cs.AISep 30, 2026
SaveA2Z GameSpec-Bench: How Faithfully Can Coding Agents Generate Games from Game Design Specifications?
Organizations: KRAFTON · KAIST · Korea National University of Arts
Abstract
Delegating complete application development to coding agents requires preserving the intended design rather than simply producing plausible outputs through naive prompting. Game development provides a demanding testbed, as long-form Game Design Documents (GDDs) describe requirements that must work together across game logic, visual rendering, and player interactions. However, existing game-development benchmarks typically use compact specifications and provide limited support for evaluating interdependent requirements across these aspects in long-form GDDs. We introduce A2Z GameSpec-Bench, a benchmark of 100 long-form GDDs for evaluating end-to-end game development by agents. We measure faithfulness by checking whether the game satisfies the GDD requirements and preserves the relationships among them. Each GDD is turned into a dependency-aware contract that contains rules, constraints, and prerequisite relations. Following game-development practices, we combine source-code inspection with agent-generated test policies for scenario-based replay and adaptive playtesting. The contract remains fixed across agents and revision rounds, while judgments and evidence linked to the same requirements support consistent comparison and failure detection. Our evaluations show that current agents struggle to jointly satisfy interdependent requirements across code implementation and actual play. Requirement-specific feedback improves GDD Fidelity by 10.9% relative to self-revision after two rounds. A2Z GameSpec-Bench assesses end-to-end specification-following ability beyond implementation judgments and provides targeted feedback to support more faithful game development. Code and datasets are available at https://a2z-gamespec-bench.github.io.
Figures & tables
Figure 1 : Overview of A2Z GameSpec-Bench. Dependency-Aware Contracts guide evaluation and targeted revision through source-code inspection, replay, and playtesting. Our evaluation results demonstrate that High compilability and runnability do not guarantee high GDD Fidelity.
Figure 2 : Challenges in specification-driven game development. 2(a) In our game Siege Deck 2D , the deck-limit requirement gets full source-code credit, but playtesting reveals a reward gain without the card removal. 2(b) Across three game-design corpora with 353 documents, CiRA ( Fischbach et al., 2021 ) classifies 38.4% of sentences as causal on average, compared with 28% for general documents ( Frattini et al., 2023 ) (Appendix B.1 ). 2(c) Source-code pass rates decrease when all upstream rules must also pass (Appendix B.7 ).
| Benchmark Setup | Requirement Evaluation | Evaluation Channels | ||||||||
| Benchmark | Full-Game Generation | Input Type | # of Spec. Tokens | Evaluation Target | Relations | Predefined | Cross-Axis | Source Code | Rendered Behavior | Adaptive Play |
| GameDevBench | ✗ | Task + project | 185 | Task-specific tests | ✗ | ✓ | ✗ | ✓ | ✗ | ✗ |
| GameEngineBench | ✗ | Spec. + project | – | Tests + LLM judge | ✗ | ✓ | ✗ | ✓ | ✗ | ✗ |
| OpenGame-Bench | ✓ | Game spec. | 1,830 | Build / visual / intent | ✗ | ✓ | ✗ | ✗ | ✓ | ✗ |
| WebGameBench | ✓ | Structured spec. | – | Runtime quality | ✗ | ✓ | ✗ | ✗ | ✓ | ✓ |
| GameCraft-Bench | ✓ | Game spec. | 1,547 | Predefined rubric | ✗ | ✓ | ✗ | ✗ | ✓ | ✗ |
Table 1 : Comparison of agentic game development benchmarks. A2Z GameSpec-Bench fixes a dependency-aware evaluation contract from long-form GDDs independently of generated outputs and links judgments from complementary evidence channels to the same requirements.
