diff --git a/.gitattributes b/.gitattributes index bed0738c7eeb449bca98b5d2f33c89a1ee56349a..969e1c19d55848104286d81b7b284f86447263de 100644 --- a/.gitattributes +++ b/.gitattributes @@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text # Video files - compressed *.mp4 filter=lfs diff=lfs merge=lfs -text *.webm filter=lfs diff=lfs merge=lfs -text +data/benchmark.jsonl filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..1e6e71a314c1476a304a6170c18470079594db3d --- /dev/null +++ b/.gitignore @@ -0,0 +1,4 @@ +__pycache__/ +*.py[cod] +outputs/ +config/local_config.json diff --git a/README.md b/README.md index 5cdd99a329d862aac60603d9acbefa18d054b921..f922db1144ed06250ebe864b5b1c3b7872d5e1dd 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,353 @@ --- +pretty_name: IMUG-Bench license: other -license_name: imug-license -license_link: LICENSE +license_name: imug-bench-mixed-license +license_link: LICENSE.md +language: +- en +size_categories: +- 1K + +# IMUG-Bench: Benchmarking Unified Multimodal Models on Interleaved Understanding and Generation + +**Lingyi Meng*1, Zecong Tang*†1, Haoran Li*1, Tengju Ru*1, Zhejun Cui1, Weitong Lian1, Qi Kang1, Hangshuo Cao1, Yichen Zhu1, Yechi Liu3, Kaixuan Wang2, Yu-Jie Yuan4, Chunwei Wang4, Yu Zhang‡1, Bo Dai‡2** + +1Zhejiang University   2The University of Hong Kong   3Institute of Automation, Chinese Academy of Sciences   4Huawei + +*Equal contribution   †Project leader   ‡Corresponding author + +[![Paper](https://img.shields.io/badge/Paper-arXiv%3A2606.09169-B31B1B.svg)](https://arxiv.org/abs/2606.09169) +[![GitHub](https://img.shields.io/badge/GitHub-IMUG--Bench-181717.svg)](https://github.com/ccccEsion/IMUG-Bench) +[![Dataset](https://img.shields.io/badge/Dataset-Hugging%20Face-FFD21E.svg)](#dataset) +[![License](https://img.shields.io/badge/license-mixed-lightgrey.svg)](#license) + +[Paper](https://arxiv.org/abs/2606.09169) | +[GitHub](https://github.com/ccccEsion/IMUG-Bench) | +[Dataset](#dataset) | +[Examples](#example-records) | +[Results](#results--analysis) + + + +

