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.gitattributes

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LICENSE

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+AV-QuantBench Dataset License
+
+Unless otherwise stated, the dataset artifacts in this repository, including annotations, metadata, generated media, and documentation, are released under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.
+
+You may share and adapt the material for non-commercial research and educational purposes, provided that appropriate credit is given. Commercial use is not permitted without separate written permission from the copyright holders.
+
+For the full license text, see: https://creativecommons.org/licenses/by-nc/4.0/

README.md

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+---
+pretty_name: AV-QuantBench
+license: cc-by-nc-4.0
+task_categories:
+- question-answering
+- video-text-to-text
+tags:
+- multimodal
+- audio
+- video
+- time-series
+- synthetic
+- benchmark
+size_categories:
+- 10K<n<100K
+annotations_creators:
+- machine-generated
+language_creators:
+- machine-generated
+language:
+- en
+multilinguality:
+- monolingual
+source_datasets:
+- original
+---
+
+# AV-QuantBench
+
+AV-QuantBench is a procedural audio-visual benchmark for evaluating multimodal foundation models on abstract temporal reasoning, cross-modal conflict detection, and synchronized data interpretation across finance, medical, and industrial domains.
+
+This Hugging Face dataset repository is structured to support a benchmark-style release. It contains:
+
+- split metadata in JSONL format,
+- question-answer annotations,
+- sample video/audio assets,
+- manifest files by domain,
+- and documentation for schema and responsible use.
+
+The sample subset included here is upload-ready and demonstrates the expected repository structure. It can be extended to the full release by replacing the sample media and JSONL files with the complete benchmark assets generated by the AV-QuantBench pipeline.
+
+## Dataset Summary
+
+AV-QuantBench converts time-series signals into synchronized visual topology and acoustic momentum. Each sample is paired with machine-generated QA derived from deterministic state-machine triggers. The benchmark is designed to evaluate whether a model can jointly reason over audio and video when the two modalities either align or intentionally diverge.
+
+### Supported tasks
+
+- Cross-modal conflict detection
+- Audio-visual temporal reasoning
+- Multimodal question answering on synthetic data videos
+- Robustness evaluation under counterfactual splicing
+
+### Modalities
+
+- Video (`.mp4`)
+- Audio (`.wav`)
+- Frame previews (`.png`)
+- Structured annotations (`.jsonl`)
+
+### Domains
+
+- Finance
+- Medical monitoring
+- Industrial IoT
+
+## Repository Structure
+
+```text
+AV-QuantBench-HF-Dataset/
+├── README.md
+├── LICENSE
+├── .gitattributes
+├── dataset_infos.json
+├── metadata/
+│   ├── train.jsonl
+│   ├── val.jsonl
+│   └── test.jsonl
+├── qa/
+│   ├── train.jsonl
+│   ├── val.jsonl
+│   └── test.jsonl
+├── manifests/
+│   ├── finance.jsonl
+│   ├── medical.jsonl
+│   └── iiot.jsonl
+├── samples/
+│   ├── finance/
