0.1), (0.2, 0.2))]
+    mean, worst = cx.complementary_null(rows, RSQ)
+    assert mean == pytest.approx(0.5)
+    assert worst == pytest.approx(2 / 3)
+
+
+# --------------------------------------------------------------------------- #
+# regression — the published 384 richmond column, read from committed files only
+# --------------------------------------------------------------------------- #
+def _published_cells(tmp_path, rampnet_op_threshold=None):
+    """The four cells for the published Vistas 384 arm on richmond.
+
+    Rebuilds a ``.model_cache``-shaped directory from the published export and reads it
+    back through ``DetectionCache``/``cache_key``, i.e. the path the script itself
+    takes. That is deliberate: the lookup only succeeds if the signature reconstructed
+    from ``compare_args`` is the one the export was written under. The partition is
+    the script's own ``partition_cells``, not a copy of it, so the threshold filter
+    and the op_cache fallback are under test here too.
+    """
+    published = json.load(open(PUBLISHED, encoding="utf-8"))
+    cache = DetectionCache(str(tmp_path / "cache"))
+    for pid, points in published["detections"].items():
+        cache.put(cache_key(published["model"], published["signature"],
+                            published["city"], pid), points)
+
+    label, det = build_detector("vistas", "curb-cut", {}, cx.compare_args(_args()))
+    sig = det.signature()
+    records, verdicts, _ = load_bundle(os.path.join(REPO, "benchmark", "richmond"))
+    floor = (cx.load_floor_peaks("richmond") if rampnet_op_threshold is not None
+             else None)
+    part = cx.partition_cells(
+        records, verdicts, lambda pid: cache.get(cache_key(label, sig, "richmond", pid)),
+        RSQ, rampnet_op_threshold, floor)
+    assert part.missing == 0, "signature drifted from the published export"
+    assert part.no_floor == 0
+    return part.counts
+
+
+def test_the_published_384_column_reproduces(tmp_path):
+    # docs/model_comparison.md, "Complementarity" table, the vistas @384 column.
+    # RampNet's side is the bundle's shipped detections (>= 0.5519 on richmond).
+    assert _published_cells(tmp_path) == {"both": 194, "rampnet_only": 44,
+                                          "challenger_only": 22, "neither": 50}
+
+
+def test_the_384_column_adds_up_to_richmond_s_recall_eligible_ground_truth(tmp_path):
+    counts = _published_cells(tmp_path)
+    assert sum(counts.values()) == 310
+    # 22 of RampNet's 72 misses recovered, 31%; and 216 of 310 for the challenger,
+    # which is the recall its published row reports.
+    assert counts["challenger_only"] + counts["neither"] == 72
+    assert counts["both"] + counts["challenger_only"] == 216
+
+
+def test_the_384_column_at_the_two_op_cache_thresholds_reproduces(tmp_path):
+    # docs/model_comparison.md, the seam-exposure table: the published 384 arm with
+    # RampNet re-sourced from analysis_out/op_cache/richmond.json at the recommended
+    # 0.30 and at the 0.05 floor. Both sides committed, so a clean clone checks them.
+    assert _published_cells(tmp_path, 0.30) == {"both": 202, "rampnet_only": 55,
+                                                "challenger_only": 14, "neither": 39}
+    assert _published_cells(tmp_path, 0.05) == {"both": 213, "rampnet_only": 66,
+                                                "challenger_only": 3, "neither": 28}