---
license: other
license_name: mixed-per-asset
license_link: https://anonymous.4open.science/r/multimedia-terminal-bench-3532/ATTRIBUTIONS.md
task_categories:
  - other
language:
  - en
pretty_name: MultiMedia-TerminalBench (MMTB)
size_categories:
  - n<1K
tags:
  - benchmark
  - agent-evaluation
  - multimodal
  - terminal-agents
  - audio-perception
  - video-perception
  - image-perception
  - multimedia-tools
  - harbor-framework
configs: []
viewer: false
---

# MultiMedia-TerminalBench (MMTB) — Benchmark Media Hub

Media assets accompanying the paper *MMTB: Evaluating Terminal Agents on Multimedia-File Tasks*.

![Overview of MMTB tasks and terminal-agent approaches](mmtb-figure1-overview.png)

*Figure 1.* **An example MMTB task and two terminal-agent approaches.** The
task merges three videos and one audio file into one edited artifact.
Agents with native multimodal access read the raw files directly; text-only
agents must reach the same evidence through command-line tools (OCR, ASR,
motion-energy), adding processing steps that introduce inefficiency and
errors.

This dataset hosts the media assets (video, audio, image, document)
consumed by the **MultiMedia-TerminalBench** benchmark — a Harbor-native
evaluation suite for terminal-based AI agents that must understand
multimedia content and act on it through persistent file workflows.

The benchmark itself (task definitions, instructions, verifiers, agent
harnesses) lives in the code repository:

- **Code repository (anonymous for review):** <https://anonymous.4open.science/r/multimedia-terminal-bench-3532/>

This Hugging Face dataset is the **media bytes layer** referenced by every
task's `media.toml`. The repository at the URL above contains the
executable benchmark; this dataset stores the source-of-record media files
those tasks fetch at build time.

## What's inside

```
mmtb-core/
├── <task-id-1>/
│   └── environment/assets/   # media files for one task
├── <task-id-2>/
│   └── environment/assets/
└── … (105 tasks; 536 declared media sources / ~570 actual files; 1.73 GB compressed on Hugging Face Hub)
```

Per-task contents include video (mp4/webm/ogv), audio (wav/mp3/flac),
images (png/jpg), and documents (pdf) — whichever modalities a task
exercises.

## Sample data / partial download

The dataset has no separate "sample subset" — each of the 105 task
subdirectories holds the media bytes for one task (between 1 MB and
~300 MB under `mmtb-core/<task-id>/environment/assets/`). The complete
Harbor task unit (instruction, verifier, oracle, and the per-task
`media.toml` manifest) lives in the companion code repository. The minimum
useful sample is therefore **one task**, fetched in seconds:

```bash
uv run python scripts/download_media.py audience-ringtone-detection
```

This downloads only that task's media and is sufficient to exercise the
Harbor build, the agent harness, and the verifier end-to-end. A small
audio task such as `audience-ringtone-detection` is a fast smoke test;
`av-desync-detection` (Blender CC-BY film clips) is a video-perception
representative.

Per-task download metadata (file count, size, sha256) is in each task's
`media.toml` manifest in the companion code repository (this Hugging Face
dataset hosts the media bytes; the `media.toml` files live alongside the
task definitions in the code repository).

## How to use

Download a single task's assets:

```bash
uv run python scripts/download_media.py <task-id>
```

(Run from the cloned code repository. The script reads each task's
`media.toml`, fetches files from this dataset, and verifies sha256.)

Or fetch the entire dataset programmatically:

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="mmtb-anonymous/mmtb-media",
    repo_type="dataset",
    local_dir="./mmtb-media",
)
```

## License

**Per-asset license details follow.** The HF top-level `license_name: mixed-per-asset` indicates that the dataset combines multiple per-file licenses; the **full mixture includes Apache-2.0, MIT, CC-BY family, CC0, Public Domain, ODbL, GPL, CC-BY-NC, and GFDL** with restrictions noted below. The 4 NC/research-only files and 7 GPL files carry redistribution constraints that downstream users should review.

Per-asset licenses are recorded in each task's `media.toml` under
`[media.source].license`. All 536 declared media records carry a license
tag in this field. The aggregate table below sums to 498 records under a
license-family rollup; this is a categorization-rollup count rather than a
record-level count, and does not indicate any record without license
metadata. The paper's Appendix B.2 Table 11 reports 497 records under a
slightly different aggregation; this artifact lists 498 because the
Wav2Lip-derived non-commercial output is recorded as its own row. The
underlying byte content is identical.

| License family | File count | Examples |
|---|---:|---|
| Apache-2.0 (incl. Kokoro-82M TTS) | 90 | TTS speech (CPU-fast voices) |
| MIT | 78 | Author-built assets (ffmpeg / FluidSynth / LilyPond / Godot pipelines) |
| ODbL-1.0 | 88 | Map-tile derivatives for one geo task |
| Public Domain / PD / Public Domain Mark 1.0 | 84 | Historical recordings (archive.org), NASA media |
| CC-BY family (3.0 / 4.0 / SA-3.0 / SA-4.0 / 2.0) | 110 | Blender Foundation open movies, Wikimedia, author-contributed footage |
| CC0-1.0 (incl. Kenney game asset packs) | 35 | Game-task graphics + sound effects |
| GPL-3.0+ / GPL-2.0+ | 7 | A small number of GPL-licensed engine/font derivatives |
| CC-BY-NC-SA-4.0 / CC-BY-NC-4.0 | 3 | Non-commercial-restricted; flagged in `media.toml` |
| Wav2Lip-derived (research-use only) | 1 | `debate-attribution` lip-synced output inherits the Rudrabha/Wav2Lip non-commercial research-use license |
| GFDL 1.2 | 2 | Wikimedia legacy items |

A small number of files (4 NC / research-only + 7 GPL) carry restrictions
that downstream users should review before redistributing. Per-file license
strings, source URLs, and sha256 hashes are recorded in each task's
`media.toml` (in the code repository). See
[`ATTRIBUTIONS.md`](ATTRIBUTIONS.md) (this dataset) and
[`ATTRIBUTIONS.md` in the code repository](https://anonymous.4open.science/r/multimedia-terminal-bench-3532/ATTRIBUTIONS.md)
for canonical attribution text.

## Documentation and disclosures

For a full datasheet — dataset composition, source datasets, construction
methods, biases, ethics, and Croissant 1.1 with RAI fields — see
[`DATASHEET.md`](DATASHEET.md). The machine-readable Croissant metadata is
in [`croissant.jsonld`](croissant.jsonld).

## Dataset details

- **Tasks:** 105 (across 5 meta-categories; 16 fine-grained workflow
  categories; 12 capability tags)
- **Media files:** 536 declared sources / ~570 actual files; 1.73 GB compressed on Hugging Face Hub
- **Modalities exercised:** video, audio, image, document/PDF
- **Languages:** primarily English (text + spoken content)
- **Companion code repository (anonymous for review):** <https://anonymous.4open.science/r/multimedia-terminal-bench-3532/>

## Citation

```bibtex
@software{mmtb,
  title  = {{MMTB}: Evaluating Terminal Agents on Multimedia-File Tasks},
  author = {Anonymous Authors},
  url    = {https://anonymous.4open.science/r/multimedia-terminal-bench-3532},
  year   = {2026}
}
```