+│   │   ├── videos/
+│   │   ├── audio/
+│   │   ├── qa/
+│   │   └── metadata/
+│   ├── medical/
+│   │   ├── videos/
+│   │   ├── audio/
+│   │   ├── qa/
+│   │   └── metadata/
+│   └── iiot/
+│       ├── videos/
+│       ├── audio/
+│       ├── qa/
+│       └── metadata/
+└── docs/
+    ├── schema.md
+    └── responsible_use.md
+```
+
+## Data Fields
+
+Each record in `metadata/*.jsonl` contains:
+
+- `id`
+- `split`
+- `domain`
+- `video`
+- `audio`
+- `frame_preview`
+- `duration_sec`
+- `fps`
+- `sample_rate`
+- `task_type`
+- `question`
+- `ground_truth`
+- `has_conflict`
+- `source_mode`
+- `seed`
+- `algorithmic_metadata`
+
+See `docs/schema.md` for details.
+
+## Splits
+
+This upload-ready package includes one example for each split:
+
+- `train`: finance
+- `val`: medical
+- `test`: iiot
+
+In the full release, these files should be replaced by the complete split manifests and associated media.
+
+## Example Usage
+
+```python
+import json
+from pathlib import Path
+
+root = Path('.')
+with open(root / 'metadata' / 'train.jsonl', 'r', encoding='utf-8') as f:
+    sample = json.loads(f.readline())
+
+print(sample['domain'])
+print(sample['question'])
+print(sample['video'])
+```
+
+## Generation Pipeline
+
+The full AV-QuantBench generator creates each sample using the following stages:
+
+1. substrate synthesis or ingestion,
+2. anti-leakage visual rendering,
+3. acoustic mapping and sonification,
+4. deterministic audio-video synchronization,
+5. state-machine-based QA generation.
+
+## Licensing
+
+Unless otherwise stated, the dataset artifacts in this repository are released under **CC BY-NC 4.0**. This includes metadata, annotations, documentation, and generated media distributed here.
+
+## Limitations
+
+- The package currently includes a minimal sample subset for upload readiness.
+- Full benchmark scale requires exporting the complete media and annotation assets from the generator repository.
+- Synthetic audio-visual data should not be interpreted as real financial or medical advice.
+
+## Responsible Use
+
+Please see `docs/responsible_use.md`.
+
+## Citation
+
+If you use AV-QuantBench, please cite the accompanying paper:
+
+```bibtex
+@article{gu2026avquantbench,
+  title={AV-QuantBench: An Audio-Visual Data Video Benchmark for Foundation Models in Complex Temporal Reasoning},
+  author={Gu, Fengchen and Ren, Xiaotian and Jiang, Zhengyong and Garc{'i}a-Fern{'a}ndez, {'A}ngel F. and Su, Jionglong and Li, Huakang},
+  journal={arXiv preprint arXiv:XXXX.XXXXX},
+  year={2026}
+}
+```
+
+
+## Sample Layout
+
+The `samples/` directory mirrors the domain-first output layout produced by the AV-QuantBench generator, so newly generated data can be copied into the repository with minimal restructuring:
+
+- `samples/finance/videos`, `samples/finance/audio`, `samples/finance/qa`, `samples/finance/metadata`
+- `samples/medical/videos`, `samples/medical/audio`, `samples/medical/qa`, `samples/medical/metadata`
+- `samples/iiot/videos`, `samples/iiot/audio`, `samples/iiot/qa`, `samples/iiot/metadata`
+
+This layout is intentionally aligned with the local `all_outputs`-style export structure for easier dataset refresh and replacement.

