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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.13011 |
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| _version_ | 1866909791293538304 |
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| author | Tian, Yuyang Mao, Shunqiang Gao, Wenchang Qiu, Lanlan He, Tianxing |
| author_facet | Tian, Yuyang Mao, Shunqiang Gao, Wenchang Qiu, Lanlan He, Tianxing |
| contents | Large Language Models (LLMs) have revolutionized the simulation of agent societies, enabling autonomous planning, memory formation, and social interactions. However, existing frameworks often overlook systematic evaluations for event organization and lack visualized integration with physically grounded environments, limiting agents' ability to navigate spaces and interact with items realistically. We develop MiniAgentPro, a visualization platform featuring an intuitive map editor for customizing environments and a simulation player with smooth animations. Based on this tool, we introduce a comprehensive test set comprising eight diverse event scenarios with basic and hard variants to assess agents' ability. Evaluations using GPT-4o demonstrate strong performance in basic settings but highlight coordination challenges in hard variants. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13011 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Visualized Framework for Event Cooperation with Generative Agents Tian, Yuyang Mao, Shunqiang Gao, Wenchang Qiu, Lanlan He, Tianxing Artificial Intelligence Large Language Models (LLMs) have revolutionized the simulation of agent societies, enabling autonomous planning, memory formation, and social interactions. However, existing frameworks often overlook systematic evaluations for event organization and lack visualized integration with physically grounded environments, limiting agents' ability to navigate spaces and interact with items realistically. We develop MiniAgentPro, a visualization platform featuring an intuitive map editor for customizing environments and a simulation player with smooth animations. Based on this tool, we introduce a comprehensive test set comprising eight diverse event scenarios with basic and hard variants to assess agents' ability. Evaluations using GPT-4o demonstrate strong performance in basic settings but highlight coordination challenges in hard variants. |
| title | A Visualized Framework for Event Cooperation with Generative Agents |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.13011 |