VistaWise: Building Cost-Effective Agent with Cross-Modal Knowledge Graph for Minecraft

• URL: http://arxiv.org/abs/2508.18722v2
• Date: Sat, 30 Aug 2025 11:01:08 GMT
• Title: VistaWise: Building Cost-Effective Agent with Cross-Modal Knowledge Graph for Minecraft
• Authors: Honghao Fu, Junlong Ren, Qi Chai, Deheng Ye, Yujun Cai, Hao Wang,
• Abstract summary: VistaWise is a cost-effective agent framework that integrates cross-modal domain knowledge.<n>It reduces the requirement for domain-specific training data from millions of samples to a few hundred.<n>It achieves state-of-the-art performance across various open-world tasks.
• Score: 30.110035501991344
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have shown significant promise in embodied decision-making tasks within virtual open-world environments. Nonetheless, their performance is hindered by the absence of domain-specific knowledge. Methods that finetune on large-scale domain-specific data entail prohibitive development costs. This paper introduces VistaWise, a cost-effective agent framework that integrates cross-modal domain knowledge and finetunes a dedicated object detection model for visual analysis. It reduces the requirement for domain-specific training data from millions of samples to a few hundred. VistaWise integrates visual information and textual dependencies into a cross-modal knowledge graph (KG), enabling a comprehensive and accurate understanding of multimodal environments. We also equip the agent with a retrieval-based pooling strategy to extract task-related information from the KG, and a desktop-level skill library to support direct operation of the Minecraft desktop client via mouse and keyboard inputs. Experimental results demonstrate that VistaWise achieves state-of-the-art performance across various open-world tasks, highlighting its effectiveness in reducing development costs while enhancing agent performance.

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