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arxiv: 2504.06148 · v3 · submitted 2025-04-08 · 💻 cs.CV

V-MAGE: A Game Evaluation Framework for Assessing Vision-Centric Capabilities in Multimodal Large Language Models

Xiangxi Zheng , Linjie Li , Zhengyuan Yang , Ping Yu , Alex Jinpeng Wang , Rui Yan , Yuan Yao , Lijuan Wang This is my paper

Pith reviewed 2026-05-22 20:03 UTC · model grok-4.3

classification 💻 cs.CV
keywords multimodal large language modelsvision evaluationgame benchmarksinteractive reasoningdynamic perceptionAI assessment framework
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The pith

Leading multimodal AI models match humans on simple visual tasks but lag far behind in complex interactive scenarios that demand reasoning and orchestration.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces V-MAGE, a framework that tests multimodal large language models using video games set in free-form, visually complex environments. Models receive only visual input and must interpret changing game states to make decisions, much like human players across over thirty scenarios in five different games. Tests against human baselines show top models approach human performance on straightforward challenges yet decline sharply when tasks require advanced reasoning or coordinating multiple actions over time. This gap points to core weaknesses in handling vision-based control in continuous, interactive settings. The framework applies a dynamic ELO ranking system to enable fair comparisons across varying difficulties and task types.

Core claim

V-MAGE is a game evaluation framework designed to assess vision-centric capabilities in MLLMs through interactive, continuous-space video game scenarios. Benchmarking reveals that while leading models approach human-level performance in simple tasks, their performance drops significantly in complex scenarios requiring advanced reasoning and task orchestration. This highlights fundamental limitations in current MLLMs' ability to perform vision-grounded, interactive frame-by-frame control in simulated continuous-time environments.

What carries the argument

The V-MAGE framework, consisting of five video games with over 30 evaluation scenarios in free-form visually complex environments, evaluated using a dynamic ELO-based ranking system to compare models across different difficulties.

If this is right

  • Models need targeted advances in dynamic perception to close gaps in interactive control.
  • The identified drops in complex scenarios point to limits in current reasoning for task orchestration.
  • V-MAGE supplies a structured way to track improvements in vision-grounded decision making over time.
  • Persistent gaps underscore challenges in frame-by-frame visual processing for continuous environments.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar game setups could help test how these models might handle real-world tasks like robot navigation or object manipulation.
  • Extending the scenarios to new games might uncover additional weaknesses in perception under varied conditions.
  • Training approaches focused on sequential visual feedback could help reduce the observed performance differences.

Load-bearing premise

The chosen video games and scenarios in free-form continuous-space environments accurately reflect the dynamic perception and interactive reasoning abilities that matter for real-world applications.

What would settle it

If leading models achieve comparable results to humans on a different set of real-world interactive visual control tasks that do not involve the selected games, this would indicate the performance gaps may not generalize beyond the chosen scenarios.

Figures

Figures reproduced from arXiv: 2504.06148 by Alex Jinpeng Wang, Lijuan Wang, Linjie Li, Ping Yu, Rui Yan, Xiangxi Zheng, Yuan Yao, Zhengyuan Yang.

