| --- |
| pretty_name: Co-Design Bench — Public ME-96 |
| tags: |
| - arxiv:2610.35138 |
| - benchmark |
| - multimodal |
| - coding-agents |
| - web-development |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: tasks |
| default: true |
| data_files: |
| - split: test |
| path: data/tasks.parquet |
| - config_name: rubrics |
| data_files: |
| - split: test |
| path: data/rubrics.parquet |
| --- |
| |
| <p align="center"> |
| <img src="assets/logo.png" alt="Co-Design Bench mascot" width="108" /> |
| </p> |
|
|
| <h1 align="center">Co-Design Bench</h1> |
|
|
| <p align="center"><strong>Multimodal Collaborative Coding Evaluation for Agentic Models</strong></p> |
|
|
| <p align="center"> |
| <a href="https://codesignbench.github.io/">Project website</a> · |
| <a href="https://arxiv.org/abs/2610.35138">Paper</a> · |
| <a href="solve_norubric.tar.gz">Solver inputs</a> · |
| <a href="eval_withrubric.tar.gz">Evaluation inputs</a> · |
| <a href="#citation">Citation</a> |
| </p> |
|
|
|  |
|
|
| Co-Design Bench evaluates whether coding agents can translate real users' |
| multimodal requests into complete, working revisions of existing applications. |
| Each task provides a runnable starting application, a user request, an annotated |
| screenshot, and any additional media. Fixed expert-authored acceptance criteria |
| assess the requested changes and preservation constraints using source and |
| browser evidence. |
|
|
| ## Dataset scope |
|
|
| This release contains **96 Multimodal Editing (ME) tasks and 521 acceptance |
| criteria**. Repository Editing (RE) tasks are outside this release. |
|
|
| The paper evaluates 273 tasks across 100 ME and 173 RE tasks, with 2,331 criteria. |
| The overview illustrates that full evaluation; this dataset is its public ME |
| subset. Results on these 96 tasks should identify that scope explicitly and be |
| reported separately from the paper's full-set leaderboard. |
|
|
| | Primary attachment | Tasks | |
| | --- | ---: | |
| | Audio | 32 | |
| | Video | 24 | |
| | Document | 16 | |
| | Image | 4 | |
| | No extra attachment | 20 | |
| | Total | 96 | |
|
|
| Tasks without an extra attachment still include the request, starting |
| application, and annotated screenshot. |
|
|
| ## Files |
|
|
| | File | Contents | |
| | --- | --- | |
| | [Solver archive](solve_norubric.tar.gz) | Applications, user requests, annotations, and attachments for all 96 tasks, without rubrics. | |
| | [Evaluation archive](eval_withrubric.tar.gz) | The same task inputs, with each task's fixed `rubric.json`. | |
| | [Task index](data/tasks.parquet) | 96 task records, including annotation previews; available as the default `tasks` configuration. | |
| | [Rubric index](data/rubrics.parquet) | 521 criterion records; available as the evaluator-only `rubrics` configuration. | |
| | [Rubric exports](rubrics/) | CSV, JSONL, and grouped JSON exports of the fixed public criteria, with their case list. | |
| | [Original rubric package](CoDesignBench-ME-96-Rubrics-521.zip) | Original per-task rubric JSON files for all 96 tasks. | |
| | [Manifest](manifest.json) | Public scope, case IDs, counts, and archive checksums. | |
| | [SHA-256 checksums](SHA256SUMS) | File integrity checks for this release. | |
| | [Citation metadata](CITATION.cff) | Paper authors and arXiv citation. | |
|
|
| The Parquet files provide indexes for browsing and analysis. Download an archive |
| to obtain the application source and media required to run the benchmark. |
| The `rubrics/` directory contains `rubrics_521.csv`, `rubrics_521.jsonl`, and |
| `rubrics_by_task.json`, plus the fixed case list. The original 96 individual |
| `rubric.json` files are preserved in the evaluation archive and the original |
| rubric ZIP above. |
|
|
| ## Data fields |
|
|
| Both configurations use a single `test` split; these are benchmark evaluation |
| tasks. The `tasks` configuration contains solver inputs and metadata: |
|
|
| | Field | Type | Description | |
| | --- | --- | --- | |
| | `case_id` | string | Original task ID. | |
| | `benchmark_track` | string | `ME` for every task in this release. | |
| | `request` | string | Original `user_queries.json[0].text`, preserved verbatim. | |
| | `query_path` | string | Path to `user_queries.json` within an extracted archive. | |
| | `project_path` | string | Path to the starting application within an extracted archive. | |
| | `annotation_path` | string | Original annotation path under `cases/<case_id>/project/_annotations/`. | |
| | `annotation_image` | Image | Original annotation PNG embedded for dataset viewer previews. | |
| | `attachment_paths` | list of strings | Additional attachment paths within an extracted archive. | |
|
|
| All paths are relative to the archive's extraction directory. Additional |
| attachments remain inside the archives; the annotation preview does not replace |
| the full task inputs. The `tasks` index contains no acceptance criteria. |
|
|
| The `rubrics` configuration contains one row per evaluation criterion: |
|
|
| | Field | Type | Description | |
| | --- | --- | --- | |
| | `rubric_id` | string | Criterion record ID. | |
| | `case_id` | string | Original task ID, matching the task index. | |
| | `task_id` | string | Original task identifier from the rubric. | |
