OracleAgent: A Multimodal Reasoning Agent for Oracle Bone Script Research
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| Autores principales: | , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908619930337280 |
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| author | Li, Caoshuo Ding, Zengmao Hu, Xiaobin Li, Bang Luo, Donghao Peng, Xu Jin, Taisong Liu, Yongge Han, Shengwei Yang, Jing He, Xiaoping Gao, Feng Wu, AndyPian SevenShu Wang, Chaoyang Wang, Chengjie |
| author_facet | Li, Caoshuo Ding, Zengmao Hu, Xiaobin Li, Bang Luo, Donghao Peng, Xu Jin, Taisong Liu, Yongge Han, Shengwei Yang, Jing He, Xiaoping Gao, Feng Wu, AndyPian SevenShu Wang, Chaoyang Wang, Chengjie |
| contents | As one of the earliest writing systems, Oracle Bone Script (OBS) preserves the cultural and intellectual heritage of ancient civilizations. However, current OBS research faces two major challenges: (1) the interpretation of OBS involves a complex workflow comprising multiple serial and parallel sub-tasks, and (2) the efficiency of OBS information organization and retrieval remains a critical bottleneck, as scholars often spend substantial effort searching for, compiling, and managing relevant resources. To address these challenges, we present OracleAgent, the first agent system designed for the structured management and retrieval of OBS-related information. OracleAgent seamlessly integrates multiple OBS analysis tools, empowered by large language models (LLMs), and can flexibly orchestrate these components. Additionally, we construct a comprehensive domain-specific multimodal knowledge base for OBS, which is built through a rigorous multi-year process of data collection, cleaning, and expert annotation. The knowledge base comprises over 1.4M single-character rubbing images and 80K interpretation texts. OracleAgent leverages this resource through its multimodal tools to assist experts in retrieval tasks of character, document, interpretation text, and rubbing image. Extensive experiments demonstrate that OracleAgent achieves superior performance across a range of multimodal reasoning and generation tasks, surpassing leading mainstream multimodal large language models (MLLMs) (e.g., GPT-4o). Furthermore, our case study illustrates that OracleAgent can effectively assist domain experts, significantly reducing the time cost of OBS research. These results highlight OracleAgent as a significant step toward the practical deployment of OBS-assisted research and automated interpretation systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26114 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OracleAgent: A Multimodal Reasoning Agent for Oracle Bone Script Research Li, Caoshuo Ding, Zengmao Hu, Xiaobin Li, Bang Luo, Donghao Peng, Xu Jin, Taisong Liu, Yongge Han, Shengwei Yang, Jing He, Xiaoping Gao, Feng Wu, AndyPian SevenShu Wang, Chaoyang Wang, Chengjie Computer Vision and Pattern Recognition As one of the earliest writing systems, Oracle Bone Script (OBS) preserves the cultural and intellectual heritage of ancient civilizations. However, current OBS research faces two major challenges: (1) the interpretation of OBS involves a complex workflow comprising multiple serial and parallel sub-tasks, and (2) the efficiency of OBS information organization and retrieval remains a critical bottleneck, as scholars often spend substantial effort searching for, compiling, and managing relevant resources. To address these challenges, we present OracleAgent, the first agent system designed for the structured management and retrieval of OBS-related information. OracleAgent seamlessly integrates multiple OBS analysis tools, empowered by large language models (LLMs), and can flexibly orchestrate these components. Additionally, we construct a comprehensive domain-specific multimodal knowledge base for OBS, which is built through a rigorous multi-year process of data collection, cleaning, and expert annotation. The knowledge base comprises over 1.4M single-character rubbing images and 80K interpretation texts. OracleAgent leverages this resource through its multimodal tools to assist experts in retrieval tasks of character, document, interpretation text, and rubbing image. Extensive experiments demonstrate that OracleAgent achieves superior performance across a range of multimodal reasoning and generation tasks, surpassing leading mainstream multimodal large language models (MLLMs) (e.g., GPT-4o). Furthermore, our case study illustrates that OracleAgent can effectively assist domain experts, significantly reducing the time cost of OBS research. These results highlight OracleAgent as a significant step toward the practical deployment of OBS-assisted research and automated interpretation systems. |
| title | OracleAgent: A Multimodal Reasoning Agent for Oracle Bone Script Research |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.26114 |