HERBench: A Benchmark for Multi-Evidence Integration in Video Question Answering
Each question forces a model to combine at least three cues from different parts of a video, and 13 Video-LLMs score only 31 to 42%.
Key result
HERBench forces video models to aggregate evidence: each of its 26,806 questions needs at least three separate cues from distinct parts of the video, and 13 state-of-the-art Video-LLMs reach only 31 to 42% (random is 20%).
Abstract
Video Large Language Models (Video-LLMs) are improving rapidly, yet current Video Question Answering (VideoQA) benchmarks often admit single-cue shortcuts, under-testing reasoning that must integrate evidence across time. We introduce HERBench, a benchmark designed to make multi-evidence integration unavoidable: each question requires at least three non-overlapping cues drawn from distinct video segments. HERBench contains 26,806 five-way multiple-choice questions across 12 compositional tasks. To make evidential demand measurable, we introduce the Minimum Required Frame-Set (MRFS), the smallest number of frames a model must fuse to answer correctly, and show that HERBench imposes higher evidential demand than prior benchmarks. Evaluating 13 state-of-the-art Video-LLMs yields only 31-42% accuracy, only modestly above the 20% random-guess baseline. We disentangle this failure into two critical bottlenecks: (1) a retrieval deficit, where frame selectors overlook key evidence, and (2) a fusion deficit, where models fail to integrate information even when all necessary evidence is provided. HERBench thus provides a principled benchmark for studying robust multi-evidence video understanding.
Abstract as published on CVF.
Cite
@InProceedings{Ben_Ami_2026_CVPR,
author = {Ben Ami, Dan and Serussi, Gabriele and Cohen, Kobi and Baskin, Chaim},
title = {HERBench: A Benchmark for Multi-Evidence Integration in Video Question Answering},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2026},
pages = {4505-4514}
}Topics
- video question answering
- VideoQA benchmark
- multi-evidence reasoning
- long video understanding
- Video-LLMs
- frame selection
Related research
Long-video understanding: Benchmarks that show where Video-LLMs fail, and methods that let them look at less or train less.
* Equal contribution.