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+# Visual Genome hidden-permutation protocol
+
+## Purpose
+
+The Flickr30k experiment compared two independent samples from a common
+distribution. It did not present the alignment method with two empirical
+graphs over the same latent node set.
+
+This protocol instead constructs two independently indexed observations of the
+same set of real scenes:
+
+\[
+\mathcal D_V=\{V_i\}_{i=1}^N,\qquad
+\mathcal D_T=\{T_{\pi(i)}\}_{i=1}^N,
+\]
+
+where `pi` is sampled once, kept out of all training inputs, and retained only
+in a private evaluation file.
+
+## Closure tiers
+
+The experiment treats linguistic information outside the image-observable
+sigma-algebra as a controlled nuisance variable.
+
+1. `region_closed`: a fixed-size bag of localized image crops and their human
+   region descriptions. Region order and scene identity are independently
+   permuted on the two sides.
+2. `visible_relations`: region descriptions plus object attributes and
+   subject-predicate-object statements that are explicitly visible.
+3. `qa_expanded`: the visible bundle plus Visual Genome/GQA questions and
+   answers. This may introduce inference and annotation priors.
+4. `open_context`: later experiments add page-level or encyclopedic text. This
+   tier is intentionally excluded from the initial identifiability test.
+
+The core claim must work at tier 1 before higher tiers are useful.
+
+## Leakage controls
+
+- Every scene receives the same number of visual and textual views.
+- Opaque visual and text node IDs are sampled independently.
+- Text records do not contain image IDs, URLs, coordinates, or region IDs.
+- Vision records do not contain phrases, object names, predicates, answers, or
+  WordNet identifiers.
+- Text order is independently permuted within every bundle.
+- Scaling subsets are nested but independently shuffled on the two sides.
+- The ground-truth permutation is stored separately and is loaded only by
+  evaluation commands.
+
+## Representations
+
+The first pass uses frozen, unimodal backbones:
+
+- DINOv2 features for a full image and a fixed bag of localized crops;
+- multiple Qwen hidden-state views for the corresponding descriptions;
+- no CLIP/SigLIP or VLM-pretrained vision tower.
+
+Per-scene inputs remain sets rather than being averaged immediately. The
+alignment objective can therefore use:
+
+- the within-scene region relation graph;
+- the between-scene graph;
+- multi-scale diffusion and return-probability signatures;
+- higher-order or operator constraints.
+
+## Scaling axes
+
+\[
+N\in\{5k,20k,50k,100k\}
+\]
+
+\[
+\text{views per scene}\in\{1,4,16,32\}
+\]
+
+\[
+\text{closure tier}\in
+\{\text{region-closed},\text{visible-relations},\text{QA-expanded}\}.
+\]
+
+The primary diagnostic is the rank of the true hidden match as these three
+axes grow. Frozen-LLM bridge training begins only after the structural rank
+shows a reproducible scaling trend.
+