ATWL-side analysis of the designed workflows

25 formal features, syntax checker and comparison between conditions (20 ATWL designs)

The 20 designs that were produced in ATWL (conditions C2, C3, C6, C6++ and C7; two tasks; two models) were analysed with the 25 binary features of the paper (Section VI-C, Table XI), which are computed deterministically from the ATWL text by a released script, with the ATWL syntax checker, and with a few size descriptors. The same features were computed for the 54 workflows of the library (as of 2 October 2026), so the designs can be placed against the library. C1 and C4 produced no ATWL and C5 has no separate representation, so they are not covered here; the prose designs of C1 and C4 are covered by the coding analysis.

The features describe what an ATWL description makes explicit. They do not measure the quality of a design, and they cannot compare designs with and without ATWL.

Summary

The 25 features

IDFamilyFeatureReading
E01Emergent transitionsgenerate-knowledge -> define-unitdecision re-specifies units
E02Emergent transitionsgenerate-knowledge -> characterisedecision re-specifies features
E03Emergent transitionsvisualise -> generate-knowledge (unmediated jump)decision or specification straight from a view (consumer has no pattern or model input)
E04Emergent transitionsvisualise+abstract+contextualise -> define-unitunits are defined from a view, its interpretation, or the arrangement it is based on
E05Emergent transitionsbuild-model -> characterisemodel output is used as features
E06Emergent transitionsgenerate-knowledge -> visualisedecision re-specifies a view
E07Emergent transitionsgenerate-knowledge -> contextualisedecision re-specifies the arrangement
E08Emergent transitionsgenerate-knowledge -> build-modeldecision re-specifies the model
E09Emergent transitionsgenerate-knowledge -> abstractknowledge or specification steers what is interpreted (guided abstraction)
E10Emergent transitionsassess -> define-unitjudgement of results redefines the working set
E11Emergent transitionsbuild-model -> define-unitmodel output defines units (rules, sampled patches)
I01Iteration targetsunits redefined in a loopan assign in a loop updates an entities artifact
I02Iteration targetsfeatures redefined in a loopan assign in a loop updates a feature artifact
I03Iteration targetsarrangement redefined in a loopan assign in a loop updates an arrangement artifact
I04Iteration targetsmodel refined in a loopan assign in a loop updates a model artifact
I05Iteration targetsspecification revised in a loopan assign in a loop updates a specification artifact
I06Iteration targetspatterns revised in a loopan assign in a loop updates a pattern artifact
I07Iteration targetsknowledge accumulated in a loopan assign in a loop updates a knowledge artifact
T01Structuretwo or more given datasetstwo or more given entities artifacts
T02Structureunits that are groups or clustersa derived entities artifact with internal structure group (cluster, container)
T03Structureseveral views used togethera human or hybrid abstract or assess takes two or more visualisation artifacts as inputs
T04Structuretwo or more sequential loopstwo or more top-level (non-nested) loops
H01Human activitycontains a hybrid transformat least one transform with actor hybrid (human-guided computation)
H02Human activitya priori configurationa human or hybrid generate-knowledge outside any loop produces a specification and has no assess, visualise or abstract predecessor (the analyst configures the analysis from the data and prior knowledge)
H03Human activityhuman provides initial knowledgea given knowledge artifact, or a human or hybrid generate-knowledge producing knowledge without an assess, visualise or abstract predecessor

Definitions and library support are in selected_features_2026-10-02.csv; the 54 × 25 matrix of the library is in selected_features_matrix_2026-10-02.csv. H03 is optional in the paper.

Designs by condition

Table 2. Means of the four blocks (task × model) per condition

MeasureC2C3C6C6++C7
Features present, all 257.509.7510.5012.7511.50
  Emergent transitions (11)2.004.004.005.254.75
  Iteration targets (7)1.752.001.502.502.25
  Structure (4)2.252.753.253.002.50
  Human activity (3)1.501.001.752.002.00
Transforms18.824.235.243.831.8
Transforms (contracted)1518.523.526.523
Loops1.752.752.254.252.75
Human share of transforms0.280.420.300.350.28
Hybrid share of transforms0.130.050.090.070.17
Designs with checker errors (of 4)00223
Mean checker warnings0.750.757.0012.757.25
Jaccard similarity to the most similar library workflow0.480.600.530.580.52

C2: ATWL specification and one example. C3: adds the 54-workflow library in ATWL form. C6: faithful ATWL translation of the model's own C4 prose design. C6++: attempted implementation of the C6 self-critique. C7: fresh ATWL redesign from the same prose design. An undeclared assign target takes the category of the artifact assigned to it for the features (affects I01, I04, I05 and I07 in 8 designs).