Figure 1
Figure 1. Figure 1: The overview of the V-MAGE benchmark, designed to evaluate vision-centric capabilities and higher￾level reasoning of MLLMs across 5 free-form games with 30+ levels. V-MAGE assesses critical abilities in visual reasoning, providing a comprehensive evaluation of model performance in complex, dynamic environments. While progress has been made in game-based MLLM benchmarks, current approaches predominantly rel… view at source ↗
Figure 2
Figure 2. Figure 2: V-MAGE games and evaluation pipeline. V-MAGE employs five distinct games, each with several levels, to facilitate a decomposed evaluation of model performance. These games include FlappyBird, Race, SuperMario, Pong and TempestRun. During the evaluation process, the Agent module receives visual game state information directly from the Game module, primarily in the form of screenshots. The Agent module then … view at source ↗
Figure 3
Figure 3. Figure 3: Race level design. Six levels progressively increase in difficulty while sharing the core objective: navigating a car to a trophy. Detailed Race level config￾urations are provided in Appendix Table18. Existing game-based benchmarks indicate that MLLMs frequently struggle to achieve meaning￾ful scores at standard human-level difficulties in conventional game-based benchmarks (Zhang et al. [2024], Wang et al… view at source ↗
Figure 4
Figure 4. Figure 4: The MLLM trails humans by a large margin in all six games. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Capability maps of the underlying visual capabilities of each model. 10 >10 Qwen2.5VL-7B InternVL2.5-8B Qwen2.5VL-72B InternVL2.5-78B GPT4o Gemini-2.0-Flash 0 1 2 3 4 Original Score W/Text Improvement Random Baseline Human 10 >10 Qwen2.5VL-7B InternVL2.5-8B Qwen2.5VL-72B InternVL2.5-78B GPT4o Gemini-2.0-Flash 0 1 2 3 4 5 [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Error type probability distribution for GPT4o-2024-08-06 across 494 samples. Analysis of Model Errors in V-MAGE. For GPT-4o-2024-08-06’s complete inputs and responses across all game levels after one to two rounds of gameplay, we uniformly sampled 494 interaction sets for manual annotation and categorized the primary error types. The visualization results depicting the distribution of these errors are pres… view at source ↗
Figure 8
Figure 8. Figure 8: Case examples illustrating Perception Error and Reasoning Error in FlappyBird and Race. The FlappyBird example shows a Perception Error where the model misjudges the bird’s vertical position relative to the pipe gap. The Race example illustrates a Reasoning Error where the model fails to plan a path around a vertical obstacle between the car and the trophy, resulting in a suboptimal action. V-MAGE Poses Si… view at source ↗
Figure 9
Figure 9. Figure 9: RaceGame Level 1: Level Design and Prompt Overview. [PITH_FULL_IMAGE:figures/full_fig_p023_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: RaceGame Level 2-3: Level Design and Prompt Overview. [PITH_FULL_IMAGE:figures/full_fig_p024_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: RaceGame Level 1-3 No History: Level Design and Prompt Overview. [PITH_FULL_IMAGE:figures/full_fig_p025_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: RaceGame Level 4: Level Design and Prompt Overview. [PITH_FULL_IMAGE:figures/full_fig_p026_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: RaceGame Level 5-6: Level Design and Prompt Overview. [PITH_FULL_IMAGE:figures/full_fig_p027_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: SuperMario Level 1: Level Design and Prompt Overview. [PITH_FULL_IMAGE:figures/full_fig_p028_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: SuperMario Level 2-4: Level Design and Prompt Overview. [PITH_FULL_IMAGE:figures/full_fig_p029_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: SuperMario Level 5-9: Level Design and Prompt Overview. [PITH_FULL_IMAGE:figures/full_fig_p030_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: SuperMario Level 10 (Standard Level): Level Design and Prompt Overview. [PITH_FULL_IMAGE:figures/full_fig_p031_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: FlappyBird Level 1-3: Level Design and Prompt Overview. [PITH_FULL_IMAGE:figures/full_fig_p032_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: FlappyBird Level 4-6: Level Design and Prompt Overview. [PITH_FULL_IMAGE:figures/full_fig_p033_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: FlappyBird Level 7 (Standard Level): Level Design and Prompt Overview. [PITH_FULL_IMAGE:figures/full_fig_p034_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: PongGame Level 1-3: Level Design and Prompt Overview. [PITH_FULL_IMAGE:figures/full_fig_p035_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Tempest Run Level 1-4: Level Design and Prompt Overview. [PITH_FULL_IMAGE:figures/full_fig_p036_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: In the prior reasoning processes, GPT-4o accurately identified the car’s orientation relative to the trophy’s position. However, in the final reasoning instance, due to anchoring bias, the model misinterpreted previous historical information and incorrectly assumed that the car’s orientation was already directed toward the trophy. Consequently, despite correctly determining the direction, the model execut… view at source ↗
Figure 24
Figure 24. Figure 24: case studies group 1. GPT-4o Observation: The character is currently running in the tunnel. There is a red spike directly ahead on the ground, a purple wall to the left, and a green enemy on the path in front. Reasoning: The red spike must be avoided by jumping over it. The green enemy can be dealt with by using the SLIDE action to kick it. The purple wall is on the left, so it is safe to move right. Acti… view at source ↗
Figure 25
Figure 25. Figure 25: case studies group 2. 41 [PITH_FULL_IMAGE:figures/full_fig_p041_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: Error analysis in GPT4o cases. 42 [PITH_FULL_IMAGE:figures/full_fig_p042_26.png] view at source ↗
Figure 27
Figure 27. Figure 27: Error analysis in GPT4o cases. 43 [PITH_FULL_IMAGE:figures/full_fig_p043_27.png] view at source ↗
Figure 28
Figure 28. Figure 28: Error analysis in GPT4o cases. 44 [PITH_FULL_IMAGE:figures/full_fig_p044_28.png] view at source ↗
Figure 29
Figure 29. Figure 29: Error analysis in GPT4o cases. 45 [PITH_FULL_IMAGE:figures/full_fig_p045_29.png] view at source ↗
read the original abstract

Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in visual-text processing. However, existing static image-text benchmarks are insufficient for evaluating their dynamic perception and interactive reasoning abilities. We introduce Vision-centric Multiple Abilities Game Evaluation (V-MAGE), a novel game-based evaluation framework designed to systematically assess MLLMs' visual reasoning in interactive, continuous-space environments. V-MAGE features five distinct video games comprising over 30 carefully constructed evaluation scenarios. These scenarios are set in free-form, visually complex environments that require models to interpret dynamic game states and make decisions based solely on visual input, thereby closely reflecting the conditions encountered by human players. To ensure robust and interpretable comparisons across models, V-MAGE employs a dynamic ELO-based ranking system that accounts for varying difficulty levels and task diversity. Benchmarking state-of-the-art MLLMs against human baselines reveals that while leading models approach human-level performance in simple tasks, their performance drops significantly in complex scenarios requiring advanced reasoning and task orchestration. This persistent performance gap highlights fundamental limitations in current MLLMs' ability to perform vision-grounded, interactive frame-by-frame control in simulated continuous-time environments. Through extensive analyses, we demonstrate the utility of V-MAGE in uncovering these limitations and providing actionable insights for improving the visual and reasoning capabilities of MLLMs in dynamic, interactive settings. Code is publicly available at https://github.com/CSU-JPG/V-MAGE.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper introduces V-MAGE, a game-based evaluation framework using five video games and over 30 scenarios in free-form continuous-space environments to assess vision-centric capabilities of MLLMs. It benchmarks state-of-the-art models against human baselines with a dynamic ELO ranking system and claims that leading MLLMs approach human-level performance on simple tasks but exhibit significant drops in complex scenarios requiring advanced reasoning and task orchestration, highlighting limitations in vision-grounded interactive frame-by-frame control.

Significance. If the performance-gap results hold under rigorous controls, this work is significant for providing a dynamic benchmark that addresses gaps in static image-text evaluations for MLLMs. The public code release supports reproducibility, and the ELO-based system offers a principled way to handle task diversity. The empirical findings with external human baselines can inform targeted improvements in interactive visual reasoning.

major comments (1)
  1. Abstract: the central claim of significant performance drops in complex scenarios would benefit from explicit reporting of error bars, statistical significance tests, or details on scenario construction and prompting controls to confirm the gap is not an artifact of the evaluation protocol.
minor comments (2)
  1. Abstract: the description of 'free-form, visually complex environments' could include one concrete example of a scenario to illustrate the dynamic perception requirements.
  2. The ELO ranking description should clarify how difficulty levels are normalized across the five games to ensure interpretable cross-model comparisons.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback and recommendation for minor revision. We address the major comment below.

read point-by-point responses

  1. Referee: Abstract: the central claim of significant performance drops in complex scenarios would benefit from explicit reporting of error bars, statistical significance tests, or details on scenario construction and prompting controls to confirm the gap is not an artifact of the evaluation protocol.

