ByteSized32Refactored: Modular Text Game Corpus

• ByteSized32Refactored is a modular, refactored corpus that centralizes game logic to support scalable LLM text game generation and interactive world modeling.
• It leverages a foundation library with seven base classes to unify abstractions and streamline scenario-specific extensions.
• Experiments show that iterative self-reflection improves LLM alignment with task specifications and winnability despite initial abstraction challenges.

ByteSized32Refactored is a modular, extensible, and hierarchically organized corpus for text game generation, purpose-built for research in interactive world modeling and evaluation within LLMs. It refactors and condenses the original ByteSized32 implementation, centralizing reusable logic and abstractions in a foundation library and providing a unified codebase suited to extensible scenario development and rigorous LLM-driven experimentation. The design profoundly influences both code organization and the methodology by which LLMs interact with structured simulation environments.

1. Refactoring Strategy and Architectural Changes

ByteSized32Refactored was constructed by systematically redesigning the original ByteSized32 corpus, which comprised approximately 20,000 lines of Python spread across 32 individual text games. The primary refactoring innovations include:

• Reduction of total code size to ~10,000 lines.
• Isolation and centralization of common logic previously scattered throughout individual game files.
• Replacement of verbose if/elif step() action dispatch with a dictionary-driven dispatch pattern, e.g.,

  action_map = { "open": open_handler, "close": close_handler, # ... } return action_map.get(user_input, default_handler)()

• Optimization of string construction within descriptive methods such as makeDescriptionStr(), leveraging functions like join() to minimize expensive concatenation.

Redundant game-specific logic was moved out of individual files and into a foundation library, allowing the games themselves to focus exclusively on scenario-specific extensions.

2. Foundation Library: GameBasic.py and Base Class Abstractions

GameBasic.py is the central resource for shared functionality. It defines exactly seven base classes that underpin the codebase:

Base Class	Purpose	Extensibility Interface
GameObject	Root entity for objects	Attribute/method overrides
Container	Models containment relationships	Methods for insert/remove
Device	Interactive/stateful objects	State-change and event hooks
Substance	Tracks physical state (e.g., liquids)	State modeling routines
World	Governs environment context and dynamics	Rules definition, scheduling
Agent	Player/acting entity	Perception/action interface
TextGame	Orchestrates game flow	World setup, task specification

Each class offers defined function interfaces (such as initializeWorld(), getTaskDescription(), etc.), and the layered design (as shown in the paper’s Figure 1) provides unified abstraction at lower levels with scenario-specific extension via inheritance at higher levels.

3. Modularity and Corpus Extensibility

The refactored architecture enables rapid and reliable extensibility with several mechanisms:

• Centralized logic in GameBasic.py ensures that the addition of new game scenarios does not require duplication of core routines.
• Developers implement only domain-specific objects and task logic, inheriting common management, dispatch, and environmental mechanics from the base classes.
• Dictionary-based action dispatch and efficient string handling improve code maintainability and facilitate extension to new tasks or specifications without regression in the unified framework.
• Specialization of behavior through method overriding preserves compliance with interaction and evaluation pipelines established for LLM-driven analysis.

This modularity ensures that, as new scenario requirements emerge, they can be accommodated with minimal reengineering and maximum code reuse.

4. LLM Performance Evaluation on ByteSized32Refactored

Experiments with GPT-4o on ByteSized32Refactored reveal nuanced outcomes:

• Initial technical validity (encompassing proper initialization, runnable status, and valid action generation) tends to be lower compared to the original corpus. This is attributed to increased abstraction in code structure, which places greater reasoning demand on LLMs during game generation.
• After multiple rounds of self-reflection, GPT-4o exhibits marked improvements in physical reality alignment, compliance to task specifications, and winnability. This refinement demonstrates the model’s capacity to leverage hierarchical structure once the abstraction is properly internalized.
• The combination of lower initial technical validity and higher specification/winnability in successive iterations suggests that the refactored codebase introduces new challenges in LLM world modeling and evaluation, specifically in the field of reasoning about abstracted logic rather than explicit, procedural steps.

A plausible implication is that future LLM architectures or prompt engineering strategies may need to incorporate adaptive reasoning about code hierarchies to fully exploit the benefits of modular design.

5. Scalability, Demonstration Formatting, and Future Prospects

ByteSized32Refactored provides the underlying architecture for scalable simulation-driven research:

• The centralization and reduction of code size directly increases the number of code demonstrations that can fit within limited context windows during LLM evaluation and training.
• Flexible demonstration formatting, enabled by modular code structure, supports both single-shot and multi-shot sequences crucial for interactive world modeling tests.
• The architecture is future-proofed for further extensions; new text game scenarios or environment specifications are incorporated by extending base classes and reusing core logic.
• The corpus supports robust evaluation pipelines and opens avenues for more expressive forms of LLM-based world simulation and interaction, with the foundation library serving as the invariant substrate for experimental comparisons.

This suggests that ByteSized32Refactored will serve not only as a testbed for benchmarking LLM performance in text games but also as an adaptable framework for broader research in world modeling, simulation-driven RL, and interactive agent tasks.

6. Summary and Research Impact

ByteSized32Refactored is distinguished by its optimized, extensible architecture centered on the GameBasic.py foundation library—with seven unified abstractions—and a corpus-level codebase that enables efficient scenario expansion, rigorous LLM evaluation, and scalable simulation research. While performance with current LLMs such as GPT-4o indicates that increased abstraction initially impedes technical validity, iterative refinement leads to stronger compliance with specifications and winnability. The modular and hierarchically layered structure is expected to facilitate future extensions, richer demonstration protocols, and more nuanced testing setups, contributing substantially to ongoing developments in LLM-based interactive world modeling and text game generation (Wang et al., 28 Sep 2025).