PaperlayerView exact revision on arXiv

Source-linked AI summary

World Editing: Intervening on Executable Worlds at Increasing Depth

Max Ku, Nok-Kan Law, Yu-Chien Tang, Shih-Ying Yeh, Ping Nie, Andy Zheng, Tat Hei Lai, Fei-Yueh Chen, Nikko Yu, Wei-Chieh Sun, Suzy Huang, Chiao-Wei Hsu, Chih-Chuan Huang, Chak-Wing Mak, Ho Yin Sam Ng, Edisy Kin Wai Chan, Min-Hung Chen, Ho Kei Cheng

arXiv:2610.02331v1cs.AI

TL;DR

Research has emphasized generating worlds and acting within them, while deliberate editing of existing executable worlds remains underexplored. The paper defines intervention depth and builds IGMWorld and IGMBench around game modding; results show that reliability declines with depth and many failed edits remain executable but fail behavioral checks.

  • Problem

    Research has largely studied world generation and in-world action, leaving deliberate edits to existing executable worlds while preserving unrelated properties comparatively underexplored.

    Source: §1 · p. 1

  • Method

    The paper defines intervention depth by coupling among world entities, dynamics, and systems, then instantiates editing through IGMWorld and 110-task IGMBench across Minecraft and Terraria.

    Source: §1 · p. 2, §2 · p. 3

  • Results

    Reliability decreases with intervention depth even among tasks with comparable criterion counts, and most unsuccessful edits remain executable but fail behavioral validation.

    Source: §7 · pp. 9–10

  • Takeaways & Limitations

    Executable games offer a concrete setting for applying and verifying interventions, while the world-editing formulation extends to other modifiable executable worlds.

    Source: §7 · p. 10

    Quantitative evaluation covers Minecraft and Terraria; evaluating other executable worlds requires ecosystem-specific execution and validation infrastructure.

    Source: LIMITATIONS · p. 10

Abstract

from arXiv · show

Interactive world models are increasingly capable of generating environments and acting within them, yet deliberately editing an existing executable world remains underexplored. We formulate world editing as intervening on an existing world while preserving properties that should remain unchanged, and introduce intervention depth as an axis describing how strongly an edit couples world entities, dynamics, and systems. We instantiate this capability through industry-grade game modding and introduce IGMWorld, together with IGMBench, a benchmark of 110 tasks and over 1.1K executable state and behavioral criteria across Minecraft and Terraria. The tasks span property, entity, dynamics, and system interventions and are evaluated through deterministic executability, behavioral, preservation, and visual checks. Frontier coding agents already exhibit substantial world-editing capability: the strongest configuration solves 78.2% of tasks under a strict task-level criterion, while criterion-level performance reaches 94.8%. Reliability generally decreases with intervention depth, and this pattern persists even among tasks with similar numbers of evaluation criteria. Most failed edits still build and load successfully, suggesting that the main difficulty is making the edited world behave as requested. Visual consistency remains a separate weakness, with all evaluated configurations below 50% joint visual pass rate. These results show that world editing is a distinct capability from world generation and interaction, and that executable games provide a practical testbed for studying it.

1 INTRODUCTION

Research has focused on generating worlds and acting within them, leaving deliberate edits to existing executable worlds comparatively underexplored. The paper introduces game-modding infrastructure and a benchmark to study such edits and their evaluation.

1 INTRODUCTION

  • World editing targets changes to an existing world's entities, behaviors, or systems while preserving unrelated properties, complementing research on world generation and in-world action.Prior editing work cited here focuses on visual or geometric changes to generated scenes.Source: §1 · p. 1
  • The benchmark evaluates an agent's edited world for executability, state and behavioral correctness, and visual consistency with existing assets.Figure 1 depicts an agent modifying a scaffold mod repository from a natural-language request.Source: Figure 1 · p. 2
  • IGMWorld and IGMBench operationalize executable-world editing through 110 Minecraft and Terraria tasks and over 1.1K executable state and behavioral criteria.The benchmark spans property, entity, dynamics, and system interventions, with regression and visual validation.Source: §1 · p. 2
  • IGMBench tasks specify natural-language edit requests and multiple state or behavioral criteria, with task success requiring all associated criteria to pass.Table 2 describes the distribution of tasks and criteria across games and intervention levels.Source: Table 2 · p. 3

2 WORLD EDITING AS INTERVENTION

The paper defines world editing as an intervention on an existing executable world that realizes a request while preserving unrelated properties. Intervention depth describes the coupling among entities, dynamics, and systems required by that edit.

