UpstreamQA: A Modular Framework for Explicit Reasoning on Video Question Answering Tasks
Video Question Answering (VideoQA) demands models that jointly reason over spatial, temporal, and linguistic cues. However, the task's inherent complexity often requires multi-step reasoning that current large multimodal models (LMMs) perform implicitly, leaving their internal decision process opaque. In contrast, large reasoning models (LRMs) explicitly generate intermediate logical steps that enhance interpretability and can improve multi-hop reasoning accuracy. Yet, these models are not designed for native video understanding, as they typically rely on static frame sampling. We propose UpstreamQA, a modular framework that disentangles and evaluates core video reasoning components throu...
| Authors | Jason Nguyen, Ameet Rao, Alexander Chang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 25 April 2026 |
| Last updated at source | 25 April 2026 |
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