Table 3. Paired contrasts between conditions (paired by block)

ContrastWhat it contrastsMean difference in features presentBlocks more / fewer / equal
C3 - C2library in ATWL form (and not only the example)+2.253 / 1 / 0
C6 - C2prose library then faithful translation vs no library+3.003 / 0 / 1
C6 - C3prose-library translation vs ATWL-library design+0.753 / 1 / 0
C7 - C6fresh redesign vs faithful translation+1.002 / 2 / 0
C7 - C3prose-library redesign vs ATWL-library design+1.752 / 1 / 1
C6++ - C6implemented self-critique vs the translation+2.254 / 0 / 0

Single features that differ in three or more blocks: E01 (generate-knowledge -> define-unit) in C3 - C2; E08 (generate-knowledge -> build-model) in C3 - C2; E10 (assess -> define-unit) in C7 - C3; I07 (knowledge accumulated in a loop) in C6++ - C6. None is significant after correction.

Table 4. Model and task

GroupingValueDesignsFeatures presentTransformsLoopsHuman shareHybrid shareWith checker errors
modelSonnet109.634.03.10.390.061
modelTerra1011.227.52.40.270.156
taskVAST1011.131.32.60.350.114
taskVIS109.730.22.90.300.103

Table 5. Factors on the feature profiles (PERMANOVA on the Jaccard distance)

FactorGroupsPseudo-FShare of variance (R²)p (permutation)
condition50.6615%0.935
model23.2215%0.0015
task22.7813%0.0057

Table 6. Exploratory contrast (post hoc): designed directly with ATWL materials against derived from the prose design

CharacteristicC2 + C3 (n = 8)C6 + C6++ + C7 (n = 12)p, workflows treated as independentMean difference over blocksp, exact within blocks
workflows with checker errors0 of 87 of 120.0150.5830.0060
transforms21.536.9<0.00115.4<0.001
transforms (contracted)16.824.30.00957.58<0.001
loops2.253.080.0770.8330.143
features present (of 25)8.6211.60.0192.960.026
checker warnings0.7590.0408.250.011

The within-block p-values randomise the group labels within the four blocks (exact, all 104 assignments). The direction is the same in all four blocks. Within the derived group all 6 Terra designs have checker errors against 1 of 6 Sonnet designs; the 4 directly designed Terra designs have none.

Designs against the library

Table 7. Prevalence of each feature in the 54 library workflows and in the 20 designs

IDFeatureLibrary (of 54)Designs (of 20)q (Benjamini–Hochberg)
E01generate-knowledge -> define-unit29 (54%)13 (65%)0.576
E02generate-knowledge -> characterise23 (43%)11 (55%)0.576
E03visualise -> generate-knowledge (unmediated jump)19 (35%)7 (35%)1.000
E04visualise+abstract+contextualise -> define-unit22 (41%)5 (25%)0.448
E05build-model -> characterise13 (24%)14 (70%)0.0038
E06generate-knowledge -> visualise13 (24%)4 (20%)1.000
E07generate-knowledge -> contextualise11 (20%)0 (0%)0.067
E08generate-knowledge -> build-model11 (20%)9 (45%)0.090
E09generate-knowledge -> abstract4 (7%)3 (15%)0.557
E10assess -> define-unit3 (6%)10 (50%)<0.001
E11build-model -> define-unit3 (6%)4 (20%)0.154
I01units redefined in a loop19 (35%)5 (25%)0.683
I02features redefined in a loop23 (43%)5 (25%)0.338
I03arrangement redefined in a loop16 (30%)0 (0%)0.011
I04model refined in a loop8 (15%)4 (20%)0.787
I05specification revised in a loop35 (65%)20 (100%)0.0056
I06patterns revised in a loop24 (44%)1 (5%)0.0056
I07knowledge accumulated in a loop7 (13%)5 (25%)0.448
T01two or more given datasets18 (33%)13 (65%)0.046
T02units that are groups or clusters20 (37%)16 (80%)0.0056
T03several views used together27 (50%)8 (40%)0.683
T04two or more sequential loops14 (26%)18 (90%)<0.001
H01contains a hybrid transform19 (35%)17 (85%)0.0011
H02a priori configuration10 (19%)2 (10%)0.618
H03human provides initial knowledge8 (15%)14 (70%)<0.001

Shaded rows: q < 0.05 (Fisher exact test). Design counts are not independent of one another: designs share model and task, C6, C6++ and C7 share a prose design.

Features by condition

Heat map of the number of designs per condition with each feature, and the library share
Number of designs (of 4) per condition with each feature, and the share of library workflows with the feature. PDF version: conditions_feature_heatmap.pdf. Counts and p-values per feature are in the sheet “Feature x condition” of ATWL_designed_conditions.xlsx.

Limits

Files

ATWL_designed_workflows_features.xlsx

Features, descriptors and checker results of the 20 designs; comparison with the library. Sheets: Summary, Features, Library vs designed, By condition, model, task, Descriptors and checker.

ATWL_designed_conditions.xlsx

Differences between conditions: feature × condition counts, families, paired contrasts, factors, exploratory contrast, contrast by block, proximity to the library.

designed_features.csv

20 designs × 25 binary features.

web_checker_results_designed.json · library

Output of the validation function of the ATWL syntax checker for the designs and for the library.

The features, descriptors and tests were computed with the scripts designed_report.py, condition_analysis.py and run_web_checker.js of the ATWL Workflow Lens package (folder atwl_tools).