| --- |
| license: apache-2.0 |
| language: |
| - en |
| task_categories: |
| - multiple-choice |
| tags: |
| - omni-modal |
| - multimodal |
| - robustness |
| - structural-corruption |
| - video |
| - audio |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: baseline |
| data_files: data/dataset_baseline.jsonl |
| - config_name: single |
| data_files: data/dataset_single.jsonl |
| - config_name: combined |
| data_files: data/dataset_combined.jsonl |
| --- |
| |
| # SCEval — Modality Fault Lines |
|
|
| Data for **Modality Fault Lines: Structural Corruptions Reveal Fragile Omni-Modal Reasoning** (Findings of EMNLP 2026). |
|
|
| SCEval is a human-verified benchmark for omni-modal robustness. Text, vision, and audio all remain |
| **present**, but controlled corruptions make the evidence inside a channel unreliable. Each corrupted |
| item is paired with its clean counterpart at the example level, so clean-to-corrupted comparisons are |
| made on the same underlying question, answer options, and reference label. |
|
|
| Code, full documentation, and the evaluation protocol: |
| <https://github.com/Zhaolu-K/ModalityFaultLines-SCEval> |
|
|
| ## What is in this repository |
|
|
| ``` |
| data/ |
| ├─ dataset_baseline.jsonl 273 clean tri-modal questions (the baseline reference) |
| ├─ dataset_single.jsonl 29,144 single-modality corruptions (text / vision / audio) |
| ├─ dataset_combined.jsonl 17,875 bimodal + trimodal joint corruptions |
| └─ dataset_*_preview.json first 30 rows of each file, indented and field-trimmed |
| media/ |
| ├─ Social_IQ_single_question/ video + audio, 100 base examples |
| ├─ omnibench/ image + audio, 77 base examples |
| └─ valor/ video + audio, 96 base examples |
| ``` |
|
|
| `data/*.jsonl` is one question per line and carries everything needed to evaluate: the question text, |
| the answer options, the reference answer, the corruption metadata, and direct URLs to the media in |
| `media/`. The media tree mirrors |
| `<source>/<sample_id>/<modality>/<operator>/<severity>/<variant>/<file>`. |
|
|
| | Split | Rows | Contents | |
| |---|---|---| |
| | `baseline` | 273 | Clean examples. Every corruption is compared against these. | |
| | `single` | 29,144 | One modality corrupted at a time, 14 operators × severity 10/30/50/70. | |
| | `combined` | 17,875 | 18 bimodal + 11 trimodal preset conditions. | |
|
|
| ## Loading |
|
|
| Metadata only (fast — media stays remote): |
|
|
| ```python |
| from datasets import load_dataset |
| |
| baseline = load_dataset("KZL96/ModalityFaultLines-SCEval", "baseline", split="train") |
| single = load_dataset("KZL96/ModalityFaultLines-SCEval", "single", split="train") |
| |
| row = single[0] |
| print(row["question"], row["options"], row["gold_key"]) |
| print(row["visual_url"], row["audio_url"]) # resolve directly over HTTPS |
| ``` |
|
|
| Media, all of it (about 112 GB — pick a subfolder if you do not need everything): |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| |
| snapshot_download( |
| repo_id="KZL96/ModalityFaultLines-SCEval", |
| repo_type="dataset", |
| allow_patterns=["media/omnibench/**"], # drop this argument for the full tree |
| local_dir="./SCEval", |
| ) |
| ``` |
|
|
| ## Field reference |
|
|
| | Field | Meaning | |
| |---|---| |
| | `row_id`, `question_id`, `sample_id` | Identifiers. `sample_id` ties a corrupted row to its clean counterpart. | |
| | `dataset` | Source benchmark: `Social_IQ_single_question`, `omnibench`, or `valor`. | |
| | `question`, `original_question` | Question text after / before text corruption (identical unless the text channel was corrupted). | |
| | `options`, `gold_key`, `gold_answer_raw` | Answer options `A0`–`A3`, the correct key, and its full text. | |
| | `visual_url`, `audio_url` | Media actually shown to the model for this row. | |
| | `original_visual_url`, `original_audio_url` | The clean media for the same example. | |
| | `corrupted_url` | Whichever channel this row corrupts; equals `visual_url` or `audio_url`. | |
| | `visual_kind` | `video` or `image`. | |
| | `corruption_modality` | Which channel is corrupted (`文本` text / `视觉` vision / `音频` audio). | |
| | `corruption_group`, `corruption_type` | Operator family and the specific operator. | |
| | `severity_base` | `10`, `30`, `50`, or `70`. | |
| | `severity_variant` | Distinguishes multiple random realizations at one severity. | |
| | `is_baseline` | `true` only in the `baseline` split. | |
| | `condition_id`, `condition_label` | `combined` split only: which joint-corruption preset. | |
| | `human_understandable`, `human_valid`, `human_must_three_modal` | Human-verification outcomes (`是` yes / `否` no). | |
|
|
| Two conventions matter when reporting numbers. Corrupted rows are only interpretable against their |
| same-`sample_id` baseline row, so the `baseline` split should be evaluated alongside any corruption |
| split. And for stochastic operators, the multiple `severity_variant` realizations within one |
| (model, modality, operator, severity) bucket are aggregated **worst-case**: if any variant is answered |
| incorrectly, the bucket counts as incorrect. |
|
|
| ## Licensing and source media |
|
|
| This repository is released under Apache-2.0. The corrupted media is derived from three existing |
| benchmarks, and the underlying source material remains subject to their original licenses and terms: |
| [Social-IQ](https://github.com/A2Zadeh/Social-IQ), [OmniBench](https://m-a-p.ai/OmniBench/), and |
| [VALOR](https://github.com/TXH-mercury/VALOR). Please cite those datasets in addition to this work. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{sceval2026, |
| title = {Modality Fault Lines: Structural Corruptions Reveal Fragile Omni-Modal Reasoning}, |
| booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2026}, |
| year = {2026} |
| } |
| ``` |
|
|
