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    <abstract>Deciphering scanned Oracle Bone Script (OBS) images into corresponding modern Chinese characters is a crucial step in AI-assisted OBS decipherment. As a highly pictographic writing system, OBS often embeds semantic information in glyph forms, making its decipherment depend on more than visual similarity alone. However, how to effectively exploit such glyph-meaning cues and how to make the decipherment process interpretable remain challenging. To this end, we propose an LVLM-based framework that bridges glyphs and meanings through structured glyph analysis. Specifically, we introduce a progressive training strategy that guides the model from radical analysis to pictographic analysis and finally to mutual analysis, enabling it to capture semantic information embedded in OBS glyphs. The generated textual analyses serve as both interpretable reasoning traces and explicit evidence for retrieving corresponding modern Chinese characters from a dictionary. We also construct the Pictographic Decipherment OBS Dataset (PD-OBS), which provides detailed radical and pictographic analyses to support glyph-analysis learning and dictionary-based retrieval. Experiments on OBS benchmarks demonstrate the strong zero-shot capability of our method, especially its substantially improved Top-10 accuracy on unseen OBS, highlighting its ability to provide reliable and interpretable candidate references for AI-assisted archaeological and historical studies. The code and dataset will be released at https://github.com/PKXX1943/PD-OBS.</abstract>
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%0 Conference Proceedings
%T Interpretable Oracle Bone Script Decipherment via Progressive Glyph Analysis with LVLMs
%A Peng, Kaixin
%A Zhao, Mengyang
%A Yu, Haiyang
%A Fu, Teng
%A Li, Bin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F peng-etal-2026-interpretable
%X Deciphering scanned Oracle Bone Script (OBS) images into corresponding modern Chinese characters is a crucial step in AI-assisted OBS decipherment. As a highly pictographic writing system, OBS often embeds semantic information in glyph forms, making its decipherment depend on more than visual similarity alone. However, how to effectively exploit such glyph-meaning cues and how to make the decipherment process interpretable remain challenging. To this end, we propose an LVLM-based framework that bridges glyphs and meanings through structured glyph analysis. Specifically, we introduce a progressive training strategy that guides the model from radical analysis to pictographic analysis and finally to mutual analysis, enabling it to capture semantic information embedded in OBS glyphs. The generated textual analyses serve as both interpretable reasoning traces and explicit evidence for retrieving corresponding modern Chinese characters from a dictionary. We also construct the Pictographic Decipherment OBS Dataset (PD-OBS), which provides detailed radical and pictographic analyses to support glyph-analysis learning and dictionary-based retrieval. Experiments on OBS benchmarks demonstrate the strong zero-shot capability of our method, especially its substantially improved Top-10 accuracy on unseen OBS, highlighting its ability to provide reliable and interpretable candidate references for AI-assisted archaeological and historical studies. The code and dataset will be released at https://github.com/PKXX1943/PD-OBS.
%U https://aclanthology.org/2026.findings-emnlp.1535/
%P 32654-32674

Markdown (Informal)

[Interpretable Oracle Bone Script Decipherment via Progressive Glyph Analysis with LVLMs](https://aclanthology.org/2026.findings-emnlp.1535/) (Peng et al., Findings 2026)

• Interpretable Oracle Bone Script Decipherment via Progressive Glyph Analysis with LVLMs (Peng et al., Findings 2026)

ACL

• Kaixin Peng, Mengyang Zhao, Haiyang Yu, Teng Fu, and Bin Li. 2026. Interpretable Oracle Bone Script Decipherment via Progressive Glyph Analysis with LVLMs. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 32654–32674, Budapest, Hungary. Association for Computational Linguistics.