2 WORLD EDITING AS INTERVENTION

  • An intervention transforms an existing world W into W′ to realize a requested edit while preserving unrelated properties of W.The formulation represents a world through entities and state space, transition dynamics, and rules or systems governing interactions.Source: §2 · p. 3
  • The four intervention levels progress from modifying existing properties (L1), to adding entities (L2), changing behavioral rules (L3), and coordinating coupled components (L4).In game-modding terms, these correspond to parameter, content, mechanic, and system editing.Source: §2 · p. 3
  • Intervention depth measures semantic scope and coordination among world components, not implementation size or the amount of code changed.A system-level edit can be small in implementation while requiring several interacting components to remain consistent.Source: §2 · p. 3
  • Games provide an executable testbed: agents edit an existing implementation, but correctness is judged in the resulting world using an independent validator.IGMWorld supplies the execution environment, while IGMBench supplies the task instances.Source: §2 · p. 3

3 OPERATIONALIZING WORLD EDITING

IGMWorld provides a reproducible edit-build-run-observe workflow, and IGMBench supplies tasks with separate instructions and hidden executable checks. Evaluation combines build and load gates, state and behavioral correctness and preservation checks, and visual consistency checks for assets.

3 OPERATIONALIZING WORLD EDITING

  • IGMWorld gives agents a prepared mod scaffold and a fixed 3,600-second budget to edit, build, run, observe logs, and revise before separate evaluation.Tasks share a fixed-seed game environment and toolchain; task-specific executable checks are hidden from the agent.Source: §3 · p. 3
  • IGMBench contains 110 novel tasks authored by 11 experienced annotators across Minecraft and Terraria, with approximately 1.1K criteria and near-even coverage of four intervention levels.Each task separates the natural-language instruction view shown to agents from the verification view reserved for evaluation; median criteria rise from 8 at L1 to 15 at L4.Source: §3 · p. 4

3.1 EVALUATION OF EXECUTABLE WORLD EDITS

  • Evaluation checks whether edits build and load, realize the requested intervention in state and behavior, preserve unrelated properties, and—when assets change—fit the host world's visuals.Preservation checks are targeted rather than exhaustive; validators assess observable world behavior rather than source-code structure.Source: §3.1 · p. 4
  • Executability is a strict gate: submissions must compile into a valid mod package and load in the target game without crashing.World-edit correctness checks run only for submissions that build and load successfully.Source: §3.1 · p. 4
  • For visual tasks, the benchmark separately checks whether assets depict the requested content and match the host world's visual style.TPIPS compares assets with category-matched references under semantic or art-style text factors; calibrated thresholds determine passing scores.Source: §3.1 · p. 4
  • Table 3 reports criterion pass rate and all-criteria task success by intervention level, aggregated across Minecraft and Terraria.It distinguishes the fraction of individual state and behavioral criteria satisfied from the fraction of tasks passing every associated criterion.Source: Table 3 · p. 5

4 CHARACTERIZING WORLD-EDITING CAPABILITY

The evaluation finds substantial functional editing capability, but reliability generally falls as interventions become more coupled. Failures mostly occur after edits build and load, while visual consistency remains a separate weakness.

4.2 RELIABILITY VARIES WITH INTERVENTION DEPTH

  • 31.8%–78.2% of IGMBench tasks are solved under WSR, with GPT-5.6 Sol reaching 78.2%; its CPR reaches 94.8%.WSR requires all state and behavioral criteria for a task, whereas CPR measures the fraction of individual criteria passed.Source: §4.2 · pp. 5–6, §4.1 · p. 5
  • Every configuration reaches its highest WSR at L1, and deeper interventions are generally less reliable even within comparable criterion-count ranges.GPT-5.6 Sol falls from 96.3% WSR at L1 to 48.1% at L4; the figure indicates the depth pattern is not reducible to requirement count alone.Source: §4.2 · p. 6, Figure 2 · p. 6

