Beyond Frame Selection: Rethinking Long-Video Understanding with MLLMs

Abstract

Multimodal Large Language Models (MLLMs) have made strong progress in video understanding, yet long videos remain difficult: the visual token budget grows with video length, so temporally sparse evidence is easily lost. Existing methods, whether frame selection or agent-based exploration, ultimately rely on a single frame set that must trade off broad temporal coverage against fine-grained, question-relevant evidence: mixing both dilutes decisive evidence, while focusing on either sacrifices the other. We propose VideoRouter (VR), which rethinks long-video understanding as coordinating complementary evidence views rather than selecting a single subset of frames. VideoRouter first organizes each video into a question-agnostic temporal hierarchy that partitions it into coarse-to-fine temporally coherent segments. Upper-level nodes capture broad storyline context and event progression, while lower-level nodes preserve fine-grained local details and evidence-bearing moments. This gives rise to two complementary views: a global view for coverage-oriented reasoning and a local view for detail-oriented evidence recovery. We further introduce a verification-guided router that judges which view is better supported by its own selected evidence and decides the final answer. Across six backbones, routing improves over both views in all settings, and the choice of view is shown to be dataset-dependent, confirming that no single evidence granularity is universally preferable. On VideoMME and LongVideoBench, our method outperforms state-of-the-art frame-selection methods by 2.5 and 1.3 points, respectively, under the LLaVA-Video-7B backbone. We will release the code.