Datasets:

SurdAI
/

CMDB-1500

---
pretty_name: Comprehensive Multimodal Decision Benchmark 1500
license: other
license_name: source-dependent
license_link: https://huggingface.co/datasets/SurdAI/CMDB-1500/blob/main/LICENSES.md
language:
- en
- zh
size_categories:
- 1K<n<10K
task_categories:
- text-classification
- question-answering
- visual-question-answering
tags:
- benchmark
- decision-models
- multimodal
- calibration
- multi-select
- text
- image
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test.parquet
---
# CMDB-1500

**Comprehensive Multimodal Decision Benchmark 1500**, curated by **SurdAI**, contains **1,500 candidate-based decision questions**: **1,200 text-only** and **300 image-based**, spanning **10 domains and 79 sources and subtasks**.

The benchmark covers answer selection, action selection, binary judgments, ordinal ratings, and multi-select decisions in a common format.

[Interactive leaderboard / 交互榜单](https://surdai.com/zh/blog/cmdb-1500#results) · [Benchmark introduction / 基准介绍](https://surdai.com/zh/blog/cmdb-1500) · [Example questions](SAMPLES.md) · [Source catalog](SOURCES.md)

![CMDB-1500 overview](assets/cmdb1500-overview.png)

## Results and models

Explore the [interactive CMDB-1500 leaderboard](https://surdai.com/zh/blog/cmdb-1500#results) on the Surd AI website. It separates text (1,200 questions), image (300 questions), and full-benchmark (1,500 questions) accuracy, with probability calibration reported separately. Text-only models do not receive an image or full-benchmark score.

Selected Surd AI results use effort 2 and the same 1,500-question benchmark:

| Model | Text accuracy ↑ | Image accuracy ↑ | Full-benchmark accuracy ↑ |
|---|---:|---:|---:|
| [SPX-CD-Pro](https://surdai.com/zh/blog/simplex-cd-release) | 79.33% | 88.67% | 81.20% |
| [SPX-CD-Flash](https://surdai.com/zh/blog/simplex-cd-release) | 77.58% | 87.33% | 79.53% |
| [SPX-CD-Omni](https://huggingface.co/SurdAI/SPX-CD-Omni) | 74.67% | 82.67% | 76.27% |

These are team-run evaluations, not official upstream leaderboard submissions. SPX-CD-Omni's reported results use the released LoRA adapter with an AWQ INT4 base; they are not measurements of the default BF16 example configuration. The [model card](https://huggingface.co/SurdAI/SPX-CD-Omni) and [evaluation settings](https://huggingface.co/SurdAI/SPX-CD-Omni/blob/main/EVALUATION.md) describe the configuration and metric scopes.

The website's cross-model I/C comparison uses a shared text-probability cohort; it is distinct from the model card's benchmark-specific I/C scopes. Compare I/C values only within the same scoring scope. See the [SPX-CD release and API overview](https://surdai.com/zh/blog/simplex-cd-release) for Pro and Flash.

## Coverage

![Question counts by domain](assets/cmdb1500-coverage.png)

| Domain | Questions | Tasks |
|---|---:|---|
| Language, knowledge, and judgment | 450 | Science, intent classification, inference, evidence, paraphrase similarity, and response assessment |
| Professional and applied decisions | 300 | Engineering, agents, commerce, finance, law, data, documents, product, support, and safety |
| Multimodal decisions | 300 | Diagram understanding, visual reasoning, science questions, image classification, and visual question answering |
| Games | 100 | Static game states and action selection across seven subtasks |
| Multi-select | 100 | MultiRC and GoEmotions |
| Safety and adversarial decisions | 100 | Content safety, phishing, and adversarial instruction handling |
| Embodied action selection | 50 | ALFRED, ALFWorld, and VirtualHome |
| Chinese ASR hypothesis selection | 50 | Selection among text transcription hypotheses |
| Tool-call necessity | 25 | BFCL-derived judgments of whether a tool call is needed |
| Spatial and complex rules | 25 | Spatial relations and rule-based decisions |
| **Total** | **1,500** | **1,200 text-only + 300 image-based** |

The image subset contains **322 images**; some questions use more than one image.

| Record format | `kind` | Questions | Expected prediction |
|---|---|---:|---|
| Choice | `choice` | 1,077 | One candidate key |
| Ordered rating | `score` | 166 | One key on an ordered scale |
| Binary format | `noul` | 157 | One key from the two supplied candidates |
| Multi-select | `multi_choice` | 100 | A set of candidate keys |

Counts follow the `kind` field. `choice` includes two-option questions; `multi_choice` permits one or more correct answers. Ordered ratings cover helpfulness, verbosity, similarity, hate-speech levels, and game-state assessments. Rating direction follows the question.

