Long-video understanding (LVU) is increasingly framed as an agentic process that iteratively localizes task-critical moments before perception and reasoning. Moment localization is therefore a key bottleneck: under long-video context constraints, sparse sampling can easily miss decisive evidence, while similarity-based retrieval often returns semantically related but task-irrelevant clips. We present EviScan, an evidence-seeking moment localization model built on an MLLM, designed for task-oriented localization and near-second temporal scanning on hour-long videos. To learn task-oriented localization, we construct ToMo-Pile, a training set that pairs temporal intervals with rationale traces explaining why the interval resolves the task. To make near-second scanning feasible on long videos, EviScan uses DualKV Prefill for memory-efficient streaming inference, and Progressive Localization to first narrow down candidate intervals and then refine boundaries using high-resolution evidence. We further introduce ToMo-Bench, a benchmark for task-oriented moment localization across diverse long-video scenarios. EviScan consistently outperforms specialized localizers and general-purpose MLLMs on both ToMo-Bench and standard temporal grounding benchmarks. When used as a plug-in localization module, it yields substantial gains on downstream long-video understanding benchmarks (MLVU and LongVideoBench), and these gains are larger than those brought by other moment-localization methods. We will release the model, dataset, benchmark, and code.

With the popularity of multimodal techniques, it receives growing interests to acquire useful information in visual forms. In this work, we formally define an emerging IR paradigm called Visualized Information Retrieval, or Vis-IR, where multimodal information, such as texts, images, tables and charts, is jointly represented by a unified visual format called Screenshots, for various retrieval applications. We further make three key contributions for Vis-IR. First, we create VIRA (Vis-IR Aggregation), a large-scale dataset comprising a vast collection of screenshots from diverse sources, carefully curated into captioned and question-answer formats. Second, we develop UniSE (Universal Screenshot Embeddings), a family of retrieval models that enable screenshots to query or be queried across arbitrary data modalities. Finally, we construct MVRB (Massive Visualized IR Benchmark), a comprehensive benchmark covering a variety of task forms and application scenarios. Through extensive evaluations on MVRB, we highlight the deficiency from existing multimodal retrievers and the substantial improvements made by UniSE. Our data, model and benchmark have been made publicly available, which lays a solid foundation for this emerging field.