dataset_infos.json

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+{
+  "default": {
+    "description": "Upload-ready Hugging Face dataset package for AV-QuantBench with JSONL metadata, QA annotations, and sample audio-video assets.",
+    "citation": "@article{gu2026avquantbench, title={AV-QuantBench: An Audio-Visual Data Video Benchmark for Foundation Models in Complex Temporal Reasoning}, author={Gu, Fengchen and Ren, Xiaotian and Jiang, Zhengyong and Garc{\\'i}a-Fern{\\'a}ndez, {\\'A}ngel F. and Su, Jionglong and Li, Huakang}, journal={arXiv preprint arXiv:XXXX.XXXXX}, year={2026}}",
+    "homepage": "https://github.com/your-org/AV-QuantBench",
+    "license": "CC BY-NC 4.0",
+    "features": {
+      "id": {
+        "dtype": "string",
+        "_type": "Value"
+      },
+      "split": {
+        "dtype": "string",
+        "_type": "Value"
+      },
+      "domain": {
+        "dtype": "string",
+        "_type": "Value"
+      },
+      "video": {
+        "dtype": "string",
+        "_type": "Value"
+      },
+      "audio": {
+        "dtype": "string",
+        "_type": "Value"
+      },
+      "frame_preview": {
+        "dtype": "string",
+        "_type": "Value"
+      },
+      "duration_sec": {
+        "dtype": "int64",
+        "_type": "Value"
+      },
+      "fps": {
+        "dtype": "int64",
+        "_type": "Value"
+      },
+      "sample_rate": {
+        "dtype": "int64",
+        "_type": "Value"
+      },
+      "task_type": {
+        "dtype": "string",
+        "_type": "Value"
+      },
+      "question": {
+        "dtype": "string",
+        "_type": "Value"
+      },
+      "ground_truth": {
+        "dtype": "string",
+        "_type": "Value"
+      },
+      "has_conflict": {
+        "dtype": "bool",
+        "_type": "Value"
+      },
+      "source_mode": {
+        "dtype": "string",
+        "_type": "Value"
+      },
+      "seed": {
+        "dtype": "int64",
+        "_type": "Value"
+      },
+      "metadata_file": {
+        "dtype": "string",
+        "_type": "Value"
+      },
+      "qa_file": {
+        "dtype": "string",
+        "_type": "Value"
+      },
+      "algorithmic_metadata": {
+        "_type": "string"
+      }
+    },
+    "builder_name": "json",
+    "dataset_name": "AV-QuantBench",
+    "config_name": "default",
+    "splits": {
+      "train": {
+        "name": "train",
+        "num_examples": 1,
+        "dataset_name": "AV-QuantBench",
+        "num_bytes": 929
+      },
+      "val": {
+        "name": "val",
+        "num_examples": 1,
+        "dataset_name": "AV-QuantBench",
+        "num_bytes": 916
+      },
+      "test": {
+        "name": "test",
+        "num_examples": 1,
+        "dataset_name": "AV-QuantBench",
+        "num_bytes": 805
+      }
+    },
+    "version": {
+      "version_str": "1.0.0",
+      "major": 1,
+      "minor": 0,
+      "patch": 0
+    }
+  }
+}

docs/responsible_use.md

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+# Responsible Use
+
+AV-QuantBench is intended for research on multimodal temporal reasoning, robustness, and evaluation.
+
+Recommended uses:
+- Benchmarking multimodal foundation models.
+- Studying cross-modal conflict detection.
+- Reproducing procedural audio-visual synthesis pipelines.
+- Research on safer AI for high-stakes monitoring scenarios.
+
+Not recommended uses:
+- Deploying generated samples as real financial or medical advice.
+- Presenting synthetic clips as authentic monitoring data.
+- Using the dataset to deceive human operators or downstream systems.
+
+The dataset contains synthetic or de-identified signals and excludes personally identifiable information.

docs/schema.md

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+# Schema
+
+Each JSONL record in `metadata/` contains the following fields:
+
+- `id`: unique sample identifier.
+- `split`: dataset split (`train`, `val`, `test`).
+- `domain`: one of `finance`, `medical`, or `iiot`.
+- `video`: relative path to the MP4 file.
+- `audio`: relative path to the WAV file.
+- `frame_preview`: relative path to a representative PNG frame.
+- `duration_sec`: clip duration in seconds.
+- `fps`: video frame rate.
+- `sample_rate`: audio sampling rate.
+- `task_type`: benchmark task family.
+- `question`: generated question.
+- `ground_truth`: deterministic answer.
+- `has_conflict`: whether a counterfactual cross-modal conflict was injected.
+- `source_mode`: substrate source (`synthetic` or `yfinance` for finance in the full generator).
+- `seed`: random seed used by procedural generation.
+- `metadata_file`: relative pointer to the split metadata file.
+- `qa_file`: relative pointer to the split QA file.
+- `algorithmic_metadata`: domain-specific gradients and trigger values used by the state machine.
+
+The `qa/` JSONL files mirror the QA-specific subset of this information.

