Pith Number

pith:BA3E4RBK

pith:2025:BA3E4RBKSWX4UICQKOEYAQKHQI

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VideoGameBench: Can Vision-Language Models complete popular video games?

Alex L. Zhang, Karthik R. Narasimhan, Ofir Press, Thomas L. Griffiths

Frontier vision-language models complete only 0.48 percent of VideoGameBench and 1.6 percent of its paused Lite version.

arxiv:2505.18134 v3 · 2025-05-23 · cs.AI · cs.CL · cs.CV

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Claims

C1strongest claim

Frontier vision-language models struggle to progress beyond the beginning of each game, completing only 0.48% of VideoGameBench and 1.6% of VideoGameBench Lite.

C2weakest assumption

That raw visual input plus a high-level objective description is sufficient to isolate and measure the targeted human skills of perception, spatial navigation, and memory management, rather than being dominated by game-specific mechanics or control mapping difficulties. (Abstract, benchmark description paragraph)

C3one line summary

Frontier vision-language models complete only 0.48% of VideoGameBench and 1.6% of its paused Lite version, a new real-time benchmark of 10 1990s games with raw visuals and minimal scaffolding.

References

60 extracted · 60 resolved · 0 Pith anchors

[1] Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, and Oriol Vinyals 2016 · doi:10.1126/science.abq1158

[2] URLhttps://openreview.net/forum?id=pmcFzuUxsP. Together AI. Together ai partners with meta to offer llama 4: Sota multimodal moe models. https: //www.together.ai/blog/llama-4, April 2025. Chen Feng Ts 2025

[3] Think: Analyze the current state and decide what to do next

[4] Action: Choose one of the following actions: - click [options as action_input]: Click the mouse at the current mouse position. Options include: * right: Right click instead of left click (default is l

[5] Observation: You will receive the result of your action You will interact with the game via the keyboard and mouse actions. To help you with mouse actions, we provide a thin red grid overlay that inte

Formal links

2 machine-checked theorem links

Cited by

6 papers in Pith

Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse

Do Vision-Language-Models show human-like logical problem-solving capability in point and click puzzle games?

Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse

Odysseus: Scaling VLMs to 100+ Turn Decision-Making in Games via Reinforcement Learning

Receipt and verification

First computed	2026-05-20T00:00:20.195682Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

08364e442a95afca20505389804147821f9e55809e1f8ae7cb9ab2eb621da407

Aliases

arxiv: 2505.18134 · arxiv_version: 2505.18134v3 · doi: 10.48550/arxiv.2505.18134 · pith_short_12: BA3E4RBKSWX4 · pith_short_16: BA3E4RBKSWX4UICQ · pith_short_8: BA3E4RBK

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/BA3E4RBKSWX4UICQKOEYAQKHQI \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 08364e442a95afca20505389804147821f9e55809e1f8ae7cb9ab2eb621da407

Canonical record JSON

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    "abstract_canon_sha256": "07db93e47d8dc643bdfff7199eed21509f5dd47466a8cfd59c1e5e9727ef0454",
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2025-05-23T17:43:27Z",
    "title_canon_sha256": "06b9c12099682154e7d69b86fe78f79590832809e7b3fd3d05eff7a724eec3c8"
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