4.3 MOST FAILURES OCCUR AFTER EXECUTABILITY

  • Behavioral failures dominate at every intervention level, with interaction/progression failures rising from L2 to L4 while resource/registration failures remain relatively stable.Interaction/progression failures reach 43% at L4; resource/registration failures remain at 13–24%.Source: §4.3 · pp. 6–7, Figure 3 · p. 7
  • Successful edits range from introducing entities to adding dynamics and modifying coupled systems; system edits coordinate multiple components rather than merely requiring larger implementations.Representative examples cover Minecraft and Terraria at L2–L4.Source: Figure 4 · p. 7, §4.4 · p. 8

4.4 HOW AGENTS EDIT WORLDS

  • GPT-5.6 Sol, GPT-5.6 Luna, and Claude Opus 4.8 inspect game sources in 91–99% of runs, while agents differ in their use of search, runtime evidence, and asset-construction strategies.Rebuilding after an initial implementation is common across configurations.Source: §4.4 · p. 8

4.5 VISUAL CONSISTENCY IS A DISTINCT BOTTLENECK

  • Every evaluated configuration remains below 0.50 joint visual pass rate, despite native assets reaching 0.80 in Minecraft and 0.78 in Terraria.The strongest joint pass rates are 0.47 in Minecraft and 0.29 in Terraria, and visual performance does not follow functional-editing rankings.Source: Table 4 · p. 6, §4.5 · p. 8

5 IMPLICATIONS OF WORLD EDITING

World editing offers controlled variation of executable worlds while preserving surrounding structure. Its intervention-depth framing may support future research, although those uses are not evaluated here.

5 IMPLICATIONS OF WORLD EDITING

  • World editing constructs controlled variants of an existing executable world while preserving much of its surrounding structure.Intervention depth organizes changes from localized property edits to modifications of dynamics and interacting systems.Source: §5 · pp. 8–9
  • Training, robustness, and generalization are possible downstream uses of this controlled variation, but the paper does not evaluate them.Source: §5 · p. 9
  • World editing complements executable or code-based world models by intervening on an already instantiated world rather than constructing or inferring its representation.Games serve as a concrete, verifiable testbed, while qualitative examples extend the intervention taxonomy to PEAK, Palworld, and Starbound.Source: §5 · p. 9

6 RELATED WORK

Related work primarily studies world generation, prediction, simulation, or game construction. This paper distinguishes editing an already instantiated executable world while maintaining consistency.

6 RELATED WORK

  • World-model research largely generates, predicts, or simulates worlds and their behavior, whereas world editing intervenes on an existing executable world while preserving what should remain unchanged.The paper further organizes interventions by depth, from localized property changes to edits coupling multiple dynamics and systems.Source: §6 · p. 9
  • Code-based world models make dynamics explicit and controllable, but primarily construct or infer executable representations rather than edit an already instantiated world.Source: §6 · p. 9
  • Game-generation and agentic-development work focuses on constructing games or completing development tasks; StarCharM studies Stardew Valley modding as a single-content-type HCI case study without executable evaluation.World editing instead targets modifications to an existing executable world, with behavioral and visual consistency requirements.Source: §6 · p. 9

7 CONCLUSION

The paper defines world editing as intervening on an existing executable world while preserving unrelated properties, with intervention depth reflecting how strongly an edit couples entities, dynamics, and systems. Its evaluation finds that reliability decreases with depth even among tasks with comparable criterion counts, while many unsuccessful edits fail behaviorally rather than at basic executability.

7 CONCLUSION

  • Reliability decreases with intervention depth even within comparable evaluation-criterion-count ranges.The paper frames depth as coupling among entities, dynamics, and systems, not simply the number of requirements.Source: §7 · p. 9
  • Most unsuccessful edits produce executable worlds but fail behavioral validation, while visual integration remains a separate weakness.The paper identifies realizing requested world semantics, rather than basic build correctness, as the main bottleneck.Source: §7 · p. 10
  • The formulation extends beyond game modding, although the quantitative evaluation in this paper uses Minecraft and Terraria.Executable games are presented as a concrete setting for applying and verifying interventions in worlds whose states, dynamics, and systems can be modified.Source: §7 · p. 10

LIMITATIONS

The paper identifies limits in verification coverage, evaluated game ecosystems, evaluated modalities, and the number of agent trials. These boundaries constrain what the results establish about open-ended edits, broader worlds, complete game experiences, and run-to-run reliability.

