---
pretty_name: MIRAGE
dataset_info:
- config_name: MMST_Standard
  splits:
  - name: train
    num_examples: 17532
  - name: test
    num_examples: 8184
- config_name: MMST_Contextual
  splits:
  - name: test
    num_examples: 3934
- config_name: MMMT_Direct
  splits:
  - name: train
    num_examples: 3876
  - name: dev
    num_examples: 878
  - name: test
    num_examples: 861
- config_name: MMMT_Decomp
  splits:
  - name: train
    num_examples: 3876
  - name: dev
    num_examples: 878
  - name: test
    num_examples: 861
configs:
- config_name: MMST_Standard
  data_files:
  - split: train
    path: MMST_Standard/train-*.parquet
  - split: test
    path: MMST_Standard/test-*.parquet
- config_name: MMST_Contextual
  data_files:
  - split: test
    path: MMST_Contextual/test-*.parquet
- config_name: MMMT_Direct
  default: true
  data_files:
  - split: train
    path: MMMT_Direct/train-*.parquet
  - split: dev
    path: MMMT_Direct/dev-*.parquet
  - split: test
    path: MMMT_Direct/test-*.parquet
- config_name: MMMT_Decomp
  data_files:
  - split: train
    path: MMMT_Decomp/train-*.parquet
  - split: dev
    path: MMMT_Decomp/dev-*.parquet
  - split: test
    path: MMMT_Decomp/test-*.parquet
license: cc-by-sa-4.0
task_categories:
- image-text-to-text
- visual-question-answering
language:
- en
tags:
- vqa
- agriculture
- agml
- vision-language
- mirage
size_categories:
- 100K<n<1M
---

# MIRAGE

MIRAGE is a benchmark for multimodal, expert-guided agricultural consultations,
introduced in ["MIRAGE: A Benchmark for Multimodal Information-Seeking and
Reasoning in Agricultural Expert-Guided Conversations"](https://arxiv.org/abs/2506.20100)
(arXiv:2506.20100, NeurIPS 2025). It's built from real crop health and pest
diagnosis consultations — curated down from 218,000 raw interactions
(2012–2025) to 37,512 high-quality ones, spanning 7,600+ distinct biological
entities (plants, pests, diseases). Unlike closed-set VQA benchmarks, MIRAGE's
scenarios are underspecified and open-world by design, requiring models to
recognize when they need more context rather than guessing. In the paper's
evaluation, 20+ frontier vision-language models were tested, with the
best (GPT-4.1) reaching only 43.9% identification accuracy.

## Configs

MIRAGE ships as four configs, corresponding to the paper's task families:

| config | description | splits |
|---|---|---|
| `MMST_Standard` | Single-turn, self-contained questions answerable from the given text + image(s) alone — entity identification, causal explanation, visually-grounded answers. | train, test |
| `MMST_Contextual` | Single-turn questions with a latent information gap — the model must recognize missing context (location, time, agricultural background) rather than answer directly. | test |
| `MMMT_Direct` **(default)** | Multi-turn dialogues; at each turn the model must decide whether to ask a clarifying question or answer, and generate that utterance directly. | train, dev, test |
| `MMMT_Decomp` | The same underlying multi-turn dialogues as `MMMT_Direct`, reformulated as a decomposed reasoning task (hence the matching unique-image counts between the two configs below). | train, dev, test |

`MMMT_Direct` is set as the default config.

## Layout

Each config is a self-contained folder — its own metadata parquet(s) plus its
own `images/` folder of zip shards — following the same storage format across
every AgML-standardized dataset:

```
MIRAGE_P/
  MMST_Standard/
    train-0000-of-0001.parquet   # 17,532 rows
    test-0000-of-0001.parquet    #  8,184 rows
    images/
      image-mmst_standard-000-of-003.zip ... 002   # 46,874 unique images, 6.92GB
      path_to_shard.parquet
  MMST_Contextual/
    test-0000-of-0001.parquet    #  3,934 rows
    images/
      image-mmst_contextual-000-of-001.zip         #  8,032 unique images, 1.33GB
      path_to_shard.parquet
  MMMT_Direct/
    train-0000-of-0001.parquet   #  3,876 rows
    dev-0000-of-0001.parquet     #    878 rows
    test-0000-of-0001.parquet    #    861 rows
    images/
      image-mmmt_direct-000-of-001.zip             #  7,995 unique images, 1.32GB
      path_to_shard.parquet
  MMMT_Decomp/
    train-0000-of-0001.parquet   #  3,876 rows
    dev-0000-of-0001.parquet     #    878 rows
    test-0000-of-0001.parquet    #    861 rows
    images/
      image-mmmt_decomp-000-of-001.zip             #  7,995 unique images, 1.32GB
      path_to_shard.parquet
```

Every row has: `images`, `id`, `messages`, `raw_metadata`. `images` holds
`{"bytes": None, "path": ...}` per referenced image — but unlike AgroOmni,
`path` here is a **SHA256 content hash**, not a filesystem path: MIRAGE's
source data has no path metadata at all (images exist only as embedded bytes
per row), so the hash is what makes dedup and shard lookup possible.
`path_to_shard.parquet` maps each unique hash to the shard filename holding
it. `answer_type` (mcqa/oeqa) lives inside `raw_metadata` rather than as its
own column, keeping the schema consistent with every other AgML dataset.

**Not auto-loadable via `load_dataset()`.** Requires the companion loader
(`load_agml_dataset()`), which reads images lazily and directly out of the
zip shards — no extraction step.

## Why content hashing instead of paths

The companion loader looks up each image independently via
`zipfile.ZipFile.open(member_name)` — true random access via zip's central
directory, no extraction needed — so, as with AgroOmni, there's no
correctness requirement that a row's co-referenced images share a shard.
Since MIRAGE has no natural path identifiers, a SHA256 hash of each image's
raw bytes is used instead, which has the added benefit of deduplicating any
images that repeat across rows within a config.

## Usage

```python
from agml import loadImageTextToTextDataset

ds = loadImageTextToTextDataset(
      "Project-AgML/MIRAGE",
      config="MMMT_Direct",
      token=HF_TOKEN
)
print(ds)                 # DatasetDict({'train': ..., 'dev': ..., 'test': ...})
ds["train"][0]            # images decoded lazily on access
```

## Citation

If you use this dataset, please cite the original MIRAGE paper:

```bibtex
@misc{mirage2025,
  title  = {MIRAGE: A Benchmark for Multimodal Information-Seeking and Reasoning in Agricultural Expert-Guided Conversations},
  author = {Dongre, Vardhan and Gui, Chi and Garg, Shubham and Nayyeri, Hooshang and Tur, Gokhan and Hakkani-T{\"u}r, Dilek and Adve, Vikram S.},
  year   = {2025},
  eprint = {2506.20100},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  note   = {NeurIPS 2025},
  url    = {https://arxiv.org/abs/2506.20100}
}
```

Project page: https://mirage-benchmark.github.io/

Code: https://github.com/MIRAGE-Benchmark/MIRAGE-Benchmark

---

This dataset is indexed and structured on https://project-agml.github.io/ as part of the AgML python library. This dataset was reformatted from its original format to match HuggingFace's Imagefolder standards but requires an external module (agml) that processes and returns a HF Dataset object faster than HF module functions.