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01yatin-superintelligence /Audio-Video-Engineering-Agentic-Tasks-1M Audio/Video Engineering Agentic Tasks (1M) Abstract A highly specialized dataset comprising 1,029,459 in-context troubleshooting prompts and execution commands built for the deepest levels of media production. Unlike standard datasets that simulate clean, theoretical instructions, this matrix captures the chaotic, highly-detailed, and conversational reality of professional audio engineers, composers, and video editors mid-session. It is engineered to train multimodal AI… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/Audio-Video-Engineering-Agentic-Tasks-1M.tabulartext-generation1M<n<10M14 likes1.2k downloads7mo agoHugging Face02ameddserM /agentic_vbench_video_repair0 likes875 downloads4mo agoHugging Face03ameddserM /agentic_vbench_video_repurposevideon<1K0 likes621 downloads4mo agoHugging Face04ameddserM /agentic_vbench_video_sequencingvideon<1K0 likes508 downloads4mo agoHugging Face05ameddserM /agentic_vbench_video_assemblyvideon<1K0 likes463 downloads4mo agoHugging Face06ppppppz /video_agent_trace Friday prompt delivery — 30K text-only corpus 本目录是可直接消费的 prompt 交付包;所有内容均来自已完成的 Friday GPT 文本生产,不包含视频,也不表示 H3/视频评测通过。 文件 文件 内容 top_historical_test_prompts_4of5.jsonl 第一轮多轮历史实验中全局最高接受率的两条 test prompt(各 4/5)。 family_champion_test_prompts.jsonl 四个家族各自采用的 champion test prompt;包含 4/5、4/5、3/5、3/5 的历史结果与选择理由。 base_prompts_1200.jsonl 1,200 条干净 base prompt,每条有稳定 base_id。 injection_prompts_30000.jsonl 30,000 条最终 injection prompt;prompt 是可直接读取的 prompt… See the full description on the dataset page: https://huggingface.co/datasets/ppppppz/video_agent_trace.tabularn<1K0 likes192 downloads24d agoHugging Face07SnoopyFan /agentic_videovideon<1K0 likes167 downloads6d agoHugging Face08Agents-X /sft_data_vsi_wo_video_hint tabular1K<n<10K0 likes73 downloads1y agoHugging Face09Agents-X /PyVision-Video-RL-Data PyVision-Video-RL-Data Project Page | Paper | GitHub This repository contains the reinforcement learning (RL) data used to train PyVision-Video-RL, as presented in the paper PyVision-RL: Forging Open Agentic Vision Models via RL. PyVision-RL is a reinforcement learning framework for open-weight multimodal models that stabilizes training and sustains interaction. For video reasoning, PyVision-Video employs on-demand context construction, selectively sampling task-relevant frames… See the full description on the dataset page: https://huggingface.co/datasets/Agents-X/PyVision-Video-RL-Data.textvideo-text-to-text10K<n<100K0 likes64 downloads7mo agoHugging Face10Agents-X /sft_data_longvila_wo_video_hint tabular10K<n<100K0 likes59 downloads1y agoHugging Face11DeepNLP /video-generator-ai-agent Video Generator Agent Meta and Traffic Dataset in AI Agent Marketplace | AI Agent Directory | AI Agent Index from DeepNLP This dataset is collected from AI Agent Marketplace Index and Directory at http://www.deepnlp.org, which contains AI Agents's meta information such as agent's name, website, description, as well as the monthly updated Web performance metrics, including Google,Bing average search ranking positions, Github Stars, Arxiv References, etc. The dataset is helpful for AI… See the full description on the dataset page: https://huggingface.co/datasets/DeepNLP/video-generator-ai-agent.textn<1K3 likes27 downloads1y agoHugging Face12Funnymouth /video_debug_agent_2gated dataset_v1_packed — 视频生成缺陷标注数据集 每条样本 = 一个视频生成 case:生成需求(input_text) + 参考素材(input_media) + 成片(output_media) + 缺陷标注(label)。 Schema 字段 说明 id 样本编号 input_text 视频生成需求脚本(分镜/提示词) input_media 参考素材相对路径,顺序为 图→视频→音频 output_media 生成成片相对路径 label 缺陷标注串:<interval>起-止</interval><tag>大类:小类</tag><feedback>缺陷描述</feedback> × N 687 条样本;标签体系为 8 大类 / 31 小类闭集。 interval 单位秒,保留 1 位小数,按时间升序。 用途 仅限学术研究。媒体素材为短剧成片画面,请勿再分发。 imagevideo-text-to-textn<1K0 likes18 downloads3mo agoHugging Face13Agents-X /PyVision-Video-SFT-DataPyVision-RL: Forging Open Agentic Vision Models via RL This is the SFT data used to train PyVision-Video-SFT. @article{pyvisionrl2026, title={PyVision-RL: Forging Open Agentic Vision Models via RL}, author={Zhao, Shitian and Lin, Shaoheng and Li, Ming and Zhang, Haoquan and Peng, Wenshuo and Zhang, Kaipeng and Wei, Chen}, journal={arXiv:2602.20739}, year={2026} } 0 likes16 downloads7mo agoHugging Face14XAE2003 /videoagent_sft_v20 likes14 downloads1mo agoHugging Face15ilovevideoeditor-ai /ai-video-agent-review-checks AI Video Agent Review Checks A compact, implementation-oriented checklist for evaluating AI-generated video plans and renders before publication. The resource separates deterministic validation from creative review. It is intended for agent developers, video infrastructure teams, and evaluators building repeatable JSON-to-MP4 workflows. Why this resource exists A render can finish successfully and still fail editorially. Text may overflow, the CTA may arrive too… See the full description on the dataset page: https://huggingface.co/datasets/ilovevideoeditor-ai/ai-video-agent-review-checks.n<1K0 likes13 downloads2mo agoHugging Face16yitongl /agent_video_accelerationvideon<1K0 likes11 downloads3mo agoHugging Face17Agents-X /rl_data_vsi_filtered_wo_video_hintgatedtext10K<n<100K0 likes3 downloads1y agoHugging Face18abhranil14 /VideoAgent_Dataimage0 likes1 downloads1y agoHugging Face19Agents-X /rl_data_longvila_filtered_wo_video_hintgatedtext10K<n<100K0 likes1 downloads1y agoHugging Face

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