+ Overview of IMUG-Bench +

+ +## Overview + +IMUG-Bench is a benchmark for **interleaved multimodal understanding and generation** in multi-turn image-text dialogue. It evaluates whether unified multimodal models can answer questions, generate or edit images, preserve relevant visual context, and reason about model-specific outcomes as a conversation evolves. + +Unlike benchmarks that evaluate understanding and generation separately or rely on static, single-turn questions, IMUG-Bench evaluates both capabilities under a shared multi-turn protocol. It includes dynamic questions whose reference answers depend on the evaluated model's own earlier outputs. + +IMUG-Bench is released as an **evaluation benchmark**. It is not presented or organized as a model-training corpus. + +### Key Facts + +* **Scale:** 3,113 samples and 12,034 interaction turns. +* **Coverage:** 19 domains and 97 fine-grained tasks. +* **Dialogue length:** 2 to 6 interleaved text and image output turns per sample. +* **Task types:** static MCQ, dynamic MCQ, and image generation or editing. +* **Score analysis:** all modalities, text-only, image-only, domain, class, and turn-wise results. + +### Contributions + +* We introduce a large-scale benchmark for jointly evaluating understanding and generation in multi-turn interleaved dialogue. +* We include dynamic MCQs that resolve their reference answers from a model's earlier generated content, rather than treating every answer as fixed. +* We provide point-based image evaluation with historical visual references for instruction following and cross-turn consistency. +* We characterize generation-side exposure bias in longer interactions and study test-time scaling strategies for mitigating it. + +## Benchmark Structure + +IMUG-Bench organizes multi-turn image-text dialogue into three complementary task classes. + +### Task Classes + +* **Static Spatial:** evaluates target-element attributes, ownership, and spatial relations. +* **Temporal Causal:** evaluates implicit natural laws and common-sense causal reasoning in context. +* **Hybrid:** combines spatial and causal requirements in diverse everyday scenarios. + +### Interleaved Task Protocol + +Each sample contains an ordered sequence of turns. At every turn, the model must return either text or an image. Subsequent turns may depend on preceding user inputs and model outputs. + +The tasks of each sample must be processed sequentially. After every turn, the model's actual response must be appended to the dialogue history before the next user turn is evaluated. + +The expected dialogue order is: + +```text +system +→ user_1 +→ assistant_1 +→ user_2 +→ assistant_2 +→ ... +→ current_user +``` + +Text and image elements within a message must retain their original order. + +| Task Type | Model Output | Evaluation | +| ---------------- | ------------------------- | ------------------------------------------------------------------------------------------------------------------- | +| Static MCQ | Answer letters | Direct answer and format scoring | +| Dynamic MCQ | Answer letters | A judge first resolves the context-dependent reference answer from referenced prior turns, then applies MCQ scoring | +| Image Generation | Generated or edited image | A judge scores each evaluation point from 0 to 5, using historical image references when needed | + +For an image turn with (N) evaluation points, the normalized turn score is the mean point score divided by 5. Text scores and image scores are both converted to percentages in the final summary. + +## Dataset + +The released data contains benchmark metadata, input images, reference links for image and dynamic evaluation, and grid ground truth for deterministic geometric-coloring scoring. + +### Repository Layout + +```text +IMUG-Bench/ +├── README.md +├── LICENSE +├── assets/ +│ ├── overview.png +│ ├── exposure_bias.svg +│ └── distribution.svg +├── data/ +│ ├── benchmark.jsonl +│ ├── image_references.jsonl +│ ├── dynamic_references.jsonl +│ ├── images/ +│ └── grid_gt/ +└── example.json +``` + +The files have the following purposes: + +```text +data/benchmark.jsonl + Complete benchmark samples and ordered turn metadata. + +data/images/ + Input images referenced by benchmark records. + +data/image_references.jsonl + Historical or reference-image dependencies used for image evaluation. + +data/dynamic_references.jsonl + Referenced turns used to resolve dynamic MCQ answers. + +data/grid_gt/ + Ground truth for deterministic geometric-coloring tasks. + +example.json + Five representative benchmark records. +``` + +### Data Format + +`data/benchmark.jsonl` uses the JSON Lines format. Each line represents one complete multi-turn sample. + +Every record contains: + +| Field | Description | +| --------------- | ----------------------------------------- | +| `sample_id` | Unique identifier of the benchmark sample | +| `domain` | High-level task domain | +| `subdomain` | Fine-grained task category | +| `system_prompt` | System instruction used for the dialogue | +| `tasks` | Ordered list of interaction turns | + +Each item in `tasks` contains: + +| Field | Description | +| ------------------------------- | -------------------------------------------------- | +| `turn` | Turn index within the dialogue | +| `modality` | Expected model output modality | +| `input` | Ordered user text and image inputs | +| `answer` or evaluation metadata | Reference information used by the scoring pipeline | + +The order of elements in `input` is meaningful and must be preserved. Paths in the benchmark and reference files should be resolved relative to the root of the dataset repository. + +### Example Record + +A simplified record is shown below: + +```json +{ + "sample_id": "example_001", + "domain": "Static Spatial", + "subdomain": "Attribute Reasoning", + "system_prompt": "You are a multimodal assistant.", + "tasks": [ + { + "turn": 1, + "modality": "text", + "input": [ + { + "image": "example_input.png" + }, + { + "text": "Which object is located to the left of the blue cube?" + } + ] + }, + { + "turn": 2, + "modality": "image", + "input": [ + { + "text": "Generate the next scene while preserving the previous spatial layout." + } + ] + } + ] +} +``` + +### Example Records + +[`example.json`](example.json) contains the records at JSONL lines 1, 710, 1551, 2808, and 2788, using one-based line numbering. + +It is provided as a small, directly readable introduction to the record structure. The complete benchmark is available in [`data/benchmark.jsonl`](data/benchmark.jsonl). + +### Download + +The complete repository can be downloaded with the Hugging Face CLI: + +```bash +hf download \ + --repo-type dataset \ + --local-dir ./IMUG-Bench +``` + +Replace `` with the final Hugging Face repository identifier, for example: + +```text +organization-name/IMUG-Bench +``` + +## Evaluation + +The official evaluation client, model-server protocol, scoring scripts, and reference server implementations are maintained in the project GitHub repository: + +https://github.com/ccccEsion/IMUG-Bench + +The evaluation client processes every sample turn by turn. For each request, it sends the complete structured dialogue history together with the current user turn. Model-specific servers are responsible for converting this standard history format into the input representation required by each model. + +The expected request format is: + +```json +{ + "history": [ + { + "role": "system", + "content": [ + { + "type": "text", + "text": "You are a multimodal assistant." + } + ] + }, + { + "role": "user", + "content": [ + { + "type": "image", + "image": "" + }, + { + "type": "text", + "text": "Describe the image." + } + ] + } + ], + "output_mode": "text_only" +} +``` + +Evaluation code and additional model-server demonstrations may be released or updated independently of the dataset files. + +## Results & Analysis + +The benchmark supports separate analysis of understanding and generation, together with domain-level and turn-wise reporting. The paper finds that image-generation scores can decline as dialogue context grows, exposing the difficulty of maintaining requirements and visual consistency across turns. + +

