diff --git a/.gitignore b/.gitignore
index 37fb92d5..d8e4814f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -90,6 +90,9 @@ analysis_out/op/*
 !analysis_out/crop_window_eval_*.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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+      "y": 0.5546875
+    },
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+      "y": 0.5078125
+    },
+    {
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+      "center": 0.092798,
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+      "nearest_peak_score": 0.09808053821325302,
+      "null_med": 0.005705,
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+      "null_pct": 0.84,
+      "pano": "996959015535106",
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+      "seam": false,
+      "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 e1fe7a91..087ccc0f 100644
--- a/docs/model_comparison.md
+++ b/docs/model_comparison.md
@@ -1175,6 +1175,14 @@ 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
@@ -1190,10 +1198,10 @@ 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
@@ -1282,6 +1290,260 @@ 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 earns its place.** 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. The parity detections are **deliberately not
+published**: `--vistas-input-size` does not change the arm's label, so exporting them would
+overwrite `mask2former-vistas-curb-cut__richmond.json` — publishing them needs a distinct
+published name (the `--publish-as` pattern from #123), which was out of scope here. And this is
+still **richmond only**.
+
+#### 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), on the same cached detections, scoring-side only:
+
+| | vistas @384 | **vistas @1024 (parity)** |
+|---|---:|---:|
+| found by BOTH | 194 | 220 |
+| rampnet ONLY | 44 | 18 |
+| **challenger ONLY** (rampnet-miss ∩ hit) | 21 | **54** |
+| found by NEITHER | 51 | **18** |
+| of rampnet's 72 misses, recovered | 21 (29%) | **54 (75%)** |
+| null on that subset (same boxes, wrong pano) | 0.090 | 0.143 |
+| **attributable after the null** | **~15** | **~44** |
+| oracle-union recall | 0.835 | **0.942** |
+| boxes/pano · above chance (`null_recall.py`) | 4.5 · 0.657 | 6.2 · **0.864** |
+
+**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 51 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 is nonetheless dead, 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 | 0.835 | 0.585 |
+
+The economics are the whole story: 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 |
+
+**So ~14 of the ramps the challenger got credit for recovering are ramps RampNet already has at
+the operating point we recommend — the shipped threshold was discarding them.** The deployable
+complementary gain is **~30, not ~44**. The recovery *rate* barely moves (75% → 72%), which is the
+honest way to read it: the challenger is not preferentially finding the easy sub-threshold ones,
+there are simply fewer misses to find. And a naive union stays dead 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).
+
+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 to promote. `act` is 0.215 median — unpeaked heatmap mass `peak_local_max` never called a maximum. |
+
+**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. Two-fifths of the recoverable set has no
+peak to raise, and a further tenth is a matching bug wearing a threshold costume.
+
+**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.
+
+**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, stated because these numbers predate the #132 seam fixes.** 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. Two consequences, both bounded by measurement rather than
+argued away:
+
+* The greedy match used here does not wrap. `score_pano`'s own docstring records that wrapping
+  *"moves no metric on any committed split"* while #130's duplicate ground truth masks it, so the
+  aggregate P/R/F1 are unaffected — but a cell assignment is finer-grained than an aggregate.
+* `analysis_out/op_cache/richmond.json` is **unchanged by those commits**, i.e. it was not
+  regenerated after the `peaks_to_dets` fix, 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: 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 headline is one ramp in 38, and no conclusion
+here turns on it. Re-running on a post-#140 main is the clean way to retire the caveat rather than
+bound it.
+
+##### Reproducing it
+
+```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
+python scripts/model_comparison/compare.py benchmark/richmond --models vistas:curb-cut
+
+# 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
+```
+
+Cost, measured rather than guessed, because the estimate going in was 3–4× and it was wrong in
+the cheap direction: **the full 124-pano parity run takes 3m38s** on one A40, and 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. 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
+wearing 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..155926ed 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. |
diff --git a/scripts/analysis/cascade_gate.py b/scripts/analysis/cascade_gate.py
new file mode 100644
index 00000000..f24ebc47
--- /dev/null
+++ b/scripts/analysis/cascade_gate.py
@@ -0,0 +1,335 @@
+"""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 is nonetheless dead — 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 load-bearing column 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** — ``act`` is unpeaked heatmap mass that
+  ``peak_local_max`` never called a local maximum. A threshold prior has nothing to
+  promote, because promotion operates on peaks.
+
+``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 build_ground_truth, radius_sq_for   # noqa: E402
+from compare import load_bundle, DetectionCache, cache_key             # noqa: E402
+from detectors import build_detector                                   # noqa: E402
+from complementarity import matched_gt, model_spec, compare_args       # noqa: E402
+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
+from operating_point_curve import CACHE_DIR, read_cache                # noqa: E402
+
+CELLS = ("both", "rampnet_only", "challenger_only", "neither")
+#: Cells where RampNet did NOT find the ramp -- the only ones a null is meaningful for.