| | `benchmark_track` | string | `ME`. | |
| | `criterion_index` | int64 | Criterion position within its task, starting at 1. | |
| | `criterion` | string | Original criterion text, preserved verbatim. | |
|
|
| Rubric rows are evaluator inputs. Keep them separate from the solver's context. |
|
|
| ## Load the indexes |
|
|
| Install the Hugging Face datasets library: |
|
|
| ```bash |
| python -m pip install datasets |
| ``` |
|
|
| With a local copy of this dataset repository, load the task index from its root: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| tasks = load_dataset( |
| "parquet", data_files={"test": "data/tasks.parquet"}, split="test" |
| ) |
| print(tasks[0]) |
| ``` |
|
|
| After this package is uploaded to Hugging Face, replace |
| `<your-org>/<your-dataset>` below with the actual dataset repository ID: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| tasks = load_dataset("<your-org>/<your-dataset>", "tasks", split="test") |
| # Evaluator only: do not include this configuration in the solver's context. |
| rubrics = load_dataset("<your-org>/<your-dataset>", "rubrics", split="test") |
| ``` |
|
|
| ## Run a task |
|
|
| From a local copy of this dataset repository, verify the included files against |
| `SHA256SUMS` and extract the inputs separately: |
|
|
| ```bash |
| sha256sum -c SHA256SUMS |
| mkdir -p work/solver-inputs work/evaluation-inputs |
| tar -xzf solve_norubric.tar.gz -C work/solver-inputs |
| tar -xzf eval_withrubric.tar.gz -C work/evaluation-inputs |
| ``` |
|
|
| The archives use the following layout: |
|
|
| ```text |
| cases/ |
| └── <case_id>/ |
| ├── user_queries.json |
| ├── project/ |
| │ ├── _attachments/ # when present |
| │ ├── _annotations/ # annotation assets |
| │ └── ... # application source |
| └── rubric.json # evaluation archive only |
| ``` |
|
|
| For each task, copy `project/` from the solver inputs into an independent |
| workspace. Give your coding agent the complete request from `user_queries.json` |
| and access to the task's source and assets. Preserve asset paths referenced by |
| the request or application. Save the final application under |
| `submissions/<case_id>/`, with its source, assets, and dependency manifests. |
|
|
| **Keep acceptance criteria outside the solver's context and workspace.** Rubrics |
| are distributed for reproducible evaluation; load them only after collecting |
| the candidate application. Retain the original criterion text, order, and counts. |
|
|
| The Co-Design Bench code package provides `cdb prepare`, `cdb judge`, and |
| `cdb score`. When that package is installed in your evaluation environment, |
| use these local paths: |
|
|
| ```bash |
| cdb prepare --dataset work/evaluation-inputs --submissions submissions --out work/evaluation |
| cdb judge --cases work/evaluation/cases.json --out results/run-1 --model opus |
| cdb score --cases work/evaluation/cases.json --results results/run-1/journal.jsonl --out results/run-1/scores.json |
| ``` |
|
|
| The reference judge requires an authenticated Claude CLI, Node.js, Playwright, |
| and Chromium. Set `--model` to an identifier accepted by your configured CLI. |
| It supports static frontend applications and npm builds; application builds and |
| agent tools should run in a disposable evaluation environment. Consult the code |
| package's evaluation guide for runtime setup and supported applications. |
|
|
| ## Evaluation and metrics |
|
|
| The judge-then-refute protocol first gathers source and browser evidence and |
| assigns each criterion `PASS`, `PARTIAL`, or `FAIL`. A separate verifier |
| challenges positive credit for stubs, hardcoded outputs, unreachable features, |
| and mismatches with the requested behavior. Score the final verified verdicts. |
| Evaluation does not require gold code or target screenshots. |
|
|
| For a task with `n` criteria: |
|
|
| ```text |
| Strict completion = #PASS / n |
| Graded completion = (#PASS + 0.5 × #PARTIAL) / n |
| Full task pass = 1 if all criteria are PASS, otherwise 0 |
| ``` |
|
|
| Average each metric with **equal weight per task** and report percentages. |
| PARTIAL earns zero Strict credit and half Graded credit. Report the dataset |
| scope, model, agent harness, evaluator, and evaluation settings with results. |
| The public reference judge is a starting point; reproducing the paper's |
| leaderboard also requires its complete infrastructure and evidence audits. |
|
|
| ## Citation |
|
|
| Please cite the [arXiv paper](https://arxiv.org/abs/2610.35138) when using this |
| dataset. The [project website](https://codesignbench.github.io/) provides the |
| full evaluation and interactive leaderboard. |
|
|
| ```bibtex |
| @misc{wei2026codesignbench, |
| title = {{Co-Design Bench}: Multimodal Collaborative Coding Evaluation for Agentic Models}, |
| author = {Xilin Wei and Shengyuan Ding and Xinyu Fang and Tiancheng Gu and |
| Yuchi Wang and Zhixun Li and Ziyu Liu and Yuhang Zang and |
| Weixiang Yu and Cheng Lu and Chonggao Sun and Tong Zhou and |
| Dahua Lin and Hang Zhang and Shuai Bai and Dayiheng Liu}, |
| year = {2026}, |
| eprint = {2610.35138}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.AI}, |
| url = {https://arxiv.org/abs/2610.35138} |
| } |
| ``` |
|
|
