# EpiAgent: An Agent-Centric System for Ancient Inscription Restoration

arXiv: https://arxiv.org/abs/2604.09367 · HTML: https://mlsanity.com/paper/2604.09367/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 27 of its 48 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 27 of 27)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [MUSIQ: Multi-scale Image Quality Transformer](https://mlsanity.com/paper/2108.05997/) (2021)
- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2204.08958/) (2022)
- [Deep Long-Tailed Learning: A Survey](https://mlsanity.com/paper/2110.04596/) (2021)
- [NIMA: Neural Image Assessment](https://mlsanity.com/paper/1709.05424/) (2017)
- [Seed1.5-VL Technical Report](https://mlsanity.com/paper/2505.07062/) (2025)
- [MambaIR: A Simple Baseline for Image Restoration with State-Space Model](https://mlsanity.com/paper/2402.15648/) (2024)
- [ICDAR 2019 Robust Reading Challenge on Reading Chinese Text on Signboard](https://mlsanity.com/paper/1912.09641/) (2019)
- [Complexity Experts are Task-Discriminative Learners for Any Image Restoration](https://mlsanity.com/paper/2411.18466/) (2024)
- [A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends](https://mlsanity.com/paper/2410.15067/) (2024)
- [RestoreAgent: Autonomous Image Restoration Agent via Multimodal Large Language Models](https://mlsanity.com/paper/2407.18035/) (2024)
- [An Intelligent Agentic System for Complex Image Restoration Problems](https://mlsanity.com/paper/2410.17809/) (2024)
- [DocDiff: Document Enhancement via Residual Diffusion Models](https://mlsanity.com/paper/2305.03892/) (2023)
- [DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks](https://mlsanity.com/paper/2405.04408/) (2024)
- [Restoring ancient text using deep learning: a case study on Greek epigraphy](https://mlsanity.com/paper/1910.06262/) (2019)
- [Multi-Agent Image Restoration](https://mlsanity.com/paper/2503.09403/) (2025)
- [Visual Text Processing: A Comprehensive Review and Unified Evaluation](https://mlsanity.com/paper/2504.21682/) (2025)
- [Hybrid Agents for Image Restoration](https://mlsanity.com/paper/2503.10120/) (2025)
- [CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image Denoising](https://mlsanity.com/paper/2207.07798/) (2022)
- [Predicting the Original Appearance of Damaged Historical Documents](https://mlsanity.com/paper/2412.11634/) (2024)
- [Text Image Inpainting via Global Structure-Guided Diffusion Models](https://mlsanity.com/paper/2401.14832/) (2024)
- [Reviving Cultural Heritage: A Novel Approach for Comprehensive Historical Document Restoration](https://mlsanity.com/paper/2507.05108/) (2025)
- [PhyDAE: Physics-Guided Degradation-Adaptive Experts for All-in-One Remote Sensing Image Restoration](https://mlsanity.com/paper/2510.08653/) (2025)

## Built on by (most cited first, 2 of 2)

- [Beyond Pixel Reconstruction: Retrieval-Guided Glyph-Aware Restoration for Low-Resource Manchu Historical Documents](https://mlsanity.com/paper/2610.00315/) (2026)
- [Look Before You Restore: Learning to Predict Before Acting for Agentic Image Restoration](https://mlsanity.com/paper/2610.20384/) (2026)

## Abstract (arXiv)

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.