# Enhancing Multimodal Large Language Models for Ancient Chinese Character Evolution Analysis via Glyph-Driven Fine-Tuning

arXiv: https://arxiv.org/abs/2604.11299 · HTML: https://mlsanity.com/paper/2604.11299/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 13 Apr 2026.

## Placed in

- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)

## Builds on (most cited first, 13 of 13)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://mlsanity.com/paper/2508.18265/) (2025)
- [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://mlsanity.com/paper/2406.12793/) (2024)
- [OBI-Bench: Can LMMs Aid in Study of Ancient Script on Oracle Bones?](https://mlsanity.com/paper/2412.01175/) (2024)
- [An open dataset for oracle bone script recognition and decipherment](https://mlsanity.com/paper/2401.15365/) (2024)
- [Deciphering Oracle Bone Language with Diffusion Models](https://mlsanity.com/paper/2406.00684/) (2024)
- [CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image Denoising](https://mlsanity.com/paper/2207.07798/) (2022)
- [WenyanGPT: A Large Language Model for Classical Chinese Tasks](https://mlsanity.com/paper/2504.20609/) (2025)
- [Oracle Character Recognition using Unsupervised Discriminative Consistency Network](https://mlsanity.com/paper/2312.06075/) (2023)
- [Mitigating Long-tail Distribution in Oracle Bone Inscriptions: Dataset, Model, and Benchmark](https://mlsanity.com/paper/2504.09555/) (2025)
- [AncientBench: Towards Comprehensive Evaluation on Excavated and Transmitted Chinese Corpora](https://mlsanity.com/paper/2512.17756/) (2025)

## Built on by (most cited first, 1 of 1)

- [Chronicles-OCR: A Cross-Temporal Perception Benchmark for the Evolutionary Trajectory of Chinese Characters](https://mlsanity.com/paper/2605.11960/) (2026)

## Abstract (arXiv)

In recent years, rapid advances in Multimodal Large Language Models (MLLMs) have increasingly stimulated research on ancient Chinese scripts. As the evolution of written characters constitutes a fundamental pathway for understanding cultural transformation and historical continuity, how MLLMs can be systematically leveraged to support and advance text evolution analysis remains an open and largely underexplored problem. To bridge this gap, we construct a comprehensive benchmark comprising 11 tasks and over 130,000 instances, specifically designed to evaluate the capability of MLLMs in analyzing the evolution of ancient Chinese scripts. We conduct extensive evaluations across multiple widely used MLLMs and observe that, while existing models demonstrate a limited ability in glyph-level comparison, their performance on core tasks-such as character recognition and evolutionary reasoning-remains substantially constrained. Motivated by these findings, we propose a glyph-driven fine-tuning framework (GEVO) that explicitly encourages models to capture evolutionary consistency in glyph transformations and enhances their understanding of text evolution. Experimental results show that even models at the 2B scale achieve consistent and comprehensive performance improvements across all evaluated tasks. To facilitate future research, we publicly release both the benchmark and the trained modelsfootnotehttps://github.com/songruiecho/GEVO.