Datasetpublic

agentvidbench/agentvidbench-sample

AgentVidBench (Sample): A Multi-Hop Video Question Answering Benchmark for Evaluating MLLM Agents This repository is a representative sample of AgentVidBench, provided so reviewers can inspect data quality without downloading the full ~4GB+ corpus. The full dataset remains available at the link above. Sample selection The sample contains the first 10 questions (question_id 1–10) and the 9 unique videos they reference. IDs and filenames are preserved from the… See the full description on the dataset page: https://huggingface.co/datasets/agentvidbench/agentvidbench-sample.

sourceHugging Faceccupdated 2mo agoView on Hugging Face

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Dataset Card

AgentVidBench (Sample): A Multi-Hop Video Question Answering Benchmark for Evaluating MLLM Agents

This repository is a representative sample of AgentVidBench, provided so reviewers can inspect data quality without downloading the full ~4GB+ corpus. The full dataset remains available at the link above.

Sample selection

The sample contains the first 10 questions (question_id 1–10) and the 9 unique videos they reference. IDs and filenames are preserved from the full dataset — no renumbering — so any record in this sample can be cross-referenced with the full dataset by its question_id or file_name.

—question_id 1–10 (contiguous)
—Videos referenced: video1, video2, video4, video5, video6, video7, video8, video9, video10 (9 unique; video2 is referenced by both questions 2 and 3, and video3 is not referenced by any of the first 10 questions)
—Whisper transcripts (.srt) are included for each of the 9 videos
—All schemas, fields, and file formats are identical to the full dataset

Layout

.
├── README.md
├── questions.jsonl        # 10 rows — first 10 questions of the full dataset
├── videos.jsonl           # 9 rows — videos referenced by those questions
├── videos/
│   └── video*.mp4         # 9 video files
└── transcripts/
    └── video*.srt         # 9 Whisper transcripts

`questions.jsonl` schema

Field	Type	Notes
`question_id`	int	1–100 in the full dataset (1–10 in this sample)
`video_path`	str	relative path to the video file (`videos/videoN.mp4`); equals the `file_name` in `videos.jsonl`
`transcript_path`	str	relative path to the Whisper transcript (`transcripts/videoN.srt`); shares the basename with `video_path`
`title`	str	short descriptive title
`question_text`	str	full question prompt
`options`	list[str] (len 26)	answer choices in order A, B, C, … Z
`answer`	str	correct answer letter, A–Z
`answer_explanation`	str	reasoning + reference for the answer
`skills`	list[str]	core capabilities the question tests
`difficulty`	str	`easy` / `medium` / `hard` / `very_hard`
`options_type`	str	distractor strategy
`categories`	list[str]	cognitive task tags
`milestones`	list[step]	sub-tasks the question tests; each step is `{id, type, description}`
`ecom_required`	bool	true iff the answer cannot be locked in until later evidence in the video has been observed (i.e., the full clip must be watched)
`trajectory`	dict	Reasoning trace. Shape: `{steps: list[step], final_answer: str, model: str, temperature: float, generated_at: str, elapsed_seconds: float}`. Each step is `{tool: str, args: dict, thought: str, observation: str}`; `args` is heterogeneous per tool (HF infers it as a `Json` feature). Populated for all questions.

`videos.jsonl` schema

Field	Type	Notes
`file_name`	str	relative path to the video file (`videos/videoN.mp4`)
`title`	str	—
`duration`	str	`M:SS`
`genre`	str	one of 12 enum values
`production_style`	list[str]	shot/edit style tags
`audio_type`	list[str]	audio characteristics
`length_category`	str	`short` / `medium` / `long`
`url`	str	source URL
`license`	str	license
`source`	str	—
`author`	str	—

Download

bash

hf download agentvidbench/agentvidbench-sample --repo-type dataset --local-dir dataset-sample

The full dataset is at `agentvidbench/agentvidbench`.