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Are Large Vision Language Models Good Game Players?

Xinyu Wang, Bohan Zhuang, Qi Wu

arXiv:2503.02358v1cs.CVcs.AIcs.CL

TL;DR

Existing LVLM evaluations often underassess detailed visual perception and multi-turn reasoning. LVLM-Playground uses games to evaluate LVLM abilities in structured environments, and experiments identify weaknesses in long structured outputs and dense visual perception.

  • Problem

    Existing LVLM benchmarks often inadequately assess detailed visual perception and multi-turn reasoning, while training-data overlap can risk contamination.

    Source: §1 · p. 1

  • Method

    LVLM-Playground evaluates LVLMs across six games using a framework that quantifies abilities in perception, reasoning, decision-making, and adversary skills.

    Source: §1 · pp. 2–3

  • Results

    Experiments identify looping behavior in long structured outputs and poor performance on dense visual perception tasks.

    Source: §1 · p. 3

  • Takeaways & Limitations

    LVLM-Playground offers a game-based alternative for evaluating LVLM perception, reasoning, and decision-making abilities.

    Source: §5 · p. 10

Abstract

from arXiv · show

Large Vision Language Models (LVLMs) have demonstrated remarkable abilities in understanding and reasoning about both visual and textual information. However, existing evaluation methods for LVLMs, primarily based on benchmarks like Visual Question Answering and image captioning, often fail to capture the full scope of LVLMs' capabilities. These benchmarks are limited by issues such as inadequate assessment of detailed visual perception, data contamination, and a lack of focus on multi-turn reasoning. To address these challenges, we propose \method{}, a game-based evaluation framework designed to provide a comprehensive assessment of LVLMs' cognitive and reasoning skills in structured environments. \method{} uses a set of games to evaluate LVLMs on four core tasks: Perceiving, Question Answering, Rule Following, and End-to-End Playing, with each target task designed to assess specific abilities, including visual perception, reasoning, decision-making, etc. Based on this framework, we conduct extensive experiments that explore the limitations of current LVLMs, such as handling long structured outputs and perceiving detailed and dense elements. Code and data are publicly available at https://github.com/xinke-wang/LVLM-Playground.

1 INTRODUCTION

The introduction argues that existing LVLM benchmarks inadequately test detailed visual perception, contamination-resistant evaluation, and multi-turn reasoning. It presents LVLM-Playground as a game-based framework and reports weaknesses in current models on long structured outputs and dense visual perception.

1 INTRODUCTION

  • Existing LVLM benchmarks often miss fine-grained visual understanding, risk training-test overlap, and inadequately assess multi-turn reasoning.Source: §1 · pp. 1–2
  • Games combine detailed state perception, rule-based outcomes, consistent prompts, and multi-turn strategy, while game data is largely absent from LVLM training sets.Source: §1 · p. 2
  • LVLM-Playground evaluates LVLMs in six games and systematically measures abilities across perception, reasoning, decision-making, and adversary skills.Source: §1 · pp. 2–3
  • The benchmark integrates game interfaces and AI opponents for online and offline interactions, and the authors evaluate open-source models and commercial APIs.Source: §1 · p. 2
  • Experiments reveal looping behavior in long structured outputs and poor performance on dense visual perception tasks.Source: §1 · p. 3

2 RELATED WORK

Prior LVLM evaluation has expanded from disparate vision-language tasks to broader frameworks, but remains concentrated on VQA-related tasks. Game-based LVLM systems and text-only game benchmarks leave room for a systematic evaluation using visual and textual inputs.

2.1 EVALUATION FOR LVLMS

  • Earlier evaluations used disparate datasets, while later broad frameworks still concentrate on VQA-related tasks and combining benchmarks increases evaluation burden.Source: §2.1 · p. 3

2.2 LVLM FOR GAMES

  • Prior game-agent systems perform control tasks, while game benchmarks for LLMs use text-only states and therefore omit visual perception and multimodal reasoning.Source: §2.2 · pp. 3–4

3 LVLM-PLAYGROUND

LVLM-Playground evaluates LVLMs in turn-based game simulators through tasks that separate visual perception, reasoning, rule application, and full-game play. It rates game demands across four abilities and uses task-specific measures to assess model performance.

