aiqbench analysis
This report details the fourth Perception Test benchmark, introducing city-scale audio-visual tracks that reveal a significant gap between standalone multimodal models and expensive agentic pipelines in handling complex spatial reasoning. The findings highlight that while generalist models achieve satisfactory performance on standard tasks, solving large-scale visual questions currently relies heavily on orchestration-heavy architectures rather than pure model scaling.
Perception Test 2026: Challenge Summary and Extension to City-scale Audio-Visual Reasoning
arXiv:2610.12081v1 Announce Type: cross Abstract: Continuing the Perception Test challenge series, we organised the fourth edition as a workshop at the European Conference on Computer Vision (ECCV) 2026 in Malm\"o, Sweden. This edition focused on spatial intelligence and featured four different tracks: unified multiple-choice videoQA and grounded videoQA from the original Perception Test benchmark, alongside two new tracks based on city-scale walking-tour videos (KilometerAudio and KilometerVision). In this report, we describe the new benchmarks used for the city-scale tracks and summarise the winning solutions across all tracks, including a generalist model that competed across all tracks with satisfactory performance. The winning solutions in the newly added city-scale tracks demonstrated that complex spatial and multimodal reasoning can be solved by expensive agentic pipelines, but remains difficult for multimodal models used standalone.
Abstract:Continuing the Perception Test challenge series, we organised the fourth edition as a workshop at the European Conference on Computer Vision (ECCV) 2026 in Malmö, Sweden. This edition focused on spatial intelligence and featured four different tracks: unified multiple-choice videoQA and grounded videoQA from the original Perception Test benchmark, alongside two new tracks based on city-scale walking-tour videos (KilometerAudio and KilometerVision). In this report, we describe the new benchmarks used for the city-scale tracks and summarise the winning solutions across all tracks, including a generalist model that competed across all tracks with satisfactory performance. The winning solutions in the newly added city-scale tracks demonstrated that complex spatial and multimodal reasoning can be solved by expensive agentic pipelines, but remains difficult for multimodal models used standalone.
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
arXiv:2610.12081 [cs.CV]
(or arXiv:2610.12081v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.12081 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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