Newest papers

DuoCook: An Overcooked-Inspired Benchmark for Visual Planning and Coordination

Ziyi Jiao, Heng Zhang, Drishti Regmi, Xiaokang Ye, Lianhui Qin, Peter Yichen Chen · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31511 · cited by 0 works (OpenAlex) · open-access PDF

Abstract

Coordinating two players in a cooking task requires a vision-language agent to track changing scene states, sequence recipe steps, and allocate shared resources under a time budget. We introduce an Overcooked-inspired benchmark for evaluating these capabilities in Unreal Engine environments constructed using coding agents. A single vision-language model controls both players from top-down visual observations, selecting high-level actions while the engine executes navigation. The benchmark contains 20 maps in five categories. These categories support evaluation of centralized coordination under equipment contention, changing terrain, and handoff dependencies. A prop legend and pot ingredient-count indicators provide explicit visual cues, while grid-based destinations support spatial grounding. The evaluation compares four VLMs on all 20 maps, measuring task score, order completion, delivery precision, action acceptance rate, and model API expenditure. A memory ablation on the same 20 maps examines the effect of a shared, self-authored notebook while retaining six-response action history in both conditions. The strongest evaluated model, \texttt{gpt-6-astra}, achieves 26.2\% mean order completion. Notebook memory has mixed score effects across models, with positive mean differences for Claude and Qwen and negative differences for GPT and Gemini.

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Topics: Benchmark (surveying), Computer science, Task (project management), Handover, Coding (social sciences), Artificial intelligence

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