manifests/finance.jsonl

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+{"id": "avqb-train-finance-0001", "split": "train", "domain": "finance", "video": "samples/videos/finance/AV_QuantBench_Finance_Sample.mp4", "audio": "samples/audio/finance/AV_QuantBench_Finance_Sample.wav", "frame_preview": "samples/frames/finance/AV_QuantBench_Finance_Sample_mid.png", "duration_sec": 15, "fps": 30, "sample_rate": 44100, "task_type": "Cross-Modal Conflict", "question": "Between t=10s and t=15s, does the acoustic momentum support the visual upward trend?", "ground_truth": "No. While the visual spatial topology shows higher highs, the acoustic tempo decelerates because the underlying volume drops, indicating a bearish cross-modal divergence.", "has_conflict": true, "source_mode": "synthetic", "seed": 42, "metadata_file": "metadata/train.jsonl", "qa_file": "qa/train.jsonl", "algorithmic_metadata": {"price_gradient": 19.53789955610945, "volume_gradient": -47.92301604939097, "conflict_detected": true}}

manifests/iiot.jsonl

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+{"id": "avqb-test-iiot-0001", "split": "test", "domain": "iiot", "video": "samples/videos/iiot/AV_QuantBench_IIoT_Sample.mp4", "audio": "samples/audio/iiot/AV_QuantBench_IIoT_Sample.wav", "frame_preview": "samples/frames/iiot/AV_QuantBench_IIoT_Sample_mid.png", "duration_sec": 15, "fps": 30, "sample_rate": 44100, "task_type": "Cross-Modal Conflict", "question": "Between t=10s and t=15s, does the acoustic momentum match the visually stable machine pattern?", "ground_truth": "Yes. The visual and acoustic indicators remain aligned.", "has_conflict": false, "source_mode": "synthetic", "seed": 42, "metadata_file": "metadata/test.jsonl", "qa_file": "qa/test.jsonl", "algorithmic_metadata": {"vibration_gradient": -0.441661218487967, "friction_gradient": 1.8600040381837637, "conflict_detected": false}}

manifests/medical.jsonl

@@ -0,0 +1 @@
+{"id": "avqb-val-medical-0001", "split": "val", "domain": "medical", "video": "samples/videos/medical/AV_QuantBench_Medical_Sample.mp4", "audio": "samples/audio/medical/AV_QuantBench_Medical_Sample.wav", "frame_preview": "samples/frames/medical/AV_QuantBench_Medical_Sample_mid.png", "duration_sec": 15, "fps": 30, "sample_rate": 44100, "task_type": "Cross-Modal Conflict", "question": "Between t=10s and t=15s, does the acoustic tempo support the visually active medical signal?", "ground_truth": "No. The visual waveform remains active, but the acoustic tempo slows, suggesting a cross-modal mismatch consistent with deteriorating vital momentum.", "has_conflict": true, "source_mode": "synthetic", "seed": 42, "metadata_file": "metadata/val.jsonl", "qa_file": "qa/val.jsonl", "algorithmic_metadata": {"signal_gradient": -0.07117362667543287, "heart_rate_gradient": -33.11302142454403, "conflict_detected": true}}

metadata/test.jsonl

@@ -0,0 +1 @@
+{"id": "avqb-test-iiot-0001", "split": "test", "domain": "iiot", "video": "samples/videos/iiot/AV_QuantBench_IIoT_Sample.mp4", "audio": "samples/audio/iiot/AV_QuantBench_IIoT_Sample.wav", "frame_preview": "samples/frames/iiot/AV_QuantBench_IIoT_Sample_mid.png", "duration_sec": 15, "fps": 30, "sample_rate": 44100, "task_type": "Cross-Modal Conflict", "question": "Between t=10s and t=15s, does the acoustic momentum match the visually stable machine pattern?", "ground_truth": "Yes. The visual and acoustic indicators remain aligned.", "has_conflict": false, "source_mode": "synthetic", "seed": 42, "metadata_file": "metadata/test.jsonl", "qa_file": "qa/test.jsonl", "algorithmic_metadata": {"vibration_gradient": -0.441661218487967, "friction_gradient": 1.8600040381837637, "conflict_detected": false}}

