Datasets:

Project-AgML
/

MIRAGE

---
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.

	---
	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.