    Authors: We agree that strengthening the central claim with additional statistical rigor and methodological transparency is beneficial. In the revised manuscript, we will add error bars to the key performance figures in the results section, report statistical significance tests (e.g., paired t-tests or Wilcoxon tests) for the differences between simple and complex scenarios, and expand Section 3 with explicit details on scenario construction criteria and prompting controls. These changes will be briefly referenced in the abstract to support the reported performance gaps. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical benchmark only

full rationale

This is an empirical evaluation paper that introduces game-based scenarios, applies a standard dynamic ELO ranking system, and reports performance gaps against independent human baselines. No load-bearing derivations, fitted-parameter predictions, self-definitional steps, or self-citation chains appear in the abstract or described framework. The central claims rest on external measurements rather than reducing to the paper's own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Framework rests on domain assumptions about game environments representing real interactive vision tasks and standard benchmark validity practices.

axioms (1)
  • domain assumption Selected video games capture dynamic perception and interactive reasoning abilities relevant to real-world use cases
    Invoked when claiming the framework reflects conditions encountered by human players

pith-pipeline@v0.9.0 · 5817 in / 1037 out tokens · 47103 ms · 2026-05-22T20:03:32.523935+00:00 · methodology

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Forward citations

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Reference graph

Works this paper leans on

31 extracted references · 31 canonical work pages · cited by 4 Pith papers

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    Mario can jump (actions involving UP) only if he is on the ground or on a solid surface like a platform or pipe

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    You can make six types of actions to control Mario:

    If Mario is in mid-air, he can only use LEFT or RIGHT to adjust his position, or NONE to continue falling or moving with momentum. You can make six types of actions to control Mario:

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    UP: Makes Mario jump upward (only available when Mario is on the ground or solid platforms)

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    LEFT: Moves Mario left

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    RIGHT: Moves Mario right

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    UP+LEFT: Makes Mario jump upward and left simultaneously (only available when on the ground or solid platforms)

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    UP+RIGHT: Makes Mario jump upward and right simultaneously (only available when on the ground or solid platforms)

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    Note that DOWN has no effect and cannot be used, so you should never attempt to use it

    NONE: No new action is performed; Mario continues to be affected by gravity (if airborne) or momentum from previous movements. Note that DOWN has no effect and cannot be used, so you should never attempt to use it. You should think step by step and respond with the following format, remember to respond with plain text without any special characters or sym...

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    UP: Makes the bird rise a bit of distance

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    DOWN: Makes the bird fall a bit of distance

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    KEEP: The bird will keep the current position. You should think step by step and response with the following format, remember to response the plain text without any special characters or symbols, DO NOT response in markdown or Latex format. Observation: ... (describe the current position of the bird and the gap.) Reasoning: ... (think step by step and exp...

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    UP: Makes the bird rise

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    LEFTUP: Moves the left paddle up

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    LEFTDOWN: Moves the left paddle down

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    Use RISE to return to a normal running position after a SLIDE

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    You can make six types of actions to control the character:

    NONE is a valid action to maintain the current state if no immediate action is needed. You can make six types of actions to control the character:

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    JUMP: Makes the character jump upward, useful for avoiding ground obstacles like red spikes

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    LEFT: Moves the character to the left

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    SLIDE: Makes the character duck and slide forward, useful for dealing with green enemies or passing under certain obstacles

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    RISE: Returns the character to a normal running position after sliding

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    NONE: No new action is performed; the character maintains their current trajectory. You should think step by step and respond with the following format, remember to respond with plain text without any special characters or symbols, DO NOT respond in markdown or Latex or any other format. Response: Observation: ... (Describe the character's current positio...

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