# GameWorld: Towards Standardized and Verifiable Evaluation of Multimodal Game Agents

arXiv: https://arxiv.org/abs/2604.07429 · HTML: https://mlsanity.com/paper/2604.07429/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 20 of its 86 references that are in the map.
- First posted on arXiv 8 Apr 2026.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)
- LLM agents and tool use / [Reinforcement learning for LLM agents](https://mlsanity.com/agents/reinforcement-learning-for-llm-agents/)

## Builds on (most cited first, 20 of 20)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://mlsanity.com/paper/2307.13854/) (2023)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Gemini: A Family of Highly Capable Multimodal Models](https://mlsanity.com/paper/2312.11805/) (2023)
- [Kimi K2: Open Agentic Intelligence](https://mlsanity.com/paper/2507.20534/) (2025)
- [GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning](https://mlsanity.com/paper/2507.01006/) (2025)
- [Seed1.5-VL Technical Report](https://mlsanity.com/paper/2505.07062/) (2025)
- [JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models](https://mlsanity.com/paper/2311.05997/) (2023)
- [Cradle: Empowering Foundation Agents Towards General Computer Control](https://mlsanity.com/paper/2403.03186/) (2024)
- [DeepMind Lab](https://mlsanity.com/paper/1612.03801/) (2016)
- [The Dawn of GUI Agent: A Preliminary Case Study with Claude 3.5 Computer Use](https://mlsanity.com/paper/2411.10323/) (2024)
- [See and Think: Embodied Agent in Virtual Environment](https://mlsanity.com/paper/2311.15209/) (2023)
- [ASSISTGUI: Task-Oriented Desktop Graphical User Interface Automation](https://mlsanity.com/paper/2312.13108/) (2023)
- [JARVIS-VLA: Post-Training Large-Scale Vision Language Models to Play Visual Games with Keyboards and Mouse](https://mlsanity.com/paper/2503.16365/) (2025)
- [Lumine: An Open Recipe for Building Generalist Agents in 3D Open Worlds](https://mlsanity.com/paper/2511.08892/) (2025)
- [DeepPHY: Benchmarking Agentic VLMs on Physical Reasoning](https://mlsanity.com/paper/2508.05405/) (2025)
- [V-MAGE: A Game Evaluation Framework for Assessing Vision-Centric Capabilities in Multimodal Large Language Models](https://mlsanity.com/paper/2504.06148/) (2025)

