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
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}
}