Recently, Multimodal Large Language Models (MLLMs) have demonstrated strong visual understanding and reasoning abilities across vision-language tasks. However, conventional Visual Question Answering (VQA) pipelines do not fully exploit the visual features and medical knowledge captured by MLLMs, leading to limited performance in zero-shot medical disease recognition. To address this issue, we propose LLaVA-RadZ, a simple yet effective framework for zero-shot radiology recognition. LLaVA-RadZ introduces Decoding-Side Feature Alignment Training (DFAT), which leverages decoder-side representations with modality-specific tokens for image-text alignment, and a Domain Knowledge Anchoring Module (DKAM), which incorporates disease-aware semantic anchors to mitigate the category semantic gap. Extensive experiments show that LLaVA-RadZ substantially improves the zero-shot disease recognition ability of MLLMs and achieves competitive performance compared with well-established CLIP-based radiology recognition methods.

Video understanding is moving beyond closed-context perception toward open-world evidence exploration, a paradigm formalized as Video Deep Research (VDR). However, existing multimodal search agents primarily target static images, and the current VDR benchmark relies on text-centric retrieval that discards crucial visual information. To address these limitations, we propose VideoSearcher, a closed-loop agentic framework that empowers Vision-Language Models with multi-tool reasoning for VDR. VideoSearcher unifies temporal localization, spatial focusing, and multimodal search within a single reasoning trajectory, enabling agents to progressively ground visual anchors, retrieve relevant evidence, and synthesize answers. To optimize knowledge-intensive reasoning trajectories, we propose Bi-branch Sequence Policy Optimization (BiSPO), a reinforcement learning algorithm that decouples tool-invocation optimization from answer-accuracy optimization. This design provides stable learning signals for both evidence-grounded reasoning and purposeful tool use. Furthermore, we construct VideoSearch-QA, the first benchmark designed to evaluate open-world video information grounding and multimodal search-based reasoning. Extensive experiments demonstrate that VideoSearcher significantly outperforms prior open-source agentic baselines across various search-oriented and multimodal understanding benchmarks. Our code is publicly available at https://github.com/Stephen-gzk/VideoSearcher.