Wiselnn/Co-Design-Bench

Co-Design Bench

Multimodal Collaborative Coding Evaluation for Agentic Models

Project website · Paper · Solver inputs · Evaluation inputs · Citation

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	Applications, user requests, annotations, and attachments for all 96 tasks, without rubrics.
Evaluation archive	The same task inputs, with each task's fixed rubric.json.
Task index	96 task records, including annotation previews; available as the default tasks configuration.
Rubric index	521 criterion records; available as the evaluator-only rubrics configuration.
Rubric exports	CSV, JSONL, and grouped JSON exports of the fixed public criteria, with their case list.
Original rubric package	Original per-task rubric JSON files for all 96 tasks.
Manifest	Public scope, case IDs, counts, and archive checksums.
SHA-256 checksums	File integrity checks for this release.
Citation metadata	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:

python -m pip install datasets

With a local copy of this dataset repository, load the task index from its root:

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:

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:

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:

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:

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:

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 when using this dataset. The project website provides the full evaluation and interactive leaderboard.

@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}
}