We introduce Video-DeepResearch (Video-DR), extending multimodal agents from static images to continuous video streams, a setting that demands dense spatiotemporal grounding coupled with open-web exploration. Preliminary evaluations reveal two critical bottlenecks in current models: (1) modality bias, where agents bypass visual tools in favor of textual search, and (2) parametric knowledge leakage, where models rely on internal memory rather than genuine tool-augmented execution. To address these challenges, we propose Video-DeepResearch, featuring a decoupled perception-exploration pipeline with stage-wise tool unlocking that compels exhaustive cross-frame visual grounding prior to web retrieval. Our framework adopts a two-stage training recipe—supervised fine-tuning followed by Group Relative Policy Optimization (GRPO)—enabling autonomous exploration that breaks the imitation-learning ceiling. Furthermore, we curate VideoDR-Bench, a human-AI collaborative benchmark comprising 200 complex, multi-hop VQA instances. Empirical results demonstrate that our Video-DeepResearch-35B-A3B establishes a new state-of-the-art of 64.0% average accuracy, surpassing proprietary Claude-4.5-Sonnet (59.0%) by 5.0 points and significantly outperforming GPT-5 (52.5%) and Gemini 2.5 Pro (57.5%). The 30B-A3B variant achieves 59.3%, competitive with Claude-4.5-Sonnet and demonstrating the effectiveness of our training paradigm even at compact scale.

In this paper, we present the first cross-lingual dataset that captures a transnational cultural phenomenon, focusing on the Chinese and Japanese “Jirai” subculture and its association with risky health behaviors. Our dataset of more than 15,000 annotated social media posts forms the core of JiraiBench, a benchmark designed to evaluate LLMs on culturally specific content. This unique resource allowed us to uncover an unexpected cross-cultural transfer in which Japanese prompts better handle Chinese content, indicating that cultural context can be more influential than linguistic similarity. Further evidence suggests potential cross-lingual knowledge transfer in fine-tuned models. This work proves the indispensable role of developing culturally informed, cross-lingual datasets for creating effective content moderation tools that can protect vulnerable communities across linguistic borders.