Figure 3 : Overall evaluation and revision pipeline of A2Z GameSpec-Bench.
| Model | Overall GDD Fidelity | Evaluation Axis | Verifiable Rate | ||||||||
| Source | Replay | Playtest | |||||||||
| All | Small | Big | Small | Big | Small | Big | Small | Big | Small | Big | |
| Claude-Fable-5.1 | 77.0 | 82.8 | 71.1 | 89.7 | 83.5 | 73.5 | 55.1 | 85.2 | 74.7 | 98.7% | 98.7% |
| Claude-Opus-5 | 73.9 | 80.9 | 66.8 | 87.9 | 78.4 | 71.2 | 54.4 | 83.6 | 67.7 | 100.0% | 99.3 % |
| Claude-Opus-4.8 | 56.8 | 67.9 | 45.6 | 72.5 | 49.6 | 62.5 | 41.4 | 68.7 | 46.0 | 99.3% | 100.0% |
| GPT-6-Astra | 71.6 | 78.5 | 64.6 | 84.2 | 74.4 | 69.9 | 52.1 | 81.6 | 67.4 | 99.3% | 97.3% |
Table 2 : Main results on A2Z GameSpec-Bench. Overall GDD Fidelity, per-axis scores, and verifiable rates on 100 GDDs. Bold represents the best result, and underline indicates the second-best.
Figure 4 : Complementarity of source-code evaluation, scenario-based replay, and adaptive playtesting. 4(a) Examples of specification violations identified through different evidence traces. 4(b) Playtest judgment outcomes grouped by source-code score. 4(c) Ratio of reversed GDD fidelity rank orderings across all 100 evaluated games built by GPT-5.6-Sol upon omission of individual evaluation axes.
Figure 7 : Requirement-level repairs beyond self-revision. Each panel pairs a GDD requirement with the initial build, the self-revision baseline, and Source + Replay + Playtest ; both revision columns show second-round builds. The examples compare 7 body recoloring, 7 estimate-class icons and confidence, 7 a held fragment following the pointer, and 7 the Hull update required by oxygen depletion. In each illustrated case, the violation remains in the initial and self-revision builds and is repaired by Source + Replay + Playtest . Detail crops enlarge the highlighted regions in 7 – 7 ; crosshairs in 7 mark the actual pointer position. In 7 , all three builds display a drowned-run screen, but only the feedback revision sets Hull to 0. The state cards report values recorded during execution. In 7 , is the reveal radius and is Manhattan distance from the ship.
Table 4 : Component analysis. 4(a) Mean and maximum absolute changes in source-code score from uniform weighting (Appendix F.1.2 ). 4(b) Evidence support for fixed-rate versus adaptive frame selection (Appendix G.3 ). 4(c) Judgment coverage (%) after a second normal or adversarial pass under matched budgets. The first normal pass reaches 40.5%; gains are percentage points relative to this shared first pass (Appendix F.3 ). Coverage includes both satisfied and violated requirements.
Figure 8 : Extension of contract-based evaluation to 3D games. GDD requirement summaries accompany original gameplay captures from two Three.js builds: 8 vehicle-based ball play in Rocket League and 8 momentum-based platforming in Sonic: Cascade Coast . Both builds are assessed through source-code inspection, scenario-based replay, and adaptive playtesting against their fixed contracts. Appendix H reports the scores and requirement-level diagnoses.
Appendix figures & tables46 assets
Supplementary material from the paper’s appendix.
Appendix
Figure S1 : One game traced from brief to GDD. The brief fixes what to build, the CV adds how the game should feel and why (without numbers), and the GDD specifies every value and rule. The highlighted boxes follow one requirement, the cost of letting a unicorn flee, through all three stages: a rough rule in the brief, a design intent in the CV, and a cited rule, constants, and a worked example in the GDD.
| Setting | Value |
| CVs used in the search ( ) | 13 |
| Candidate harnesses per iteration ( ) | 3 |
| Repeats per quality judgment / pairwise comparison | 3 / 3 |
| Proposer and generator | Claude-Opus-4.8 |
| Evaluator (scoring and pairwise judging) | GPT-5.5 |
| Maximum iterations | 20 |
Table S1: Harness search configuration.
