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Preprint Sep 2026

Not Another Text Benchmark: Putting the"Visual"Back in Visual Question Answering for Large Video Models

Three new vision-centric evaluation benchmarks in temporal frame retrieval, video future prediction, and causal memory distortion are introduced, all designed around evaluating visual understanding capabilities in large video models, which complements the existing approaches to evaluate video understanding in frontier...

Rwiddhi Chakraborty, Y. Wang, Cheng Zhang et al. · 0 citations
Preprint Oct 2026

Video-Index: A Curated Meta-Benchmark for Video Understanding

A video benchmark should reward the capability it claims to measure, yet models can exploit answer options, question text, or partial visual evidence. We introduce the attack pyramid, five levels of shortcut attacks with increasing access to each item, and audit 115 video benchmarks with it. On 35 benchmarks, attackers...

En-Xin Song, Yi-Nuo Xu, Shu-Sheng Yang et al. · 0 citations
2026

VideoEvent: Leveraging Relevance and LLMs for Video Question Answering

VideoEvent is a lightweight and efficient training-free framework that segments an input video into question-relevant temporal events and selectively supplements them with low-level visual cues such as background and object layout, making it highly suitable for practical deployment.

Chen-Chen Lin, Ming-Han Lee, Kun-Ru Wu et al. · 0 citations
Preprint Aug 2026

Video-IFBench: Evaluating Instruction Following of Multimodal LLMs in Video Understanding Scenarios

This work conducts a large-scale evaluation of more than 20 recent MLLMs and shows that video instruction following remains challenging for current models, especially for instructions with many constraints, semantic constraints, or complex conditional structures that require selecting the correct branch or path based o...

Hongbo Liu, Peixian Chen, Siyuan Liu et al. · 0 citations
#computer vision Preprint Aug 2026

TRACE: Transparent Retrieval for Abstract Concept Evaluation

This work compares six off-the-shelf VLMs against a transparent baseline that uses classical SIFT keypoint matching and a signal-quality index over retrieved images, and shows that a learned visual representation is not the bottleneck for this task: given retrieved images, a shape-appropriate classical similarity suffi...

Joe M. Bingham · 0 citations

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