mlsanityData to 7 Oct 2026

MIMIC-Py: An Extensible Tool for Personality-Driven Automated Game Testing with Large Language Models

arXiv 2604.07752 ↗First posted 9 Apr 2026cs.SE cs.AIcited by 1

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Abstract (arXiv)

Modern video games are complex, non-deterministic systems that are difficult to test automatically at scale. Although prior work shows that personality-driven Large Language Model (LLM) agents can improve behavioural diversity and test coverage, existing tools largely remain research prototypes and lack cross-game reusability. This tool paper presents MIMIC-Py, a Python-based automated game-testing tool that transforms personality-driven LLM agents into a reusable and extensible framework. MIMIC-Py exposes personality traits as configurable inputs and adopts a modular architecture that decouples planning, execution, and memory from game-specific logic. It supports multiple interaction mechanisms, enabling agents to interact with games via exposed APIs or synthesized code. We describe the design of MIMIC-Py and show how it enables deployment to new game environments with minimal engineering effort, bridging the gap between research prototypes and practical automated game testing. The source code and a demo video are available on our project webpage: https://mimic-persona.github.io/MIMIC-Py-Home-Page/.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 32 references that are in the map. First posted on arXiv 9 Apr 2026.

Builds on

10 of 32 references.

References · 10

  1. 2020ALFWorld: Aligning Text and Embodied Environments for Interactive Learningcited by 1,143
  2. 2023Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memorycited by 288
  3. 2024Evaluation of Retrieval-Augmented Generation: A Surveycited by 146
  4. 2023ChessGPT: Bridging Policy Learning and Language Modelingcited by 73
  5. 2023Auto MC-Reward: Automated Dense Reward Design with Large Language Models for Minecraftcited by 60
  6. 2023See and Think: Embodied Agent in Virtual Environmentcited by 54
  7. 2024Odyssey: Empowering Minecraft Agents with Open-World Skillscited by 26
  8. 2018On Reinforcement Learning for Full-length Game of StarCraftcited by 15
  9. 2024Robust Imitation Learning for Automated Game Testingcited by 4
  10. 2025Agents in the Sandbox: End-to-End Crash Bug Reproduction for Minecraftcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026AI for Games in the Foundation Model Eracited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2025Leveraging LLM Agents for Automated Video Game Testing
  2. 2025Test-Time-Matching: Decouple Personality, Memory, and Linguistic Style in LLM-based Role-Playing Language Agent
  3. 2024LLMs May Not Be Human-Level Players, But They Can Be Testers: Measuring Game Difficulty with LLM Agents
  4. 2025Orak: A Foundational Benchmark for Training and Evaluating LLM Agents on Diverse Video Games
  5. 2026Beyond Cooperative Simulators: Generating Realistic User Personas for Robust Evaluation of LLM Agents
  6. 2019Automated Video Game Testing Using Synthetic and Human-Like Agents
  7. 2026GameASG-Bench: Benchmarking Autonomous Software Generation for Game Development
  8. 2025Psychologically Enhanced AI Agents