[Submitted on 15 Jul 2026]
Title:TestHallVQA: Exploring LVLMs' Document-Level Reasoning under Redundant Contexts from Scientific Exams
View a PDF of the paper titled TestHallVQA: Exploring LVLMs' Document-Level Reasoning under Redundant Contexts from Scientific Exams, by Yongqi Yu and Yu Zhang
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Abstract:Large Vision--Language Models (LVLMs) are increasingly expected to perform visual question answering (VQA) over planar media. However, existing planar VQA benchmarks typically emphasize isolated challenges: some emphasize long-document understanding with limited reasoning depth, while others require complex visual reasoning but remain restricted to single-page, noise-free settings. Moreover, through theoretical analysis, we identify the impact of irrelevant visual tokens, which leads to measurable performance degradation but has received little attention with respect to systematic quantification. To address these limitations, we introduce TestHallVQA, a multi-image VQA benchmark that simultaneously embodies document-level scale and the difficulty of human examinations, while providing comprehensive task coverage. Leveraging TestHallVQA's ability to controllably inject multi-level contextual redundancy, we further propose a novel metric, F1-R\textsuperscript{2}, which jointly quantifies LVLMs' computational reasoning capability and their evidence retrieval robustness against document-level redundancy. Extensive experiments and analyses on mainstream LVLMs reveal their latent deficiencies across multiple dimensions, offering concrete insights and directions for future research. The associated datasets, code, and complete theoretical derivations are available at this https URL.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2609.13158 [cs.CL]
(or arXiv:2609.13158v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.13158
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yongqi Yu [view email] [v1] Wed, 15 Jul 2026 03:51:15 UTC (14,379 KB)
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