LIMITATIONS

  • Finite task-specific checks cannot exhaustively verify open-ended modifications, leaving some unintended side effects untested, especially in deeper multi-system edits.The paper points to stronger regression testing and broader runtime coverage, including long-horizon or multiplayer scenarios, as needed for broader verification.Source: LIMITATIONS · p. 10
  • Quantitative evaluation covers Minecraft and Terraria, while other games may have different APIs, asset pipelines, runtime interfaces, and modding constraints.Broader quantitative evaluation remains future work because each environment requires ecosystem-specific execution and validation infrastructure.Source: LIMITATIONS · p. 10
  • The evaluation targets executable state, behavior, and visual assets, but does not explicitly evaluate audio, animation, or narrative.Reliable automatic evaluation of those modalities would require additional modality-specific infrastructure and validation protocols.Source: LIMITATIONS · p. 10
  • Each model–task pair is evaluated once, so results describe observed configuration-level performance rather than per-task success probabilities across repeated stochastic runs.Repeated evaluations could better estimate variance and reliability but would substantially increase experimental cost.Source: LIMITATIONS · p. 10

ETHICS STATEMENT

The authors describe the provenance and release boundaries of IGMWorld and IGMBench, then provide supplementary examples of edit failures and generated asset styles.

ETHICS STATEMENT

  • The authors created IGMWorld’s source code and IGMBench’s tasks from scratch and do not release game binaries, proprietary assets, or other copyrighted game files.The release relies on official or community modding APIs and toolchains; users must obtain games and compatible toolchains independently and comply with applicable terms.Source: ETHICS STATEMENT · p. 10
  • IGMBench tasks are designed to evaluate benign world-editing capabilities and do not intentionally request harmful or deceptive modifications..Source: ETHICS STATEMENT · p. 10

A.1 MORE REPRESENTATIVE EXAMPLES

  • Representative failures across entity, dynamics, and system interventions include missing registration or assets, incorrect mechanic execution, and unintended system-level behavior.Figure 6 presents examples from Minecraft and Terraria.Source: Figure 6 · p. 18, §A.1 · p. 18
  • Image-generated textures are described as smoother and higher-contrast than native Minecraft assets, while programmatic ones are flatter and more geometric; in Terraria, image-generated assets better match the dense, outlined style.Programmatic Terraria assets remain visibly simpler and more geometric than native textures.Source: Figure 7 · p. 18, §A.1 · p. 18, Figure 8 · p. 19

B DETAILED RESULTS

The detailed results describe benchmark measures and regression checks, analyze where failures manifest and how agents debug, and report an ablation of host-provided skills. The failure analysis distinguishes behavioral and ecosystem-integration breakdowns, while the skills ablation reports improved task-level completion without corresponding gains in visual quality.

B DETAILED RESULTS

  • Tables 5–8 cover build, criterion, and task-level functional performance, visual quality, regression preservation, and cost efficiency.The regression analysis includes 225 checks across 30 tasks, primarily at L1; checks are reported over those reached by each configuration.Source: B DETAILED RESULTS · p. 19, Table 5 · p. 19, Table 6 · p. 20

C FAILURE STUDY AND AGENT BEHAVIORS

  • Most audited failed submissions progress past compilation and loading, with failures instead appearing during behavioral validation.Interaction/progression and mechanic semantics are the most common observed failure domains.Source: C FAILURE STUDY AND AGENT BEHAVIORS · p. 20, Table 9 · p. 22
  • Figure 9 groups failures into broad categories and specific types, but the categories indicate where failures manifest rather than necessarily identifying root causes.The study notes that many cases remain unresolved with respect to underlying cause.Source: C FAILURE STUDY AND AGENT BEHAVIORS · p. 20, Figure 9 · p. 21
  • Ecosystem-specific errors such as outdated resource conventions, initialization order, and loader assumptions can prevent correctly reasoned edits from working in the running game.These cases distinguish world-edit reasoning from conformity with the target ecosystem.Source: C FAILURE STUDY AND AGENT BEHAVIORS · p. 21
  • Some strong configurations show more tool use, probing, and log inspection on failing runs, and rebuilding after an initial implementation is common across observable configurations.The observed pattern is an iterative edit–build–inspect–repair workflow, though other configurations show less rebuilding and source inspection on failed runs.Source: C FAILURE STUDY AND AGENT BEHAVIORS · pp. 21–22