| Criterion | Requirement | Failure if violated | Example violation |
| G1 Structural Conformance | • Standalone, all sections present • No empty or [TBD] section • No dangling identifier | The requirement cannot be checked. | A section cited elsewhere had been removed. |
| G2 Referential Consistency | • One canonical definition ( DBT- ) • Every citation resolves • Repeated values agree | Contradictions; no implementation can satisfy all. | bool[56] declared for 55 species; 1 wave in App. A vs. 3 in App. B. |
| G3 Behavioral Soundness | • Total, deterministic rule tables • No unreachable or dead mechanic • Difficulty never decreases | Faithful implementations are penalized. | A 90 s boss timer never fires; HP decay kills the patient at 40 s. |
| G4 Executable Formulas | • Variables bound to constants • Fixed evaluation order, bounds • Worked example that recomputes | Agent and evaluator can compute different answers. | Kill time ignores the boss’s DEF; ceil(50/4) written as 12. |
Table S2: Criteria for golden GDDs. Each criterion excludes one way a GDD can invalidate conformance-based evaluation. Example violations are taken from our validation logs.
| Statistic | Small | Big |
| Documents | 50 | 50 |
| Lines (mean) | 629 | 1,153 |
| Tokens (total) | 704,270 | 1,314,862 |
| Tokens (mean, range) | 14,085 (5,240–24,022) | 26,297 (12,489–51,433) |
| Outcome requirements (total) | 2,710 | 4,202 |
| Outcome requirements (mean, range) | 54.2 (9–151) | 84.0 (10–246) |
Table S3 : GDD dataset statistics. Document length and requirement composition of the Small and Big splits. Outcome requirements count individual promises in GDD rule-table rows, including presentation outcomes.
| # | Task Family | Games | |
| 1 | Reaction, Inhibition & Speed Classification | 9 | reaction_test , no_response_test , bloom_or_weed , orbit , bin_bit , odd_even_flash , odd_patch , same_or_shift , three_letter_delivery |
| 2 | Temporal Precision & Rhythm | 7 | beat_bistro , centerline , chord_snap , half_beat_bell , ooga_ooga_tower , ozone_dash , rooftop_rumble |
| 3 | Continuous & Precision Motor Control | 9 | cursor_pursuit , still_cursor , chalk_escape , stamp_register , pocket_curling , magnet_dock , safe_dial , warm_ascent , wheel_steps |
| 4 | Short-Term Memory & Attentional Tracking | 3 | echo_three , hidden_spot , key_under_cups |
| 5 | Perceptual Estimation & Psychophysics | 4 | blink_count , equal_slice , mobile_balance , whisper_compass |
| 6 | Spatial Reasoning & Mental Transformation | 5 | one_fold_letter , shadow_pair , lighthouse_mirrors , tiny_moving_van , untangled_cords |
Table S4 : Task-family distribution of the Small GDDs.
| # | Genre | Games | |
| 1 | Platformer & Metroidvania | 7 | afterglow , chromashade , rainwright , ssitgim , turning_keep , neon_spray , the_librarians_hook |
| 2 | Action Roguelite & Survival | 4 | abyssal_chain , seamline , todays_best_spot , afterglow_network |
| 3 | Shooter & Bullet-hell | 3 | golem_workshop , magma_arc , snowfall_draw |
| 4 | Rhythm | 4 | beat_reroute , beatstorm , cloud_cast , rhythm_of_response |
| 5 | Puzzle | 4 | chameleon_slide , fable_knot , fogfall_delivery , u001_toyfit |
| 6 | Strategy & Tactics | 5 | ashen_citadel , gateline_2d , siege_deck_2d , chroma_bastion , xl001_edens_debt |
Table S5 : Genre distribution of the Big GDD split.
Figure S2 : A Small golden GDD example. Excerpt of traces_left ; omitted sections are marked.
Figure S3 : A Big golden GDD example. Excerpt of chameleon_slide GDD; omitted sections are marked.
| Document | Sentences | Causal (%) |
| The Sky Above, the Sky Below | 2,069 | 38.3 |
| Frontier Pharmacist | 1,996 | 42.5 |
| Leisure Suit Larry 5 | 1,701 | 38.3 |
| Love for Sail | 1,551 | 37.5 |
| Shape Up or Slip Out | 1,444 | 44.6 |
| Claw | 1,428 | 29.6 |
Table S6 : Studio game-design documents used in the causality analysis. Sentence counts are computed after text normalization and non-prose filtering; causal rates are CiRA predictions over the retained sentences.
| Corpus | Documents | Sentences | Causal (%) |
| Studio GDDs | 10 | 11,741 | 37.9 |
| Game benchmark specifications | 246 | 11,441 | 32.0 |
| Board game rulebooks | 97 | 50,518 | 45.3 |
| Pooled | 353 | 73,700 | 42.0 |
| Macro average over corpora | – | – | 38.4 |
Table S7: Causal-sentence estimates in external game-design corpora. Rates are CiRA predictions on retained prose. The pooled rate weights corpora by sentence count, while the macro average gives equal weight to the three corpora.