metadata/train.jsonl

@@ -0,0 +1 @@
+{"id": "avqb-train-finance-0001", "split": "train", "domain": "finance", "video": "samples/videos/finance/AV_QuantBench_Finance_Sample.mp4", "audio": "samples/audio/finance/AV_QuantBench_Finance_Sample.wav", "frame_preview": "samples/frames/finance/AV_QuantBench_Finance_Sample_mid.png", "duration_sec": 15, "fps": 30, "sample_rate": 44100, "task_type": "Cross-Modal Conflict", "question": "Between t=10s and t=15s, does the acoustic momentum support the visual upward trend?", "ground_truth": "No. While the visual spatial topology shows higher highs, the acoustic tempo decelerates because the underlying volume drops, indicating a bearish cross-modal divergence.", "has_conflict": true, "source_mode": "synthetic", "seed": 42, "metadata_file": "metadata/train.jsonl", "qa_file": "qa/train.jsonl", "algorithmic_metadata": {"price_gradient": 19.53789955610945, "volume_gradient": -47.92301604939097, "conflict_detected": true}}

metadata/val.jsonl

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+{"id": "avqb-val-medical-0001", "split": "val", "domain": "medical", "video": "samples/videos/medical/AV_QuantBench_Medical_Sample.mp4", "audio": "samples/audio/medical/AV_QuantBench_Medical_Sample.wav", "frame_preview": "samples/frames/medical/AV_QuantBench_Medical_Sample_mid.png", "duration_sec": 15, "fps": 30, "sample_rate": 44100, "task_type": "Cross-Modal Conflict", "question": "Between t=10s and t=15s, does the acoustic tempo support the visually active medical signal?", "ground_truth": "No. The visual waveform remains active, but the acoustic tempo slows, suggesting a cross-modal mismatch consistent with deteriorating vital momentum.", "has_conflict": true, "source_mode": "synthetic", "seed": 42, "metadata_file": "metadata/val.jsonl", "qa_file": "qa/val.jsonl", "algorithmic_metadata": {"signal_gradient": -0.07117362667543287, "heart_rate_gradient": -33.11302142454403, "conflict_detected": true}}

qa/test.jsonl

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+{"id": "avqb-test-iiot-0001", "split": "test", "domain": "iiot", "video": "samples/videos/iiot/AV_QuantBench_IIoT_Sample.mp4", "audio": "samples/audio/iiot/AV_QuantBench_IIoT_Sample.wav", "task_type": "Cross-Modal Conflict", "question": "Between t=10s and t=15s, does the acoustic momentum match the visually stable machine pattern?", "ground_truth": "Yes. The visual and acoustic indicators remain aligned.", "algorithmic_metadata": {"vibration_gradient": -0.441661218487967, "friction_gradient": 1.8600040381837637, "conflict_detected": false}}

qa/train.jsonl

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+{"id": "avqb-train-finance-0001", "split": "train", "domain": "finance", "video": "samples/videos/finance/AV_QuantBench_Finance_Sample.mp4", "audio": "samples/audio/finance/AV_QuantBench_Finance_Sample.wav", "task_type": "Cross-Modal Conflict", "question": "Between t=10s and t=15s, does the acoustic momentum support the visual upward trend?", "ground_truth": "No. While the visual spatial topology shows higher highs, the acoustic tempo decelerates because the underlying volume drops, indicating a bearish cross-modal divergence.", "algorithmic_metadata": {"price_gradient": 19.53789955610945, "volume_gradient": -47.92301604939097, "conflict_detected": true}}

qa/val.jsonl

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+{"id": "avqb-val-medical-0001", "split": "val", "domain": "medical", "video": "samples/videos/medical/AV_QuantBench_Medical_Sample.mp4", "audio": "samples/audio/medical/AV_QuantBench_Medical_Sample.wav", "task_type": "Cross-Modal Conflict", "question": "Between t=10s and t=15s, does the acoustic tempo support the visually active medical signal?", "ground_truth": "No. The visual waveform remains active, but the acoustic tempo slows, suggesting a cross-modal mismatch consistent with deteriorating vital momentum.", "algorithmic_metadata": {"signal_gradient": -0.07117362667543287, "heart_rate_gradient": -33.11302142454403, "conflict_detected": true}}

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