ViTexQA: A Multi-Frame Temporal Perception Dataset for Video Text Question Answering

Despite remarkable progress in multimodal understanding,current MLLMs still exhibit limitations in video text understanding, par-ticularly when semantics emerge through the integration of temporallydistributed textual cues across multiple frames. This perception challengefundamentally differs from static image text understanding, yet existingdatasets fail to capture: the vast majority of questions remain answerablefrom single frames, inadequately reflecting real-world video text compre-hension demands. To address this, we present ViTexQA, a large-scalevideo-text QA dataset, and FrameThinker for robust multi-frame tem-poral reasoning. We build ViTexQA via a quality-controlled Chain-of-Thought (CoT) annotation pipeline boosted with temporal constraints;all its QA pairs demand cross-frame text fusion to solve, enforcing truetemporal reliance. FrameThinker adopts two-stage training for explicittemporal modeling: CoT-Guided Supervised Fine-Tuning (SFT) gen-erates frame-aware reasoning chains, followed by Temporally-groundedReinforcement Learning (RL) optimized with multi-frame coherence re-wards. Evaluations show our method outperforms SOTA baselines onViTexQA, lifting ROUGE-L by 6.3%.