Figure S4 : Contract construction from a GDD. The pipeline fixes vocabulary and rule entries before completing rule definitions and validating the contract. Coral boxes denote model-based stages, while dark-red boxes denote deterministic stages without model calls. The labels indicate independent repetitions: row facts are consolidated by two-out-of-three voting, whereas generated contracts are compared for structural consistency before one is frozen for evaluation. The dashed line indicates that the vocabulary remains fixed throughout subsequent stages.
Figure S5 : Contract-linked execution of traces_left . Nodes show rule occurrences in a recorded normal playtest, and labeled links indicate the state attributes connecting them. Three sensor placements and one removal precede P4 , which commits the run. The fixed contract supplies the rule identities and dependencies; the trace supplies their observed order. Source GDD rows appear in Figure S6 .
Figure S6 : The GDD rows the entries in Figure S5 were derived from, in the four sections of traces_left that the run touched, each labelled with its line in the framed GDD. A row states a situation and its required effects ( ); the pipeline formalizes the situation as the rule’s trigger and preconditions, and the required effects as state updates or event emissions.
| Agreement Measure | Contract |
| Matched Items | 100% |
| Dependency Graph | |
| Trigger Name | 0.94 |
| Predicate Structure | 0.70 |
Table S8: Consistency of evaluation targets across repeated generations. Three rule generations per GDD on 50 Small and 50 Big GDDs with fixed vocabulary and rule entries, averaged over the two splits; items are matched by their source entries.
| Game & Rule | GDD statement (abridged) | Generation | Generation | Ratio of ( ) |
| untangled_cords R8 ( Small ) | All other frames: keep state, keep updating t in Play_Active | timer INCREMENT dt | timer SET advanced by dt | 1.00 / 1.00 |
| magma_arc STAR2 ( Big ) | Not STAR3, and hit events this attempt: 2 stars | event <= 3 STAR SET 2 | event IN [1, 2, 3] STAR SET 2 | 1.00 / 1.00 |
Table S9 : Examples of predicate-structure variation across contract generations. Rules whose predicate structure differs between two contract generations, with the GDD row they encode and the source-code score each generation received on the same build.
| Split | games | rules | Predicate structure | pairs | |
| Small | 50 | 869 | identical | 1,773 | 12.7% |
| different | 834 | 17.7% | |||
| Big | 50 | 1,860 | identical | 4,045 | 8.8% |
| different | 1,535 | 11.1% |
Table S10: Effect of predicate-structure variation on the source-code score. Rule pairs across the three generations, three per rule, judged on the same build; the last column is the share of pairs whose per-rule scores differ by at least 0.25.
Figure S7 : Excerpt of the source-code judge system prompt. Text is verbatim, and omitted passages are marked [...] .
Figure S8 : Excerpts of the two replay-judge system prompts. Frame selection over the recorded replay and scoring from the selected frames. Text is verbatim, and omitted passages are marked [...] .
Figure S9 : Excerpts of the three adaptive-playtest system prompts. Text is verbatim, and omitted passages are marked [...] .
Figure S10 : The playtest runtime interface and two recorded bots for magnet_dock . (a) Every bot receives the same ctx . (b) The normal playtest bot reads state, issues inputs, and records labelled snapshots naming the target rule. (c) The adversarial playtest bot enters through a declared scenario, pins the precondition of rule D1 with setState , and observes the outcome.
Figure S11 : Pairwise model comparisons and Elo ratings: Pooled GDDs. Matrix entries give the row model's win rate against each column model, with ties counted as one half. The rightmost column reports the model's Elo rating for that panel. Values above 50% favor the row model; Elo ratings are relative and centered at 1,000 within each panel.