## Sources

- **Language and judgment:** 19 JevBench configurations, including Banking77, ARC-Challenge, BoolQ, CLINC, MASSIVE, MMLU, ChaosNLI, MNLI, SST-5, and HelpSteer2.
- **Professional decisions:** 31 Atlan Decision Bench tasks covering engineering, agents, commerce, finance, law, data, documents, product, support, and safety.
- **Multimodal decisions:** 30 questions each from AI2D, Fashion200k, GQA, MMMU, MMMU-Pro, MathVista, ScienceQA, TextVQA, VQAv2, and VizWiz.
- **Other domains:** Open-Jev games, MultiRC, GoEmotions, ChineseHP, Aegis, PhishNChips, JevAdvBench, BFCL v3, THIS-THAT, ALFRED, ALFWorld, and VirtualHome.

[SOURCES.md](SOURCES.md) lists all question counts, upstream links, and task definitions. Subtasks may share an upstream dataset.

## Load the dataset

```python
from datasets import load_dataset
bench = load_dataset("SurdAI/CMDB-1500", split="test")
item = bench[0]
model_input = {
    key: item[key]
    for key in ("state", "question", "options", "kind", "images")
}
```

## Data format

The `test` split is available as [Parquet with embedded images](data/test.parquet) and [JSONL](data/questions.jsonl). JSONL image paths are relative to the dataset directory.

| Fields | Contents |
|---|---|
| `state`, `state_format` | Context as text or serialized JSON |
| `question`, `options` | Decision instruction and candidate keys/texts |
| `kind`, `modality`, `images` | Output type and image inputs |
| `correct_keys`, `target_probs` | Reference answers and optional auxiliary targets |
| `id`, `source`, `category` | Question identifier, source, and domain |
| `source_id`, `original_split`, `source_metadata`, `label_origin` | Source provenance and reference-label basis |

Parse `state` when `state_format` is `json`. `target_probs` and `source_metadata` are JSON-serialized strings. Candidate keys are specific to each question. Use the model-input fields shown above; reference answers and provenance are for evaluation.

## Evaluation

Use accuracy for categorical choices, including binary questions, exact-set match and set F1 for multi-select, and accuracy plus level error for ordinal ratings. Calibration metrics use valid model probability outputs and the stated reference labels; report the included sample count and the binning rule. Auxiliary target distributions are not model confidence predictions.

Report domain scores and text/image results alongside an overall score, specifying the aggregation weights. Retain the full scope denominator when a native model interface cannot represent a question; report such failures separately. Text-only evaluations cover 1,200 questions and must not be presented as full 1,500-question results.

These are fixed-input decisions. Game and embodied questions assess state/action selection; BFCL questions assess tool-call necessity; Chinese ASR questions use text hypotheses. GQA, VQAv2, TextVQA, and VizWiz use yes/no subsets. Scores describe these selected tasks rather than complete upstream benchmark evaluations.

## Attribution and licensing

[LICENSES.md](LICENSES.md) contains source-specific terms and required attribution, including HelpSteer2. ChaosNLI is CC BY-NC 4.0, with attribution and noncommercial conditions; ScienceQA also has noncommercial conditions. MultiNLI permits modification and redistribution for typical machine-learning uses, with underlying source terms retained. An explicit redistribution license for SST-5 has not been identified in the inspected original release documentation. MathVista permits test-set use only. Upstream terms apply to the corresponding records.

Please cite **CMDB-1500, SurdAI**, together with the upstream datasets used.

