The Low Frequency Trap: Video Language Models Fail at Simple Event Bookkeeping
Score
78首次以可执行轨迹审计方式揭示事件表征类型对计数可靠性的决定性影响，信息增量高。
实验设计严密，2190个受控视频加真值轨迹，结果呈现清晰的阶段性失效规律。
提供可复现的诊断方法和对采样率、提示策略的定量结论，直接指导多模态模型评估与改进。
针对当前视频语言模型真实能力短板，论文发布及时且题目具有强时效性。
来自arXiv AI/CL/LG预印本，方法透明、可复现，且评测对象为当前主流模型。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper exposes a systematic flaw previously masked by aggregate metrics through rigorous controlled experiments and trace auditing, representing a significant methodological contribution to video understanding evaluation.
AI commentary
「What makes this paper stand out is that it decomposes counting errors down to the timestamp level instead of just looking at final answers. It matters especially for people building video understanding evaluations and training models, because the results show that increasing sampling rate may just be inflating scores. I recommend reading its trace-auditing design—it could inspire the next generation of multimodal evaluation practices.」
Quick overview
This study introduces a trace-grounded parametric profiling framework for event counting in three controlled video tasks: bouncing-ball wall contacts, visual blinks, and categorical state transitions. Across 2,190 videos, the researchers systematically vary event count N and frequency F while holding rendering fixed, using executable event traces for capability-surface estimation and timestamp-level evaluation. Results reveal a staged temporal failure: Gemini 3.6 Flash reliably counts persistent state transitions up to 12 events at 0.5 and 1.0 Hz, yet shows no reliable positive-count region for transient blinking events. In the high-count, high-frequency regime, only 0.2% of final counts are correct and the model recovers just 18.1% of true events. Increasing sampling rate boosts Bounce Ball accuracy from 19.6% to 29.3%, but the reported sequence agrees with ground truth only 3.7% of the time, indicating extra frames inflate final scores without producing faithful event recovery. The study shifts video evaluation from aggregate accuracy metrics to detailed diagnostics of temporal reasoning failures.
- Proposes trace-grounded parametric profiling using executable event traces for timestamp-level auditing, not just final-answer scoring.
- Reveals staged temporal failure: persistent state transitions count up to 12 events, while transient blinking events show no reliable positive-count region.
- In the high-count, high-frequency regime, final count accuracy is only 0.2% and event recovery is 18.1%; increasing sampling rate inflates scores but sequence agreement is just 3.7%.
- Different prompting strategies yield limited gains, and real-world video evaluations show the same concentration of success at low event counts.
Full text
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