Figure S12 : Pairwise model comparisons and Elo ratings on Small GDDs. The comparison protocol, relative model order, and win-rate color scale follow Figure S11 . Each panel includes an Elo column alongside the empirical pairwise matrix.
Figure S13 : Pairwise model comparisons and Elo ratings on Big GDDs. The comparison protocol, relative model order, and win-rate color scale follow Figure S11 . Each panel includes an Elo column alongside the empirical pairwise matrix.
Figure S14 : Requirement consistency across repeated source-code judgments. The naive judge evaluates each of 100 unchanged builds five times. Top: the minimum, maximum, and mean number of extracted requirements per game, with rule counts shown for comparison. Bottom: the distribution of the fraction of one run’s requirements matched in another run at a Jaccard threshold of 0.5.
Figure S15 : Dependency weighting changes game-level comparisons. Each cell represents one GDD. Purple cells indicate a strict reversal in the source-code score ordering of at least one pair of the six proprietary agents between uniform weighting ( ) and downstream-reach weighting ( ), with rule judgments and invariant scores fixed. Such reversals occur on 23 of the 100 GDDs.
Figure S16 : Effect of dependency weighting on rule scores. Each bar is at . Left: root-defective games meeting the joint source-code and playtest criteria. Right: comparison games without an observed playtest violation among their assessed root rules. Negative values indicate lower weighted scores; the sign is unchanged at .
| Judge, effort | Item | Mean reward | vs. ref. | s / call | $ / call |
| sol, xhigh (reference) | – | 0.639 | – | 78 | 0.048 |
| sol, high | 0.88 | 0.667 | 71 | 0.034 | |
| sol, medium | 0.88 | 0.669 | 63 | 0.034 | |
| sol, low | 0.89 | 0.680 | 52 | 0.045 | |
| luna, xhigh | 0.85 | 0.634 | 63 | 0.0074 | |
| luna, high | 0.85 | 0.645 | 41 | 0.0072 |
Table S11 : Agent-as-a-judge on six builds with identical scenario replays and rubric. Item is the Pearson correlation of rubric-item scores with the original sol extra-high judgments of the same builds (re-run agreement ); is the mean reward difference from them. Cost is per judge call at list prices. sol = GPT-5.6-Sol, luna = GPT-5.6-Luna.
| Coding agent | Normal (%) | Adversarial gain (pp) | Combined (%) | ||||
| Sat. | Viol. | Cov. | Sat. | Viol. | Score | Cov. | |
| All | |||||||
| Claude-Fable-5.1 | 28.6 | 0.4 | 29.0 | 51.4 | 7.6 | 80.0 | 88.0 |
| Claude-Opus-5 | 26.7 | 0.4 | 27.1 | 49.0 | 9.0 | 75.7 | 85.1 |
| Claude-Opus-4.8 | 19.2 | 0.6 | 19.7 | 38.2 | 13.6 | 57.4 | 71.5 |
| GPT-6-Astra | 25.8 | 0.5 | 26.3 | 48.7 | 8.5 | 74.5 | 83.4 |
Table S12 : Normal and adversarial playtest decomposition across agents. Each split uses the same score-reporting cohort as Table 2 ; All is the mean of Small and Big . Sat., Viol., and Cov. denote satisfied, violated, and conclusively judged requirements, respectively. Adversarial gain reports additional judgments for previously unverified requirements in percentage points (pp). Combined Score reproduces the Small and Big Playtest scores in Table 2 using the same archived per-game scores; phase contributions and coverage use exact verdict counts. All values are reported to one decimal place.
| First pass | Second pass | Coverage | |
| Normal | – | 40.5 | – |
| Normal | Normal | 41.0 | |
| Normal | Adversarial | 48.5 | |
| Adversarial | – | 45.0 | – |
Table S13 : Controlled playtest comparison. Mean judgment coverage (%) on 86 target rules from 25 Small and 25 Big games. A second pass keeps the first pass’s conclusive verdicts and re-tests only the targets it left unverified.
