@inproceedings{bedekar-2026-cost,
title = "A Cost-Efficient Vision-Text Cascade for Document {QA}: What Resolution Buys and What It Doesn{'}t",
author = "Bedekar, Urjita",
editor = "T.Y.S.S., Santosh and
Agarwal, Amit and
Gupta, Vivek and
Kementchedjhieva, Yova and
Garimella, Aparna and
Hou, Yufang",
booktitle = "The Proceedings of Workshop on Document Intelligence and Understanding",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.docinsights-1.34/",
pages = "477--482",
ISBN = "979-8-89176-477-4",
abstract = "This paper presents a two-stage vision-text cascade, submitted to the DocSem shared task at DocInsights Workshop, EMNLP 2026. A cheaper vision model (Claude Haiku 4.5) locates and transcribes the block that answers the question, and a stronger text-only model (Claude Sonnet 4.5) turns it into an arithmetic expression, which Python evaluates. Keeping the expensive model off the page images reduces cost by 14{\%} against using one model for both, and the system reaches 88.02{\%} answer accuracy on validation, up from 82.95{\%}. On the test split the same system scores 7.28{\%}, and this paper diagnoses the difference. The main constraint is block selection: test documents contain decoy blocks that repeat the question{'}s words while asking something else. Prose inside the blocks is transcribed almost perfectly; identifiers, which carry no redundancy, run at 75{--}81{\%}. I also find that the two splits are stored at different resolutions, 220 dpi against 71{--}79 dpi, and that enlarging a low-resolution page before sending it improves identifier transcription by 58 points. The gain stops at the provider{'}s 1568 px cap, above which rendering higher delivers an identical image and accuracy does not meaningfully improve."
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<abstract>This paper presents a two-stage vision-text cascade, submitted to the DocSem shared task at DocInsights Workshop, EMNLP 2026. A cheaper vision model (Claude Haiku 4.5) locates and transcribes the block that answers the question, and a stronger text-only model (Claude Sonnet 4.5) turns it into an arithmetic expression, which Python evaluates. Keeping the expensive model off the page images reduces cost by 14% against using one model for both, and the system reaches 88.02% answer accuracy on validation, up from 82.95%. On the test split the same system scores 7.28%, and this paper diagnoses the difference. The main constraint is block selection: test documents contain decoy blocks that repeat the question’s words while asking something else. Prose inside the blocks is transcribed almost perfectly; identifiers, which carry no redundancy, run at 75–81%. I also find that the two splits are stored at different resolutions, 220 dpi against 71–79 dpi, and that enlarging a low-resolution page before sending it improves identifier transcription by 58 points. The gain stops at the provider’s 1568 px cap, above which rendering higher delivers an identical image and accuracy does not meaningfully improve.</abstract>
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%0 Conference Proceedings
%T A Cost-Efficient Vision-Text Cascade for Document QA: What Resolution Buys and What It Doesn’t
%A Bedekar, Urjita
%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 bedekar-2026-cost
%X This paper presents a two-stage vision-text cascade, submitted to the DocSem shared task at DocInsights Workshop, EMNLP 2026. A cheaper vision model (Claude Haiku 4.5) locates and transcribes the block that answers the question, and a stronger text-only model (Claude Sonnet 4.5) turns it into an arithmetic expression, which Python evaluates. Keeping the expensive model off the page images reduces cost by 14% against using one model for both, and the system reaches 88.02% answer accuracy on validation, up from 82.95%. On the test split the same system scores 7.28%, and this paper diagnoses the difference. The main constraint is block selection: test documents contain decoy blocks that repeat the question’s words while asking something else. Prose inside the blocks is transcribed almost perfectly; identifiers, which carry no redundancy, run at 75–81%. I also find that the two splits are stored at different resolutions, 220 dpi against 71–79 dpi, and that enlarging a low-resolution page before sending it improves identifier transcription by 58 points. The gain stops at the provider’s 1568 px cap, above which rendering higher delivers an identical image and accuracy does not meaningfully improve.
%U https://aclanthology.org/2026.docinsights-1.34/
%P 477-482
Markdown (Informal)
[A Cost-Efficient Vision-Text Cascade for Document QA: What Resolution Buys and What It Doesn’t](https://aclanthology.org/2026.docinsights-1.34/) (Bedekar, DocInsights 2026)
ACL