3.1 OVERVIEW

  • LVLM-Playground sends visual game states and task prompts to LVLMs, executes their responses in simulators, and returns updated states; competitive games add search-based opponents.Source: §3.1 · pp. 4–5, Figure 1 · p. 4

3.2 GAME SELECTION

  • The benchmark selects six lightweight, turn-based board games with clear win/loss conditions or scoring systems.These include Tic Tac Toe, Reversi, Minesweeper, Gomoku, Sudoku, and Chess.Source: §3.2 · p. 5, Figure 2 · p. 5

3.3 ABILITIES

  • Ability ratings quantify each game's perception, reasoning, decision, and adversary demands using game-state complexity, branching and uncertainty, game length and resources, and opponent interactions.The resulting scores are normalized to a 0.5–5 scale and mapped to half- through five-star ratings.Source: §3.3 · pp. 5–6

3.4 TASKS

  • The four tasks isolate visual transcription, perception-plus-reasoning Q&A, valid-move selection, and full-game play across perception, reasoning, decision-making, and adversarial ability.Source: §3.4 · pp. 6–7

3.5 EVALUATIONS

  • Evaluation uses matrix-cell accuracy for perception, exact-match answers for multiple-choice Q&A, and simulator-checked validity for proposed moves.Source: §3.5 · pp. 7–8
  • End-to-end evaluation considers valid moves, partial game progression, and final outcomes; three invalid moves in one gameplay automatically trigger a loss.Source: §3.5 · p. 8

4 EXPERIMENTS

Experiments evaluate commercial and open-source LVLMs across four game-playing abilities, finding strengths in simpler tasks but persistent difficulties with dense perception, complex rules, and sustained gameplay.

4 EXPERIMENTS

  • The evaluation tested commercial APIs and open-source models under identical token and prompt settings, using 2,000 generated samples for each offline task and 100 gameplays per model for end-to-end play.Offline tasks were Perceiving, Question Answering, and Rule-Following.Source: §4 · p. 8

4.1 RESULTS AND FINDINGS

  • LVLMs perform well on simple games but struggle with complex boards and long structured outputs, including malformed or repetitive Gomoku matrices.Reported examples include Gemini at 0.994 and Claude at 0.985 in Tic Tac Toe, versus Claude at 0.554 in Chess; Gomoku scores include Deepseek-vl-7b at 0.010 and Qwen2-vl-7b at 0.589.Source: §4.1 · pp. 8–9, Table 2 · p. 9
  • GPT-4o lags commercial peers in visual perception, while LLaVA-1.6-7b scores zero in Sudoku and Gomoku perception and falls below random baselines in Q&A.For Gomoku perception, GPT-4o scores 0.013 versus 0.742 for Gemini1.5-pro; for Reversi, it scores 0.195 versus 0.992.Source: §4.1 · p. 9, Table 2 · p. 9
  • Models handle simple rule-following tasks better than complex ones, with most near or below random in Reversi and performance around random in Gomoku.Examples include Gemini at 1.000 in TicTacToe and GPT-4o at 0.070 in Reversi versus a 0.127 random baseline; Gemini reaches 0.625 in Gomoku.Source: §4.1 · pp. 9–10, Table 4 · p. 9
  • Commercial models generally excel in perception and rule comprehension, while open-source models show moderate progress but struggle with complex gameplay.Across the benchmark, key limitations include dense visuals, rule understanding, and sustained play.Source: Figure 4 · p. 10, §4.1 · p. 10
  • In end-to-end play, LVLMs often exit early after repeated invalid moves, producing plausible gameplay language without reliably turning plans into valid actions.In Reversi, open-source models stagnate at the initial score of 40, while commercial models advance further.Source: §4.1 · p. 10, Table 5 · p. 10

5 CONCLUSION

The conclusion presents LVLM-Playground as a game-based framework for evaluating LVLM abilities and reports that experiments reveal limitations in existing models.

