Datasets:

govtech
/

municipalbench-full

These studies examine how scoring rules, prompts and repeated inference affect benchmark results. Semantic relaxation uses the released 11,534 images. All other studies use older frozen ground truth and populations described below; their scores must not be read as results on the current release.

Historical results are aggregate evidence. Their real-control images and per-image records are not distributed, so the release cannot reproduce those experiments end to end. The JSON files retain the original scores and source fingerprints. This guide explains their populations, findings and limitations.

Semantic relaxation

Question: How much does exact class naming affect the measured score? Some classes describe related defects in different settings, such as faulty estate lights and faulty street lights. The fixed BGE-large-en-v1.5 policy accepts 44 undirected class pairs with cosine similarity strictly above 0.80. It uses no transitive closure or manual overrides. These are algorithmically accepted pairs, not expert-confirmed equivalences.

The analysis keeps predictions fixed, matches exact labels first, then matches remaining eligible pairs one to one. Localisation also requires the selected intersection-over-union (IoU) threshold. On all 11,534 shared-valid images, classification micro F2 changes as follows:

Model	Exact	Relaxed
YOLO-World	0.1193	0.1490
Gemma 4 12B	0.3764	0.4131
GLM 5.3 Flash	0.6264	0.6679
GPT-5.6 Luna	0.5937	0.6391
Muse Glimmer 30B	0.5990	0.6424
Gemini 3.8 Flash	0.6710	0.7106

The increases reflect changed acceptance rules, not improved model predictions. Exact scoring remains primary. The separate mapping-opportunity analysis selects images whose gold contains a class with an accepted alternative; it scores all targets and predictions on those images. Its denominator differs from the full population. Sensitivity varies IoU from 0.10 to 0.90 and semantic acceptance from 0.50 to 1.00 on the same retained detections, without confidence filtering.

In semantic results, models[].gold_scopes.all.common contains full-population comparisons, mapping_opportunity contains the conditional analysis, and sensitivity contains the threshold grids. The frozen matrix and scoring code are supplied; the primary scoring example checks exact scores only.

Historical model comparisons

The preliminary benchmark compared ten models on 302 images; the expanded benchmark compared thirteen on 1,200. All models within each published comparison have valid predictions for its full population. The expanded study adds negative controls, allowing false alarms on clean images to be measured.

Benchmark	Synthetic images	Undistributed real controls	Shared valid
Preliminary	301	1	302
Expanded	1,093	107	1,200

These are separate experiments with older gold, different composition and model rosters. Differences between their scores are not paired improvements on the current dataset. See the 302-image results and 1,200-image results. experiment describes each cohort; models[].scopes contains valid/common scores, and the adjacent CSV files give compact metric tables.

Gemma prompt grouping

Question: Does asking about fewer classes in each prompt improve detection? Gemma 4 12B receives all 97 classes in one prompt, five disjoint class bundles, or thirteen domain prompts. Every image receives every prompt; gold labels do not select which prompts are used. The one-pass baseline is reused.

Five-pass and domain-pass each have 1,198 valid responses out of 1,200, leaving 1,196 shared-valid images across all three strategies:

Strategy	Classification F2	Localisation F2 at IoU 0.50
One pass	0.4071	0.1645
Five passes	0.4668	0.1129
Thirteen domain passes	0.4958	0.1331

More passes increase classification recall and F2, but reduce localisation F2. They also increase false alarms on the 200 shared-valid negative images: 6 for one pass, 20 for five passes and 19 for domain passes. Each configuration was observed once. Per-pass output caps mean that total output capacity changes with the number of passes; this does not isolate prompt grouping alone.

Gemma strategy results record the population in study, prompts in strategies[].configuration, and these scores in strategies[].scopes.common. Per-model valid scores use their own populations and can differ from this table.

Original-settings repeatability

Question: Does the same deployed configuration reproduce its outputs? The original run and two additional runs cover the same 1,200 images for each of four models, with all responses valid. Gemma uses greedy decoding; GLM requests temperature zero; GPT-5.6 Luna uses its provider default. YOLO retains its original inference settings.

Model	Identical class sets across all three runs
YOLO-World	100.0%
Gemma 4 12B	100.0%
GLM 5.3 Flash	40.08%
GPT-5.6 Luna	57.00%

Gemma and YOLO also reproduce their boxes exactly. These are agreement rates, not accuracy: repeating an error still counts as agreement. Hosted provider routing varied, so this measures the deployed configuration rather than isolating intrinsic sampling variance. Three runs provide descriptive evidence, not a precise estimate of long-run variability.

Repeatability results contain models[].pairs for each run pair, all_three for three-way agreement, and variation for F2 ranges and sample standard deviations. Exact-box agreement includes matching empty predictions; pairwise box F1 instead allows same-class spatial matches at IoU 0.50. Performance is scored against gold, separately from agreement between runs.

Sampling at temperature 0.3

Question: How stable are predictions when sampling is explicitly enabled? Gemma 4 12B, GLM 5.3 Flash and Llama 4 Maverick each have three new runs at temperature 0.3, top-p 1.0 and top-k 0, with seeds 24092401–24092403. The image population, taxonomy and one-pass prompt are fixed. Hosted providers are pinned for this study; a requested seed does not guarantee identical hosted execution.

Model	Shared valid	Same classes in all three runs	Classification F2 range
Gemma 4 12B	1,198	75.0%	0.3991–0.4058
GLM 5.3 Flash	1,200	42.5%	0.5848–0.5969
Llama 4 Maverick	1,200	77.0%	0.4822–0.4877

Individual predictions vary more than aggregate F2. Gemma has one invalid response in each of its first two runs, on different images; these failures explain its smaller common population. Pairwise comparisons can therefore have different denominators. Old and new hosted deployments differ in provider selection as well as sampling settings, so their difference is not an isolated temperature effect. One temperature and three seeds do not establish a general temperature-response curve or a causal model-size effect.

Sampling results use the same runs, pairs, all_three and variation structure as the repeatability study; sampling records the settings.

Parking prompt definitions

Question: Do explicit annotation definitions help the model recognise parking violations? GLM 5.3 Flash is evaluated on 47 parking-positive images from the older 1,200-image cohort. Each of U62–U64 has 16 positive images, with one image belonging to two classes. Both arms receive all 97 categories; scoring considers only the three parking classes. The comparison reuses the original predictions and adds a new arm with class definitions, examples and exclusions, without image-specific answers.

Task	Original prompt F2	With definitions F2
Classification	0.1515	0.6444
Localisation at IoU 0.50	0.1126	0.5622

Both arms have 47 valid responses. The improvement is specific to this selected subset. There are no clean-scene parking controls, so the study cannot measure false-alarm rates on those scenes. Prompt length also changes, and single runs on different dates do not control provider drift or run-to-run variation.

Parking results contain arms[].scores, classes for support, prompts for the actual text, and delta_f2 for absolute score differences. Scores range from 0 to 1: 0.4929 is a 49.29-percentage-point increase, not a 49.29% relative increase.