+MISS_CELLS = ("challenger_only", "neither")
+
+
+def cell_of(rampnet_hit, challenger_hit):
+    if rampnet_hit:
+        return "both" if challenger_hit else "rampnet_only"
+    return "challenger_only" if challenger_hit else "neither"
+
+
+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. 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 = {}, "op_cache"
+    try:
+        cached, _ = read_cache(os.path.join(CACHE_DIR, f"{args.split}.json"))
+        for pd in cached:
+            floor_peaks[pd["pano"]] = pd["preds"]
+    except (OSError, ValueError, KeyError):
+        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 ------------------
+    sites, 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
+        cp = cache.get(cache_key(label, sig, args.split, pid))
+        if cp is None:
+            missing += 1
+            continue
+        if args.rampnet_op_threshold is not None:
+            rp = [q for q in floor_peaks.get(pid, [])
+                  if q[2] >= args.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):
+            sites.append({"pano": pid, "x": gx, "y": gy,
+                          "cell": cell_of(i in mr, i in mc)})
+    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)
+    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]
+
+    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)
+
+    rng = random.Random(NULL_SEED)
+    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)
+        preds = floor_peaks.get(pid) or [
+            (d["x_normalized"], d["y_normalized"], d["confidence"])
+            for d in records[pid]["detections"]]
+        for s in by_pano[pid]:
+            act, off_px, center = site_profile(heat, s["x"], s["y"], radius_sq)
+            npx, nscore = nearest_peak(preds, s["x"], s["y"])
+            r_px = radius_sq ** 0.5
+            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),
+                   "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.
+            if s["cell"] in MISS_CELLS:
+                a, pct, med, p95 = null_percentile(heat, s["x"], s["y"], rng,
+                                                   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 was extracted there even at the 0.05 floor, so a")
+    print("     threshold prior has no peak to promote. 'act' can still be non-zero: that is")
+    print("     unpeaked heatmap mass, which peak_local_max did not call a local maximum.")
+
+    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, "n_sites": len(rows),
+                   "n_panos": len(panos), "skipped_sites": skipped,
+                   "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..905124c8 100644
--- a/scripts/analysis/complementarity.py
+++ b/scripts/analysis/complementarity.py
@@ -1,40 +1,73 @@
-"""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" a challenger gets credit for recovering are ramps RampNet already has and the
+shipped threshold is discarding. ``--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
+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 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
 
-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"
-
-RSQ = radius_sq_for()
-
-def matched_gt(preds, gt_points):
+def matched_gt(preds, gt_points, radius_sq):
     """Greedy 1:1 match (mirrors score_pano); return the set of GT indices covered."""
     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)))
+    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
+        pxn, pyn = _xy(preds[i])
+        px, py = pxn * PANO_SCALE_X, pyn * PANO_SCALE_Y
+        best_k, best = -1, radius_sq
         for k, (gx, gy) in enumerate(gt_points):
             if claimed[k]:
                 continue
@@ -42,46 +75,207 @@ def matched_gt(preds, gt_points):
             if d < best:
                 best, best_k = d, k
         if best_k >= 0:
-            claimed[best_k] = True; hit.add(best_k)
+            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)")
+
+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.
+
+    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."""
+    provider, model_id = parse_model_spec(token)
+    if provider in PROVIDERS:
+        return provider, model_id
+    # Legacy positional form: a bare model id meant gemini. Keep it working --
+    # reading it as a provider would raise on strings that used to be valid.
+    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:
+        cached, _ = read_cache(os.path.join(CACHE_DIR, f"{args.split}.json"))
+        floor_peaks = {pd["pano"]: pd["preds"] for pd in cached}
+        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")
+
+    n = both = r_only = c_only = neither = 0
+    r_fp = c_fp = 0
+    panos = missing = 0
+    # (challenger preds, GT points rampnet MISSED) per pano, for the null below
+    shift_rows = []
+    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 = cache.get(cache_key(label, sig, args.split, pid))
+        if cp is None:
+            missing += 1
+            continue
+        if floor_peaks is not None:
+            rp = [p for p in floor_peaks.get(pid, [])
+                  if p[2] >= args.rampnet_op_threshold]
+        else:
+            rp = [(d["x_normalized"], d["y_normalized"], d["confidence"])
+                  for d in records[pid]["detections"]]
+        mr, mc = matched_gt(rp, gt.gt_points, radius_sq), matched_gt(cp, gt.gt_points, radius_sq)
+        for i in range(len(gt.gt_points)):
+            r, c = i in mr, i in mc
+            both += r and c
+            r_only += r and not c
+            c_only += c and not r
+            neither += not r and not c
+        n += len(gt.gt_points)
+        r_fp += score_pano(rp, gt).fp
+        c_fp += score_pano(cp, gt).fp
+        shift_rows.append((cp, [g for i, g in enumerate(gt.gt_points) if i not in mr]))
+        panos += 1
+
+    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).")
+
+    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)