5 CONCLUSION

  • LVLM-Playground incorporates six diverse games across four tasks, each targeting different core abilities.The framework is presented as a game-based evaluation of LVLM perception, reasoning, and decision-making.Source: §5 · p. 10
  • The framework offers an alternative for analyzing and comparing LVLMs from multiple perspectives, addressing limitations of current benchmarks.Source: §5 · p. 10
  • Evaluations of open-source models and commercial APIs uncovered findings that highlight limitations of existing models.Source: §5 · p. 10

Appendix

The appendix provides additional details on ability ratings, game settings, and search-based AI opponents.

Appendix

  • Appendix A covers weighting coefficients, game rating calculations, and a human study of difficulty ratings.Source: Appendix · p. 14
  • Appendix B details settings for Perceiving, Question Answering, Rule Following, and End-to-End Playing.Source: Appendix · p. 14
  • Appendix C describes search-based AI opponents for Reversi, Minesweeper, Gomoku, and Chess.Source: Appendix · pp. 14–15

A CALCULATION OF ABILITY RATINGS

The appendix details how ability-rating values are calculated, including game-state, branching-factor, and game-length parameters.

A CALCULATION OF ABILITY RATINGS

A.1 WEIGHTING COEFFICIENTS

The section identifies the weighting coefficients used in the ability-rating formulas in Section 3.2.

A.1 WEIGHTING COEFFICIENTS

  • Ability-rating formulas in Section 3.2 use weighting coefficients.The section introduces the coefficients used in those formulas.Source: §A.1 · p. 15

A.2 GAME RATING CALCULATIONS

The game-rating calculations characterize games through their state spaces, boards, pieces, and other game properties, then normalize ability scores for comparison. The resulting normalized scores are mapped to a five-star system.

A.2 GAME RATING CALCULATIONS

  • Tic-Tac-Toe’s rating setup treats each of its nine cells as having three possible states, yielding a stated total state space of 19,683.The passage gives the state-space expression as 3^9, or 19,683.Source: §A.2 · p. 15
  • The games’ normalized ability scores are presented across four dimensions in Table 6.The table caption identifies the dimensions as perception, reasoning, decision-making, and adversary.Source: Table 6 · p. 18
  • Rounded normalized scores are mapped to a five-star system to compare games’ cognitive and reasoning demands.The mapping provides a comparison across games and abilities.Source: §A.2 · p. 18

A.3 HUMAN STUDY OF DIFFICULTY RATINGS

The authors compare calculated game-difficulty rankings with rankings from volunteers. The human rankings generally follow a similar trend, though the ratings do not align perfectly.

A.3 HUMAN STUDY OF DIFFICULTY RATINGS

  • The calculated difficulty chain runs from Tic Tac Toe through Reversi, Sudoku, Minesweeper, and Gomoku to Chess.To calculate overall difficulty, the authors summed normalized scores and assigned zero placeholders to N/A ability scores for Minesweeper and Sudoku in the Adversary dimension.Source: §A.3 · pp. 18–19
  • Ten volunteers ranked the six games by perceived overall difficulty, and their average rankings showed high consistency with the calculated difficulty chain.The average rankings for Sudoku, Minesweeper, and Gomoku were close; the authors note that the games emphasize different cognitive abilities.Source: §A.3 · pp. 18–19, Figure 5 · p. 19
  • In a separate study, volunteers’ relative-difficulty trends across the four capabilities were similar to the overall evaluation, although they did not align perfectly with quantitative ability ratings.Volunteers sampled tasks and played against the simulator’s search-based opponent before ranking capabilities.Source: §A.3 · p. 19

B DETAILED GAME SETTINGS

This section says it provides further details on the four LVLM-Playground tasks introduced in the main text.

B DETAILED GAME SETTINGS

  • The section introduces further details on the four tasks described in the main text.The passage does not specify those details.Source: B DETAILED GAME SETTINGS · p. 20

B.1 PERCEIVING

The Perceiving task tests whether models can read game-board visuals and encode them in standardized matrices, using game states that may not be legal in play. Responses are scored against ground-truth matrices, with unparseable or incorrectly structured outputs receiving zero accuracy.

