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arXiv · 2408.07867

Continuous Perception Matters: Diagnosing Temporal Integration Failures in Multimodal Models

Zeyu Wang·Zhenzhen Weng·Serena Yeung-Levy
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Abstract

Continuous perception, the ability to integrate visual observations over time in a continuous stream fashion, is essential for robust real-world understanding, yet remains largely untested in current multimodal models. We introduce CP-Bench, a minimal and fully controlled benchmark designed to isolate this capability using an extremely simple task: counting identical cubes in a synthetic scene while the camera moves and only reveals subsets of objects at any moment. Despite the simplicity of the setting, we find that state-of-the-art open-source and commercial models, including Qwen-3-VL, InternVL3, GPT-5, and Gemini-3-Pro, fail dramatically. A static-camera control variant confirms that the failure arises not from object recognition but from an inability to accumulate evidence across time. Further experiments show that neither higher sampling FPS, perception- or spatial-enhanced models, nor finetuning with additional videos leads to meaningful cross-temporal generalization. Our results reveal a fundamental limitation in modern multimodal architectures and training paradigms. CP-Bench provides a simple yet powerful diagnostic tool and establishes a clean testbed for developing models capable of genuine time-consistent visual reasoning.

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Zeyu Wang, Zhenzhen Weng, Serena Yeung-Levy. 2025-12-01. Continuous Perception Matters: Diagnosing Temporal Integration Failures in Multimodal Models. https://arxiv.org/abs/2408.07867

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