Real-time, continuous understanding of visual signals is essential for real-world interactive AI applications, and poses a fundamental system-level challenge. Existing research on streaming video understanding, however, typically focuses on isolated aspects such as question-answering accuracy under limited visual context or improvements in encoding efficiency, while largely overlooking practical deployability under realistic resource constraints. To bridge this gap, we introduce StreamingEval, a unified evaluation framework for assessing the streaming video understanding capabilities of Video-LLMs under realistic constraints. StreamingEval benchmarks both mainstream offline models and recent online video models under a standardized protocol, explicitly characterizing the trade-off between efficiency, storage and accuracy. Specifically, we adopt a fixed-capacity memory bank to normalize accessible historical visual context, and jointly evaluate visual encoding efficiency, text decoding latency, and task performance to quantify overall system deployability. Extensive experiments across multiple datasets reveal substantial gaps between current Video-LLMs and the requirements of realistic streaming applications, providing a systematic basis for future research in this direction. Codes will be released upon acceptance.

Large Reasoning Models (LRMs) achieve strong performance on complex tasks through explicit multi-step reasoning, but their long reasoning traces also expose them to denial-of-service (DoS) attacks that induce excessive computation. Existing DoS attacks rely on indirect, heuristic objectives to optimize adversarial inputs, without leveraging the internal mechanisms that drive long reasoning, resulting in relatively inefficient optimization and weaker attack effectiveness. In this paper, we show that over-reasoning in LRMs is mediated by a sparse neuron-level mechanism that can be systematically exploited. Specifically, we introduce Aha Neuron Localization (ANL), a method for identifying reasoning-related neurons whose activations are strongly associated with the generation of reasoning tokens, and demonstrate that selectively amplifying their activations induces over-reasoning. Based on this insight, we develop ANL-guided DoS Attack (AGDA), which optimizes a short adversarial suffix using reasoning-related neurons as precise and stable white-box signals. When attached to user queries, the suffix induces over-reasoning. Experiments show that the adversarial suffixes learned by AGDA yield reasoning traces over 3.45 × longer than the original outputs and more than 2 × longer than the strongest method, while transferring effectively to closed-source models.