EpiAgent: An Agent-Centric System for Ancient Inscription Restoration

2026-04-10

AI Summary

This study addresses the severe degradation of ancient inscriptions caused by prolonged weathering and human damage, a challenge that existing AI methods struggle to handle due to limited generalization. Inspired by epigraphers’ collaborative workflows, this work proposes an agent-based hierarchical planning framework that models restoration as an iterative “observe–hypothesize–execute–reassess” cycle. A large language model–driven central planner dynamically coordinates multimodal analysis, historical knowledge bases, specialized restoration tools, and iterative self-refinement modules. This architecture transcends conventional single-pipeline approaches by enabling dynamic, multi-capability collaboration for the first time. Experiments demonstrate that the proposed method significantly outperforms current techniques on real degraded inscriptions, achieving marked improvements in both restoration quality and generalization—representing a critical step toward expert-level intelligent cultural heritage restoration.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

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.

Problem

• ancient inscription restoration
• digital heritage preservation
• visual-textual integrity
• real-world degradation
• cultural memory

Innovation

• agent-centric system
• hierarchical planning
• inscription restoration
• multimodal analysis
• LLM-based planner