+ Image-modality score trends and a multi-turn example +

+ +

Image-modality trends across dialogue turns and a representative multi-turn interaction.

+ +IMUG-Bench spans three classes and 19 domains. The benchmark composition is summarized below. Detailed per-domain, per-class, and per-turn reports are described in the paper and supported by the evaluation code in the GitHub repository. + +

+ IMUG-Bench class and domain distribution +

+ +

Class and domain composition of IMUG-Bench.

+ +## Intended Use + +IMUG-Bench is intended for: + +* evaluating unified multimodal models; +* studying multi-turn image-text understanding and generation; +* measuring instruction following and cross-turn visual consistency; +* comparing model performance under a common evaluation protocol; +* analyzing error accumulation and exposure bias in multimodal dialogue. + +The dataset is not presented as a training corpus. Users should avoid extracting third-party images for purposes unrelated to the benchmark. + +## Limitations + +* Some dynamic questions depend on model-generated outputs and therefore require context-dependent reference resolution. +* Image evaluation may depend on multimodal judges and should be interpreted together with the evaluation configuration. +* Performance may vary according to model-specific dialogue templates, image preprocessing, decoding settings, and generation seeds. +* Third-party images remain subject to their respective source licenses and should not be redistributed independently from the benchmark. + +## Citation + +```bibtex +@article{meng2026imugbench, + title={IMUG-Bench: Benchmarking Unified Multimodal Models on Interleaved Understanding and Generation}, + author={Meng, Lingyi and Tang, Zecong and Li, Haoran and Ru, Tengju and Cui, Zhejun and others}, + journal={arXiv preprint arXiv:2606.09169}, + year={2026} +} +``` + +## License + +The benchmark text, annotations, metadata, photographs, and AI-generated images created by the IMUG-Bench authors are released under the **Creative Commons Attribution 4.0 International License (CC BY 4.0)**. + +Third-party images were collected from publicly available sources under licenses permitting their use in derivative research materials at the time of collection. These images are included solely as integral components of the annotated IMUG-Bench evaluation tasks and are not relicensed by the IMUG-Bench authors. + +Users should not extract or redistribute individual third-party images separately from the benchmark. + +Because the repository contains materials subject to different licensing terms, its Hugging Face metadata is marked as `license: other`. + +## Contact + +For questions about the benchmark, dataset, or evaluation code, please open an issue in the project GitHub repository: + +https://github.com/ccccEsion/IMUG-Bench diff --git a/assets/distribution.svg b/assets/distribution.svg new file mode 100644 index 0000000000000000000000000000000000000000..e28acbc6bad090d4af7d21507d4bbc0f4733baac --- /dev/null +++ b/assets/distribution.svg @@ -0,0 +1,913 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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