	---
	pretty_name: Comprehensive Multimodal Decision Benchmark 1500
	license: other
	license_name: source-dependent
	license_link: https://huggingface.co/datasets/SurdAI/CMDB-1500/blob/main/LICENSES.md
	language:
	- en
	- zh
	size_categories:
	- 1K<n<10K
	task_categories:
	- text-classification
	- question-answering
	- visual-question-answering
	tags:
	- benchmark
	- decision-models
	- multimodal
	- calibration
	- multi-select
	- text
	- image
	configs:
	- config_name: default
	data_files:
	- split: test
	path: data/test.parquet
	---

	# CMDB-1500

	Comprehensive Multimodal Decision Benchmark 1500, curated by SurdAI, contains 1,500 candidate-based decision questions: 1,200 text-only and 300 image-based, spanning 10 domains and 79 sources and subtasks.

	The benchmark covers answer selection, action selection, binary judgments, ordinal ratings, and multi-select decisions in a common format.

	[Interactive leaderboard / 交互榜单](https://surdai.com/zh/blog/cmdb-1500#results) · [Benchmark introduction / 基准介绍](https://surdai.com/zh/blog/cmdb-1500) · [Example questions](SAMPLES.md) · [Source catalog](SOURCES.md)

	![CMDB-1500 overview](assets/cmdb1500-overview.png)

	## Results and models

	Explore the [interactive CMDB-1500 leaderboard](https://surdai.com/zh/blog/cmdb-1500#results) on the Surd AI website. It separates text (1,200 questions), image (300 questions), and full-benchmark (1,500 questions) accuracy, with probability calibration reported separately. Text-only models do not receive an image or full-benchmark score.

	Selected Surd AI results use effort 2 and the same 1,500-question benchmark:

	\| Model \| Text accuracy ↑ \| Image accuracy ↑ \| Full-benchmark accuracy ↑ \|
	\|---\|---:\|---:\|---:\|
	\| [SPX-CD-Pro](https://surdai.com/zh/blog/simplex-cd-release) \| 79.33% \| 88.67% \| 81.20% \|
	\| [SPX-CD-Flash](https://surdai.com/zh/blog/simplex-cd-release) \| 77.58% \| 87.33% \| 79.53% \|
	\| [SPX-CD-Omni](https://huggingface.co/SurdAI/SPX-CD-Omni) \| 74.67% \| 82.67% \| 76.27% \|

	These are team-run evaluations, not official upstream leaderboard submissions. SPX-CD-Omni's reported results use the released LoRA adapter with an AWQ INT4 base; they are not measurements of the default BF16 example configuration. The [model card](https://huggingface.co/SurdAI/SPX-CD-Omni) and [evaluation settings](https://huggingface.co/SurdAI/SPX-CD-Omni/blob/main/EVALUATION.md) describe the configuration and metric scopes.

	The website's cross-model I/C comparison uses a shared text-probability cohort; it is distinct from the model card's benchmark-specific I/C scopes. Compare I/C values only within the same scoring scope. See the [SPX-CD release and API overview](https://surdai.com/zh/blog/simplex-cd-release) for Pro and Flash.

	## Coverage

	![Question counts by domain](assets/cmdb1500-coverage.png)

	\| Domain \| Questions \| Tasks \|
	\|---\|---:\|---\|
	\| Language, knowledge, and judgment \| 450 \| Science, intent classification, inference, evidence, paraphrase similarity, and response assessment \|
	\| Professional and applied decisions \| 300 \| Engineering, agents, commerce, finance, law, data, documents, product, support, and safety \|
	\| Multimodal decisions \| 300 \| Diagram understanding, visual reasoning, science questions, image classification, and visual question answering \|
	\| Games \| 100 \| Static game states and action selection across seven subtasks \|
	\| Multi-select \| 100 \| MultiRC and GoEmotions \|
	\| Safety and adversarial decisions \| 100 \| Content safety, phishing, and adversarial instruction handling \|
	\| Embodied action selection \| 50 \| ALFRED, ALFWorld, and VirtualHome \|
	\| Chinese ASR hypothesis selection \| 50 \| Selection among text transcription hypotheses \|
	\| Tool-call necessity \| 25 \| BFCL-derived judgments of whether a tool call is needed \|
	\| Spatial and complex rules \| 25 \| Spatial relations and rule-based decisions \|
	\| Total \| 1,500 \| 1,200 text-only + 300 image-based \|

	The image subset contains 322 images; some questions use more than one image.

