Most video benchmarks are secretly solvable without the video
A 2026 diagnostic study called Video-Oasis audited existing video-understanding benchmarks and found that 55% of the test questions could be answered correctly without any visual input or temporal context [3]. That means a model could rely on language priors, object co-occurrence, or generic knowledge—not on actually tracking what happened over time. So when a model scores well on a benchmark, you can't tell if it's genuinely understanding video or just exploiting shortcuts.
This echoes an earlier 2022 finding: a simple model that only looked at single frames (no temporal reasoning) matched or beat state-of-the-art video-language models on standard video question-answering and retrieval tasks [4]. The authors concluded that event temporality—the order and timing of events—was often not necessary to achieve strong performance. Together, these studies show that the typical benchmark is not testing what it claims to test.
When shortcuts are removed, even top models stumble
The real test comes when you strip away those shortcuts. In the Video-Oasis audit, after filtering out the solvable-without-video samples, the remaining 'video-native' challenges exposed a huge capability gap: state-of-the-art models performed only marginally above random guessing [3]. That's a dramatic drop from their inflated benchmark scores, and it shows that edge cases—questions that genuinely require tracking motion, causality, or long-range context—are where current AI fails.
The same pattern appears in long-form video. A 2026 framework for very long egocentric video (EGAgent) achieved only 57.5% on the EgoLifeQA benchmark, which requires multi-hop reasoning over days of footage [1]. That's barely better than chance for a complex task, and it highlights that even with advanced agentic tools, models struggle to maintain temporal coherence and recall specific details over extended periods. The gap between best-case (74.1% on a shorter long-video benchmark) and typical-case (57.5% on truly longitudinal data) shows that edge cases—like recalling a specific conversation from three days ago—are where the limits bite.
Edge cases are the difference between a demo and a dependable assistant
For real-world applications like always-on smart glasses or personal AI assistants, edge cases aren't rare anomalies—they're the norm. A 2026 position paper argues that the next frontier is 'Infinite Video Understanding,' where models must continuously process and reason about video of arbitrary, potentially never-ending duration [2]. That requires handling the messy, unpredictable moments: a person walking into frame, a sudden sound, a long gap between related events. These are exactly the cases that current benchmarks often skip, because they're hard to generate and even harder to evaluate.
The research points to a clear conclusion: if we want AI that truly understands video, we need to design benchmarks and models that force temporal reasoning, not just pattern matching. The 2022 study suggested a practical fix—using a simple frame-only model to identify and filter out 'temporally easy' samples, so that benchmarks concentrate on the hard cases [4]. Similarly, the Video-Oasis authors call for a sustainable diagnostic suite to audit benchmarks and ensure they actually measure video understanding [3]. Until then, edge cases will remain the true test—and the field's biggest weakness.
About These Sources
This answer is built on 4 studies (1 peer-reviewed, 3 preprints) — published from 2022 to 2026, 3 from 2024 or later, collectively cited 112 times — selected as the most relevant from 4 studies that passed quality screening, drawn from 57 papers retrieved from a database of over 500 million.
Sources used in this answer
Agentic Very Long Video Understanding
The EGAgent framework, using entity scene graphs and a planning agent, achieved state-of-the-art 57.5% on EgoLifeQA and 74.1% on Video-MME (Long), showing that even advanced agentic methods struggle with very long, multi-hop video reasoning.
Infinite Video Understanding
A position paper argues that 'Infinite Video Understanding'—processing arbitrary-length video—is the next frontier, citing current computational and memory limits, and the difficulty of maintaining temporal coherence over long periods.
Video-Oasis: Rethinking Evaluation of Video Understanding
The Video-Oasis audit found that 55% of existing video benchmark samples are solvable without visual or temporal input, and after filtering these shortcuts, state-of-the-art models perform only marginally above random guessing on the remaining video-native challenges.
Revisiting the “Video” in Video-Language Understanding
The atemporal probe (ATP), a frame-only model, matched or exceeded state-of-the-art video-language models on standard video QA and retrieval tasks, demonstrating that event temporality is often unnecessary for strong performance on current benchmarks.