## Built on by (most cited first, 28 of 28)

- [Show-Harness: Just a VLM Agent Can Play Robots](https://mlsanity.com/paper/2609.10522/) (2026)
- [GUI Agents for Continual Game Generation](https://mlsanity.com/paper/2605.28258/) (2026)
- [Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond](https://mlsanity.com/paper/2604.22748/) (2026)
- [Towards Interactive Video World Modeling: Frontiers, Challenges, Benchmarks, and Future Trends](https://mlsanity.com/paper/2606.01164/) (2026)
- [MineExplorer: Evaluating Open-World Exploration of MLLM Agents in Minecraft](https://mlsanity.com/paper/2605.30931/) (2026)
- [Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse](https://mlsanity.com/paper/2605.09965/) (2026)
- [SPIKE: An Adaptive Dual Controller Framework for Cost-Efficient Long-Horizon Game Agents](https://mlsanity.com/paper/2605.18636/) (2026)
- [CAST: Game Solvers as Turn-Level Teachers for LLM Agents](https://mlsanity.com/paper/2607.25308/) (2026)
- [Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning](https://mlsanity.com/paper/2608.03571/) (2026)
- [Agentic Game Development as a Verifiable Trajectory Data Engine for Scaling World Models](https://mlsanity.com/paper/2608.25518/) (2026)
- [AI for Games in the Foundation Model Era](https://mlsanity.com/paper/2609.16679/) (2026)
- [MirrorCraft: Paired Evaluation under Hidden Rule Changes in Minecraft](https://mlsanity.com/paper/2607.29218/) (2026)
- [UrbanGround: From Local Perception to Spatial Agency in a Real-Scale City](https://mlsanity.com/paper/2608.27456/) (2026)
- [VISTA: A Visual Harness for Reasoning in an Interactive World](https://mlsanity.com/paper/2610.02200/) (2026)
- [Beyond the Current Observation: Evaluating Multimodal Large Language Models in Controllable Non-Markov Games](https://mlsanity.com/paper/2606.19338/) (2026)
- [From Generation to Simulation: How Far Are World Models from Being True Simulators?](https://mlsanity.com/paper/2608.23070/) (2026)
- [V-ICAL Bench: Evaluating Video In-Context Learning for Multimodal Agents in Interactive Environments](https://mlsanity.com/paper/2609.15683/) (2026)
- [GameHorizon Suite: Multi-Horizon Data and Evaluation in Gameplay](https://mlsanity.com/paper/2609.25001/) (2026)
- [GameBoyWorlds: A Testbed for Self-Improvement in Embodied Video Games](https://mlsanity.com/paper/2609.32093/) (2026)
- [LongPuzzleBench: Evaluating GUI Agents on Long-Horizon Visual Puzzles](https://mlsanity.com/paper/2609.34769/) (2026)
- [Planarian: Managing Agent State with Statepoints](https://mlsanity.com/paper/2609.35366/) (2026)
- [A2Z GameSpec-Bench: How Faithfully Can Coding Agents Generate Games from Game Design Specifications?](https://mlsanity.com/paper/2609.39564/) (2026)
- [Separating Decision Time from Decision Quality in the Real-Time Gap of Distilled Deciders: Evidence from a Game and a Conveyor Simulator](https://mlsanity.com/paper/2610.04810/) (2026)
- [SpeedrunBench: Challenging LLM Agents with Video Game Speedrunning](https://mlsanity.com/paper/2610.08076/) (2026)
- [Mine Odyssey: Benchmarking Spatial Agentic Intelligence in the Wild](https://mlsanity.com/paper/2610.11328/) (2026)
- [Video-Arena: A Scalable Agentic Video Benchmark for Spatiotemporal Capability](https://mlsanity.com/paper/2610.14158/) (2026)
- [Game2Real-Bench: Do Toy Games Reveal and Transfer to Real-World Visual Reasoning?](https://mlsanity.com/paper/2610.18834/) (2026)
- [Player-1: Learning to Play from Synthetic Experience](https://mlsanity.com/paper/2610.20584/) (2026)

## Abstract (arXiv)

Towards an embodied generalist for real-world interaction, Multimodal Large Language Model (MLLM) agents still suffer from challenging latency, sparse feedback, and irreversible mistakes. Video games offer an ideal testbed with rich visual observations and closed-loop interaction, demanding fine-grained perception, long-horizon planning, and precise control. However, systematically evaluating these capabilities is currently hindered by heterogeneous action interfaces and heuristic verification. To this end, we introduce GameWorld, a benchmark designed for standardized and verifiable evaluation of MLLMs as generalist game agents in browser environments. Two game agent interfaces are studied: (i) computer-use agents that directly emit keyboard and mouse controls, and (ii) generalist multimodal agents that act in a semantic action space via deterministic Semantic Action Parsing. GameWorld contains 34 diverse games and 170 tasks, each paired with state-verifiable metrics for outcome-based evaluation. The results across 18 model-interface pairs suggest that even the best performing agent is far from achieving human capabilities on video games. Extensive experiments of repeated full-benchmark reruns demonstrate the robustness of the benchmark, while further studies on real-time interaction, context-memory sensitivity, and action validity expose more challenges ahead for game agents. Together, by offering a standardized, verifiable, and reproducible evaluation framework, GameWorld lays a robust foundation for advancing research on multimodal game agents and beyond. The project page is at https://gameworld-bench.github.io.