	\| Record format \| `kind` \| Questions \| Expected prediction \|
	\|---\|---\|---:\|---\|
	\| Choice \| `choice` \| 1,077 \| One candidate key \|
	\| Ordered rating \| `score` \| 166 \| One key on an ordered scale \|
	\| Binary format \| `noul` \| 157 \| One key from the two supplied candidates \|
	\| Multi-select \| `multi_choice` \| 100 \| A set of candidate keys \|

	Counts follow the `kind` field. `choice` includes two-option questions; `multi_choice` permits one or more correct answers. Ordered ratings cover helpfulness, verbosity, similarity, hate-speech levels, and game-state assessments. Rating direction follows the question.

	## Sources

	- Language and judgment: 19 JevBench configurations, including Banking77, ARC-Challenge, BoolQ, CLINC, MASSIVE, MMLU, ChaosNLI, MNLI, SST-5, and HelpSteer2.
	- Professional decisions: 31 Atlan Decision Bench tasks covering engineering, agents, commerce, finance, law, data, documents, product, support, and safety.
	- Multimodal decisions: 30 questions each from AI2D, Fashion200k, GQA, MMMU, MMMU-Pro, MathVista, ScienceQA, TextVQA, VQAv2, and VizWiz.
	- Other domains: Open-Jev games, MultiRC, GoEmotions, ChineseHP, Aegis, PhishNChips, JevAdvBench, BFCL v3, THIS-THAT, ALFRED, ALFWorld, and VirtualHome.

	[SOURCES.md](SOURCES.md) lists all question counts, upstream links, and task definitions. Subtasks may share an upstream dataset.

	## Load the dataset

	```python
	from datasets import load_dataset

	bench = load_dataset("SurdAI/CMDB-1500", split="test")
	item = bench[0]
	model_input = {
	key: item[key]
	for key in ("state", "question", "options", "kind", "images")
	}
	```

	## Data format

	The `test` split is available as [Parquet with embedded images](data/test.parquet) and [JSONL](data/questions.jsonl). JSONL image paths are relative to the dataset directory.

	\| Fields \| Contents \|
	\|---\|---\|
	\| `state`, `state_format` \| Context as text or serialized JSON \|
	\| `question`, `options` \| Decision instruction and candidate keys/texts \|
	\| `kind`, `modality`, `images` \| Output type and image inputs \|
	\| `correct_keys`, `target_probs` \| Reference answers and optional auxiliary targets \|
	\| `id`, `source`, `category` \| Question identifier, source, and domain \|
	\| `source_id`, `original_split`, `source_metadata`, `label_origin` \| Source provenance and reference-label basis \|

	Parse `state` when `state_format` is `json`. `target_probs` and `source_metadata` are JSON-serialized strings. Candidate keys are specific to each question. Use the model-input fields shown above; reference answers and provenance are for evaluation.

	## Evaluation

	Use accuracy for categorical choices, including binary questions, exact-set match and set F1 for multi-select, and accuracy plus level error for ordinal ratings. Calibration metrics use valid model probability outputs and the stated reference labels; report the included sample count and the binning rule. Auxiliary target distributions are not model confidence predictions.

	Report domain scores and text/image results alongside an overall score, specifying the aggregation weights. Retain the full scope denominator when a native model interface cannot represent a question; report such failures separately. Text-only evaluations cover 1,200 questions and must not be presented as full 1,500-question results.

	These are fixed-input decisions. Game and embodied questions assess state/action selection; BFCL questions assess tool-call necessity; Chinese ASR questions use text hypotheses. GQA, VQAv2, TextVQA, and VizWiz use yes/no subsets. Scores describe these selected tasks rather than complete upstream benchmark evaluations.

	## Attribution and licensing

	[LICENSES.md](LICENSES.md) contains source-specific terms and required attribution, including HelpSteer2. ChaosNLI is CC BY-NC 4.0, with attribution and noncommercial conditions; ScienceQA also has noncommercial conditions. MultiNLI permits modification and redistribution for typical machine-learning uses, with underlying source terms retained. An explicit redistribution license for SST-5 has not been identified in the inspected original release documentation. MathVista permits test-set use only. Upstream terms apply to the corresponding records.

	Please cite CMDB-1500, SurdAI, together with the upstream datasets used.