B.1 PERCEIVING

  • Game states may be randomly generated and need not obey game rules, isolating visual interpretation from knowledge of legal gameplay.The simulator can produce states that never occur in real games, such as a chessboard with multiple queens of the same color.Source: §B.1 · p. 20, Figure 6 · p. 20
  • Models convert board images into game-specific matrices, with each game's prompt defining how pieces, cells, or symbols are encoded.The prompts specify formats for Chess, Gomoku, Minesweeper, Reversi, Sudoku, and Tic Tac Toe.Source: §B.1 · pp. 20, 21, 22, 24
  • Accuracy compares the predicted and ground-truth matrices; outputs that cannot be parsed or do not match the required structure receive zero accuracy.All models receive the same prompt for each game to support controlled comparisons.Source: §B.1 · pp. 21, 24

B.2 QUESTION ANSWERING

The Question Answering task adapts VQA to game boards, pairing generated states with game-specific questions and ground-truth answers. Its questions test detailed perception and reasoning, while multiple-choice responses standardize evaluation across tasks.

B.2 QUESTION ANSWERING

  • Game-specific screenshots and question pools are generated with APIs that retrieve ground-truth answers, avoiding manual annotation of question-answer pairs.The setup also permits fine-grained manipulation of inputs for evaluating visual perception and reasoning.Source: §B.2 · p. 24
  • Multiple-choice options replace free-form answers to reduce response-format variability and focus evaluation on perception and reasoning.The format supports consistent evaluation and direct comparison across tasks.Source: §B.2 · pp. 24–25
  • Only responses with the correct answer in the specified format contribute to accuracy, so correct content in an unexpected format may still be marked incorrect.The authors treat strict instruction-following as part of the evaluated ability.Source: §B.2 · p. 26
  • Questions span visual recognition and reasoning, including counting, arithmetic, OCR-like symbol identification, and geometric distinctions.Examples include summing Sudoku rows or columns and identifying edge pieces or shapes.Source: §B.2 · p. 30

B.3 RULE FOLLOWING

This section describes how the framework evaluates rule following and end-to-end play, including selecting valid moves from rule-consistent states and making screenshot-based decisions in a simulator. It also covers end-to-end scoring mechanisms and the search-based AI opponents used in the framework.

B.3 RULE FOLLOWING

  • Rule Following supplies rule-consistent game states and structured rule prompts, then tests whether models can select valid moves across six games.The games are chess, Gomoku, Minesweeper, Reversi, Sudoku, and Tic-Tac-Toe.Source: §B.3 · pp. 30–31

B.4 END-TO-END PLAYING

  • End-to-End Playing prompts models to observe board screenshots, briefly explain a strategy, and output a move for simulator execution, adapting to opponents’ actions when present.The task evaluates integrated perception, reasoning, and decision-making over complete games.Source: §B.4 · pp. 33, 36
  • The games’ scores reward moves or filled cells, correctness or pieces, and outcomes, but Sudoku’s per-cell base reward applies only to non-initial filled cells.Tic-Tac-Toe gives 10 points per player O move and 50/20/0 for win/tie/loss; Sudoku gives 2 per non-initial filled cell, 10 per correctly filled non-initial cell, and 1000 for winning; Reversi gives 10 per move, 20 per Black piece, and 1000/500/0 for win/tie/loss.Source: §B.4 · p. 36
  • Minesweeper scoring awards 10 points per move, 2 per revealed non-mine cell, and a 1000-point winning bonus.The task goal is to reveal all non-mine cells without triggering a mine.Source: §B.4 · pp. 35, 37
  • E2E scores combine move counts with revealed Minesweeper cells, captured chess pieces, and game outcomes, balancing efficiency, strategic decisions, and successful results.Gomoku and chess award outcome bonuses for wins and ties; the chess score also includes the value of captured Black pieces.Source: §B.4 · p. 37

C SEARCH-BASED AI OPPONENTS

  • Search-based opponents use Minimax for Tic-Tac-Toe, Alpha-Beta pruning for Reversi, depth-limited pattern search for Gomoku, and Stockfish for Chess.Sudoku and Minesweeper have no search-based AI opponent in this framework; Stockfish uses Minimax with Alpha-Beta pruning.Source: C SEARCH-BASED AI OPPONENTS · pp. 37–38
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