D.1 USE OF AGENT SKILLS

  • 64.5% with skills versus 57.3% without skills overall WSR for GPT-5.6 Luna; at L4, WSR is 63.0% with skills versus 33.3% without.The no-skill condition removes both game-specific modding skills and Agent Sprite Forge while leaving other tools and inference settings unchanged.Source: §D.1 · p. 24, Table 12 · p. 24
  • Overall CPR is 88.5% with skills and 89.2% without, while the no-skill condition achieves slightly higher visual pass rates in both games.The authors conclude that skills mainly support functional implementation and ecosystem integration, while visual asset quality follows different agent choices and tools.Source: §D.1 · p. 24, Table 13 · p. 24

D.2 ALTERNATIVE VISUAL ASSET METRICS

The appendix compares a second visual-style metric with the main evaluator, details executable checks for both games, and reports calibration and sensitivity evidence for visual evaluation. It also documents benchmark setup and task-genre distributions.

D.2 ALTERNATIVE VISUAL ASSET METRICS

  • TPIPS conditioned on “art style” achieves 0.66 AUC against majority human judgments, versus 0.54 for CSD.The comparison uses nine raters’ judgments on 40 blind-sampled textures; the paper uses TPIPS for style consistency in its main evaluation pipeline.Source: §D.2 · p. 25, Table 14 · p. 25

E.1 AGENT AND ENVIRONMENT CONFIGURATION

  • Table 15 describes agent configurations, while Table 16 identifies the two game Docker environments used by IGMWorld.The configurations specify a backbone, coding harness, inference effort, and image-generation backend; models support approximately 1M-token context windows.Source: Table 15 · p. 26, Table 16 · p. 26

E.3 EVALUATING WORLD-EDIT CORRECTNESS

  • World-edit checks define behaviors and tests explicitly, and the combined evaluation has a 508.1-second per-task p95 runtime.Behaviors specify seven fields; tests define a fixture, trigger, measurement, acceptance rule, and prerequisite gates, with approximately six hours for a full run using five parallel workers.Source: §E.3 · p. 26, Table 17 · p. 27

E.3.1 MINECRAFT DETERMINISTIC CHECKS DESIGN

  • Minecraft checks use RCON, Mineflayer, and a Fabric client to set up tests, perform actions, and observe outcomes.The evaluator also discovers runtime subjects and interfaces, controls timing where supported, and tests stochastic requirements through sampling, parameter inspection, boundary tests, or bounded enumeration.Source: §E.3.1 · pp. 27–28

E.3.2 TERRARIA DETERMINISTIC CHECKS DESIGN

  • Terraria checks use an evaluator mod to set up and trigger tests, then send measurements to an external verification engine.The mod discovers subjects from loaded registries; supported stochastic checks use fixed-budget trials, including joint-outcome checks for crafting.Source: §E.3.2 · p. 28

E.4 EVALUATING VISUAL CORRECTNESS

  • With α = 0.15, the human calibration study found its highest mean balanced accuracy, while visual rankings and verdicts remained stable across tested k values.The study was a small calibration sanity check: nine raters assessed 40 textures, with modest inter-rater agreement. For tested k values, rank correlation was at least 0.94 and at least 88% of textures retained their verdict; k = 5 was selected as a middle setting.Source: §E.4 · pp. 28–29, Table 19 · p. 30

E.2 IGMBENCH SETUP

  • IGMBench materials include an example agent prompt, task specification, and evaluation specification.The task specification records metadata, intervention level, request, and criteria; the evaluation specification defines a controlled setup and executable checks.Source: Figure 11 · p. 31, Figure 12 · p. 31, Figure 13 · p. 32

F IGMBENCH DIVERSITY

Related papers

Loading 2610.02331v1…