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    <abstract>Document-grounded quantitative reasoning requires a system to first understand a visually presented document and then compute an exact answer from the grounded content. We describe LILIPUT, a zero-shot submission to the DocSem 2026 shared task, in which Gemini 3.8 Flash performs visual evidence localization and converts the selected content into a faithful, self-contained math problem, while DeepSeek V4 performs the downstream numerical reasoning. On 1,730 held-out test instances, our accepted submission obtains 49.54% joint exact accuracy, 55.09% answer accuracy, and 66.76% macro evidence F1. In contrast, experiments on 908 labeled training instances show much higher answer accuracy when the relevant evidence is successfully extracted: Gemini 3.7 Flash achieves 95.59%, while text-based solvers achieve 92.51%–97.03%. Although these results are not directly comparable, the large gap suggests that calculation is not the main challenge. Instead, the main difficulty is correctly understanding and extracting information from the document. We found that clean and clear documents were easier to process, while blurry, dense, and visually complex documents made extraction much harder, even after image enhancement. Since DeepSeek V4 uses the extracted content for calculation, errors during visual extraction can also lead to incorrect answers. These findings highlight visual document understanding as a key challenge for document-grounded quantitative reasoning.</abstract>
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%0 Conference Proceedings
%T When the Evidence Is Right, the Math Is Easy: Understanding the Challenges of Document-Grounded Reasoning
%A Mikat, Aftab Hossain
%A Fahim, Tamjid Hasan
%Y T.Y.S.S., Santosh
%Y Agarwal, Amit
%Y Gupta, Vivek
%Y Kementchedjhieva, Yova
%Y Garimella, Aparna
%Y Hou, Yufang
%S The Proceedings of Workshop on Document Intelligence and Understanding
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-477-4
%F mikat-fahim-2026-evidence
%X Document-grounded quantitative reasoning requires a system to first understand a visually presented document and then compute an exact answer from the grounded content. We describe LILIPUT, a zero-shot submission to the DocSem 2026 shared task, in which Gemini 3.8 Flash performs visual evidence localization and converts the selected content into a faithful, self-contained math problem, while DeepSeek V4 performs the downstream numerical reasoning. On 1,730 held-out test instances, our accepted submission obtains 49.54% joint exact accuracy, 55.09% answer accuracy, and 66.76% macro evidence F1. In contrast, experiments on 908 labeled training instances show much higher answer accuracy when the relevant evidence is successfully extracted: Gemini 3.7 Flash achieves 95.59%, while text-based solvers achieve 92.51%–97.03%. Although these results are not directly comparable, the large gap suggests that calculation is not the main challenge. Instead, the main difficulty is correctly understanding and extracting information from the document. We found that clean and clear documents were easier to process, while blurry, dense, and visually complex documents made extraction much harder, even after image enhancement. Since DeepSeek V4 uses the extracted content for calculation, errors during visual extraction can also lead to incorrect answers. These findings highlight visual document understanding as a key challenge for document-grounded quantitative reasoning.
%U https://aclanthology.org/2026.docinsights-1.30/
%P 440-447

Markdown (Informal)

[When the Evidence Is Right, the Math Is Easy: Understanding the Challenges of Document-Grounded Reasoning](https://aclanthology.org/2026.docinsights-1.30/) (Mikat & Fahim, DocInsights 2026)

• When the Evidence Is Right, the Math Is Easy: Understanding the Challenges of Document-Grounded Reasoning (Mikat & Fahim, DocInsights 2026)

ACL

• Aftab Hossain Mikat and Tamjid Hasan Fahim. 2026. When the Evidence Is Right, the Math Is Easy: Understanding the Challenges of Document-Grounded Reasoning. In The Proceedings of Workshop on Document Intelligence and Understanding, pages 440–447, Budapest, Hungary. Association for Computational Linguistics.