ICDAR2026 Competition on Multimodal Reasoning over Documents in Multiple Domains
cs.CL
Submitted: 2026-09-08
Updated: 2026-09-08
Journal ref: Document Analysis and Recognition - ICDAR 2026. Lecture Notes in Computer Science, vol 16975. Springer, Cham
DOI: 10.1007/978-3-032-36042-7_20
License: http://creativecommons.org/licenses/by-sa/4.0/
The gist: In this report we present results of the ICDAR2026 Competition on Multimodal Reasoning over Documents in Multiple Domains.
Terminology
Abstract
In this report we present results of the ICDAR2026 Competition on Multimodal Reasoning over Documents in Multiple Domains. This competition aimed to advance research in document understanding through the task of Visual Question Answering (VQA). Building upon previous DocVQA benchmarks, this competition introduces challenging reasoning questions over a diverse collection of documents spanning eight domains, including business reports, scientific papers, slides, posters, maps, comics, infographics, and engineering drawings. The competition concluded with 20 valid submissions from 8 teams spanning zero-shot VLMs, OCR and parser-augmented pipelines, agentic retrieval systems, multi-agent ensembles, and fine-tuned multimodal models. The results show that the strongest systems move beyond single-pass prompting and instead rely on structured evidence extraction, retrieval, verification, and orchestration across multiple components.
Sources
- Scene Text Visual Question Answering
- How to Correctly Report LLM-as-a-Judge Evaluations
- MapTrace: Scalable Data Generation for Route Tracing on Maps
- Qwen3-VL Technical Report
- Can Large Vision Language Models Read Maps Like a Human?
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