Update README.md

README.md

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 ## Uses
 
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 ### Direct Use
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+- **Benchmark Evaluation**: Systematic evaluation of state-of-the-art MLLMs on temporal harmful content localization, detection, and modality attribution tasks
+- **Model Development**: Training and fine-tuning multimodal models for video safety understanding, harmful content moderation, and temporal reasoning
+- **Research Analysis**: Studying the detection-localization gap of MLLMs, cross-category performance variation, and multimodal dependency patterns in harmful content understanding
+- **Safety System Validation**: Testing the robustness of video moderation systems on realistic long-form video scenarios
 
 ### Out-of-Scope Use
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+- **Malicious Use**: Using the dataset to generate, distribute, or promote harmful content
+- **Unauthorized Redistribution**: Redistributing the raw video files (not included in this dataset release) in violation of original platform terms of service
+- **Commercial Use**: Using the dataset for commercial purposes without explicit authorization from the authors
+- **Unrelated Tasks**: Using the dataset for tasks outside of video safety, harmful content understanding, and temporal localization research
+- **Biased Model Training**: Training models that perpetuate harmful stereotypes or biases using the dataset
 ## Dataset Structure
 The dataset is released as a single CSV file with 450 rows (one row per video) and 4 core columns, with segment-level annotations stored in structured list format:
 
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 ## Dataset Creation
 
 ### Curation Rationale
+Existing multimodal safety benchmarks primarily focus on video-level harmfulness recognition or safe response generation, rather than precise temporal localization of harmful content in long videos. This creates a critical gap: it remains unclear whether current MLLMs can reliably support temporally grounded harmful video moderation in real-world scenarios, where harmful content is often sparse and embedded in extended contextual content.
 
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+THVL-Bench was created to address this gap by:
+- Defining the THVL task, which requires joint detection, temporal localization, category classification, and modality attribution
+- Providing a high-quality, manually annotated benchmark of real-world long videos
+- Enabling systematic evaluation of MLLMs' capabilities and limitations in temporally precise harmful content understanding
 
 ### Source Data
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+All videos in THVL-Bench are collected from publicly accessible platforms, including YouTube and Bilibili, for research purposes only. The dataset focuses on long-form untrimmed videos (all ≥3 minutes in duration) to reflect realistic moderation scenarios, where harmful events are often temporally sparse relative to the full video duration.
 
 #### Data Collection and Processing
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+- **Taxonomy Definition**: Adopted and adapted existing online harm taxonomies from prior safety and harmful content research to define 11 fine-grained harmful categories
+- **Keyword Search**: Manually constructed category-specific keyword sets and search phrases associated with harmful behaviors, dangerous activities, hate expressions, offensive interactions, criminal events, and sensitive social content
+- **Candidate Retrieval**: Used the constructed keywords to retrieve candidate videos from the search engines of YouTube and Bilibili
+- **Manual Filtering**: Candidate videos were manually reviewed to remove duplicated, inaccessible, low-quality, or irrelevant samples
+- **Duration Filtering**: Only videos exceeding 3 minutes in duration were retained to focus on long-form video scenarios
 
 ### Source Data Producers
 The source videos are publicly available content created by users of YouTube and Bilibili. The dataset release only includes annotations and metadata, not the raw video files, in compliance with the original platforms' terms of service.
 
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 ### Annotations