| Agent | Equal | Source emphasis | Replay emphasis | Adaptive emphasis |
| Claude-Fable-5.1 | 77.0 (1) | 79.4 (1) | 73.8 (1) | 77.7 (1) |
| Claude-Opus-5 | 73.9 (2) | 76.2 (2) | 71.1 (2) | 74.3 (2) |
| GPT-6-Astra | 71.6 (3) | 73.5 (3) | 68.9 (3) | 72.3 (3) |
| GPT-5.5 | 63.2 (4) | 64.5 (4) | 60.3 (5) | 64.8 (4) |
| GPT-5.6-Sol | 61.8 (5) | 62.0 (5) | 60.4 (4) | 63.1 (5) |
Table S14: Sensitivity to three-axis aggregation weights. Each cell reports the aggregate score (0–100) and rank among nine agents. The weight order is source, replay, and adaptive playtest. Source dependency weighting remains fixed at . All-game axis scores equally average the Small and Big means. Bold cells indicate a rank change from equal weighting.
Figure S18 : Full source-code credit with a runtime violation. Each rule passes the source-code judge (item score 1.00) and is judged violated in the playtest. (a) Rainwright : with 8% water, below the 25% minimum cost, the held build preview stays teal instead of turning red, because the denial flag is set only on release. (b) Strata Keepers : a two-day zone entry consumes four days because the cost is applied twice. (c) Chroma Bastion : the finished run scores 4,869 but a constant 2,760 is staged for submission. Screenshots are from asset-integrated builds whose relevant logic is unchanged from the evaluated source; boxes, callouts, and crops are annotations.
| Measure | Small | Big | All |
| Games | 50 | 50 | 100 |
| Full-credit rules | 738 | 896 | 1,634 |
| Eligible full-credit targets | 377 | 531 | 908 |
| Additional inspection targets | 97 | 311 | 408 |
| Games with additional targets | 17 | 36 | 53 |
| Additional targets / eligible targets | 25.7% | 58.6% | 44.9% |
Table S16: Dependency context identifies additional source-code inspection targets. A target has full enumerated source credit and a lower-scored direct predecessor. Percentages are pooled over eligible targets; the enumerated judgments remain fixed.
| Judge | Effort | Rule-Level Correlation | Invariant Agreement | Mean Rule Score | Tokens/Call | Relative Cost |
| GPT-5.6-Sol | Extra high | 0.74 | 0.83 | 0.76 | 213 | 1.00 |
| High | 0.70 | 0.83 | 0.76 | 214 | 1.00 | |
| Medium | 0.75 | 0.88 | 0.78 | 176 | 0.83 | |
| Low | 0.80 | 0.86 | 0.79 | 160 | 0.75 | |
| GPT-5.6-Luna | Extra high | 0.72 | 0.77 | 0.79 | 198 | 0.15 |
| High | 0.72 | 0.76 | 0.79 | 187 | 0.15 |
Table S17 : Agreement and cost across source-code judge configurations. Rule-level correlation and invariant agreement are measured against a fixed set of reference judgments from GPT-5.6-Sol at extra-high reasoning effort. The first row reports an independent re-run of that configuration. Mean Rule Score reports the average rule implementation score in each run. Relative cost is estimated from token usage per call and the prices used in the experiment, normalized to the first row.
Figure S20 : Tokens vs. frame width . Left: tokens per frame, fixed prompt excluded. Right: item-level agreement with the next-larger width.
| Native | 640 px | 480 px | |
| 1920 1080 | |||
| 1280 720 |
Figure S21 : A sampled frame at different resolutions. The same frame region as the judge receives it at native resolution, 640 px and 480 px (Lanczos downscaling as in the pipeline). Top: 1920 1080 game; bottom: 1280 720 game.
| Judge, effort | Small | Big | Tokens / vote |
| Sol, xhigh | 0.959 / 2 | 0.930 / 4 | 485–753K |
| Sol, high (re-run) | 0.959 / 3 | 0.959 / 1 | 516–688K |
| Sol, low | 0.892 / 7 | 0.897 / 3 | 217–315K |
| Luna, xhigh | 0.919 / 3 | 0.893 / 1 | 57–86K |
| Luna, high | 0.910 / 6 | 0.888 / 3 | 51–71K |
| Luna, low | 0.833 / 9 | 0.810 / 10 | 46–64K |
Table S18 : Playtest judge comparison on fixed traces. Each cell reports verdict agreement with a GPT-5.6-Sol high reference, followed by the number of rules whose binary satisfaction score changes ( Small : 222 rules; Big : 242 rules). Each rule receives three judgments. An independent Sol high re-run provides a reference for run-to-run variation.
