The Low Frequency Trap: Video Language Models Fail at Simple Event Bookkeeping

2026-08-06

AI Summary

This work addresses a critical limitation in video-language models: their poor temporal reasoning on high-frequency or transient event counting tasks, which remains undetected by conventional evaluation protocols that rely solely on final answers. To diagnose this issue, the authors propose the first trajectory-grounded, parameterized evaluation framework, leveraging synthetically generated videos—such as bouncing balls and visual flickers—with executable event trajectories to enable timestamp-level, fine-grained assessment. Experiments reveal that current models are only reliable under low-frequency, persistent state transitions (≤12 events at 0.5–1.0 Hz), while their performance drastically degrades in high-frequency, high-count scenarios, achieving as low as 0.2% accuracy and 18.1% event recall. Although increasing frame rates marginally improves final scores, it fails to restore fidelity in event sequence reconstruction, exposing fundamental limitations in existing temporal modeling capabilities.

Abstract

Real-world video benchmarks provide broad coverage, but their fixed clips entangle event count, rate, duration, and visual complexity, making failure modes hard to isolate. While existing programmatic benchmarks offer better control, they score only the final answer rather than auditing reported events against executable ground truth. To bridge this gap, we introduce trace-grounded parametric profiling for event counting in three controlled video tasks: bouncing-ball wall contacts, visual blinks, and categorical state transitions. Across 2,190 videos, we vary event count N and frequency F while holding rendering fixed. Each video includes an executable event trace for capability-surface estimation and timestamp-level evaluation. Our results reveal a staged temporal failure. At an 80% reliability threshold, Gemini 3.6 Flash reliably counts persistent state transitions up to 12 events at 0.5 and 1.0 Hz, yet demonstrates no reliable positive-count region for transient blinking events. Thus, event representation dictates whether a model initially accesses evidence -- a limitation that compounds as count and frequency increase. 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. To test if visual access is the primary bottleneck, we increase sampling rate. Although this boosts Bounce Ball accuracy from 19.6% to 29.3%, the reported sequence agrees with ground truth only 3.7% of the time. Extra frames can therefore inflate final scores without producing faithful event recovery. Different prompting strategies yield similarly limited gains, and real-world video evaluations show the same concentration of success at low event counts. Ultimately, trace-grounded profiling shifts video evaluation from aggregate accuracy metrics to a detailed diagnostic of where temporal reasoning fails.

Problem

• video language models
• event counting
• temporal reasoning
• failure modes
• trace-grounded evaluation

Innovation

• trace-grounded evaluation
• parametric profiling
• event counting
• temporal reasoning failure
• video language models