Papers for

ai evaluation developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Video models struggle to explain magic tricks with misdirection

The Prestige: Benchmarking Cognitive Visual Reasoning using Magic Tricks

Abstract: We introduce The Prestige, a video understanding benchmark that probes cognitive visual reasoning through professional magic performances, where deliberate misdirection challenges models to track events and explain unexpected outcomes. The benchmark comprises open-ended questions ranging from temporal and causal understanding to robustness against adversarial questions. Evaluation of leading open-source and proprietary VLMs reveals a substantial gap between even the strongest model and human performance. We also find systematic discrepancies between human and automated grading and identify recurring model failure modes. Ablations further show that models can select correct multiple-choice answers even when they fail to generate correct explanations independently, motivating further investigations into question formats. Together, these findings highlight limitations in both VLM reasoning and its evaluation, underscoring the need for more reliable video understanding models and more rigorous benchmarks.

Wed 28 OctComputer Vision and Pattern RecognitionArtificial Intelligence
The gist
Tracking what happens in videos can be hard when things are meant to be tricky, like magic tricks that try to confuse. The Prestige benchmark tests video understanding models by showing them magic performances and asking questions about what really happened and why. The researchers found that even the best models have a long way to go before they understand these tricky videos as well as humans do. They also noticed that models sometimes pick the right answers without explaining them properly, showing that how we ask questions matters for evaluating video understanding.
Open → 2610.38103v1