用智能代理系统修复古代铭文，像专家一样灵活应对复杂损毁。
EpiAgent: An Agent-Centric System for Ancient Inscription Restoration
- 以人类学者工作流程为灵感，用多智能体协同实现分步修复
- 在真实损毁铭文中表现优于现有方法，通用性更强
- 适合文化遗产保护与跨模态修复研究者使用
古代铭文作为文化记忆的载体，历经数百年环境与人为破坏，其视觉与文本完整性交织受损，是数字遗产保护中最具挑战性的任务之一。现有基于AI的方法多依赖固定流程，难以适应复杂多样的现实损毁情况。受人类铭文学者协作流程启发，本文提出EpiAgent——一种以智能体为中心的系统，将铭文修复建模为分层规划问题。遵循观察-构思-执行-评估范式，基于大语言模型的中央规划器协调多模态分析、历史经验、专用修复工具及迭代自我优化。该智能体中心化协作机制实现了超越传统单次流程的灵活自适应修复。在真实世界损毁铭文上，EpiAgent在修复质量与泛化能力上均优于现有方法。本工作标志着迈向专家级智能体驱动文化遗产修复的重要一步。代码已开源：https://github.com/blackprotoss/EpiAgent。
原文摘要 · Abstract (English)
Ancient inscriptions, as repositories of cultural memory, have suffered from centuries of environmental and human-induced degradation. Restoring their intertwined visual and textual integrity poses one of the most demanding challenges in digital heritage preservation. However, existing AI-based approaches often rely on rigid pipelines, struggling to generalize across such complex and heterogeneous real-world degradations. Inspired by the skill-coordinated workflow of human epigraphers, we propose EpiAgent, an agent-centric system that formulates inscription restoration as a hierarchical planning problem. Following an Observe-Conceive-Execute-Reevaluate paradigm, an LLM-based central planner orchestrates collaboration among multimodal analysis, historical experience, specialized restoration tools, and iterative self-refinement. This agent-centric coordination enables a flexible and adaptive restoration process beyond conventional single-pass methods. Across real-world degraded inscriptions, EpiAgent achieves superior restoration quality and stronger generalization compared to existing methods. Our work marks an important step toward expert-level agent-driven restoration of cultural heritage. The code is available at https://github.com/blackprotoss/EpiAgent.
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