| Audit Outcome | Assessments | Share (%) |
| Fixed-rate preferred | 458 | 11.2 |
| Adaptive preferred | 701 | 17.1 |
| Equivalent | 2,848 | 69.4 |
| Insufficient evidence | 95 | 2.3 |
| Total | 4,102 | 100.0 |
Table S19 : Rationale-Support Outcomes. Counts and percentages over all 4,102 paired rubric-item assessments.
Figure S22 : Visual evidence from adaptive frame selection. Each panel compares the same replay of a Small game. Adaptive selection captures (a) Confirm activation, (b) the path before removal, (c) holding and defense feedback, and (d) tile convergence and overlap. These cues are absent from the displayed uniform samples. Each row shows three cropped frames with timestamps.
| Gameplay Agent | Adaptive Playtest | Judgment Coverage (%) | ||
| Game | Progress (%) | Requirements | Normal | Normal + Adv. |
| Abyssal Chain | 110 | 6.4 | 89.1 | |
| Alias Alchemy Shop | 133 | 12.0 | 91.0 | |
| Beat Reroute | 84 | 19.1 | 86.9 | |
| Chameleon Slide | 87 | 0.0 | 67.8 | |
| Fogfall Delivery | 119 | 5.0 | 88.2 | |
Table S20 : Recorded results for all ten games. Gameplay agent progress is milestone attainment (mean sample SD over three seeds); Judgment coverage is over the archived GDD-derived requirements. The percentages use different target sets. Coverage is calculated from the saved judgments, with unverified requirements retained in the denominator.
| Game | Source | Replay | Playtest |
| Sonic: Cascade Coast | 99.71 | 53.50 | 80.00 |
| Diablo Cathedral | 86.74 | 55.00 | 66.55 |
| Rocket League | 83.64 | 57.40 | 77.54 |
Table S21 : Evaluation of three additional 3D games. Scores are reported on a 0–100 scale. Playtest combines normal and adversarial judgments, using the full fixed rule set as the denominator.
Figure S23 : Selected GDD-specific repairs beyond self-revision. Initial builds are compared with second-round self-revision and Source + Replay + Playtest under matched inputs and preconditions. The cases test S23 confirmation before discarding a run, S23 CODE BLUE feedback on boss timeout, S23 opening the shop after alias selection, and S23 a diagonal attack against a target inside the specified hit area. Each displayed requirement remains unresolved after self-revision and is satisfied by Source + Replay + Playtest in the probe. Life and vigor values are recorded during execution; all targets in S23 start at vigor 1. Crops retain the archived builds’ native art; arrows indicate attack aim. These selected outcomes do not establish complete GDD compliance.
Figure S24 : Sampled gameplay in generated Small games (01). Four games from the Small split illustrate (a) selective clicking, (b) visual discrimination, (c) ring switching and collision feedback, and (d) the observation and input phases of a memory task. Each row shows three states in the order of the supplied capture sequence. The full game screens are retained, including their prompts and feedback.
Figure S25 : Sampled gameplays in generated Small games (02) Additional four frames illustrate (a) tracking a hidden key through a cup shuffle, (b) sorting curved and angular shapes, (c) accumulating and banking dice points, and (d) rotating a combination dial with persistent progress cues. Each row contains three full screenshots from one replay scenario, ordered from left to right.
Figure S26 : Sampled gameplays in generated Big games (1). Four games illustrate (a) fishing controls and catch rewards, (b) traversal tools and base defense, (c) unit deployment and tactical cards, and (d) delivery route and fleet management. Each row groups three scenario-specific screenshots from one game. Full screenshots retain the gameplay context and interface feedback.
Figure S27 : Sampled gameplays in generated Big games (2). Four games illustrate (a) light and shadow traversal, (b) soul encounters and cleansing, (c) production and persistent shop progress, and (d) excavation, restoration, and research. Each row groups three scenario-specific screenshots from one game. Full screenshots retain the gameplay context, controls, and state information.