Simplify dataset card

Keep only the dataset structure and license sections, and direct all other documentation to the public ASI-Bench repository.

README.md

@@ -1,74 +1,32 @@
 ---
-pretty_name: ASI-Bench Generated Instances (Seed 31415 Snapshot)
+pretty_name: ASI-Bench Generated Instances (Seed 31415)
 language:
   - en
 license: apache-2.0
 size_categories:
   - n<1K
-annotations_creators:
-  - machine-generated
-source_datasets:
-  - original
 tags:
   - ai-for-science
   - benchmark
-  - scientific-reasoning
   - agents
-  - agent-evaluation
-  - code-generation
-  - multimodal
-  - text
-  - tabular
-  - timeseries
+  - scientific-reasoning
 ---
 
 # ASI-Bench Generated Instances
 
-This repository is a file-based snapshot of generated task instances from
-[Agent-AI4Sci-Bench](https://github.com/apexin-ai/Agent-AI4Sci-Bench), a
-benchmark for evaluating agents on project-level AI-for-Science problems.
-
-The snapshot contains **60 task instances**, **1,530 files**, and approximately
-**113 MiB** of data. Each task provides four prompt variants, public input data,
-and generated reference artifacts. It is intended for benchmark development,
-reproducibility studies, scorer validation, and local evaluation research.
-
-> **Answer-leakage warning:** every task directory contains `reference/`
-> artifacts. These are ground-truth or validation materials and are public in
-> this repository. Do not expose them to an evaluated agent, and do not use this
-> snapshot for a blind public leaderboard. Use separately generated private
-> instances for contamination-resistant evaluation.
-
-## Scientific domains
-
-| Domain | Tasks |
-|---|---:|
-| Astronomy | 3 |
-| Biology | 6 |
-| Biostatistics | 1 |
-| Chemistry | 6 |
-| Computer science | 9 |
-| Earth science | 4 |
-| Electrical engineering | 2 |
-| Materials science | 2 |
-| Mathematics | 12 |
-| Physics | 8 |
-| Robotics | 7 |
-| **Total** | **60** |
-
-## Repository structure
+For usage instructions, task definitions, evaluation details, and all other
+information, see the [ASI-Bench repository](https://github.com/apexin-ai/ASI-Bench).
 
-This is not a conventional row-oriented dataset and does not define standard
-train, validation, or test splits. Root-level metadata is separated from the
-generated benchmark instances under `tasks/`:
+## Dataset structure
 
 ```text
 .
 ├── README.md
+├── LICENSE
 ├── metadata.json
 ├── .gitattributes
 └── tasks/
-    └── <domain>.<task_id>__seed31415/
+    └── <instance-id>/
         ├── prompt_b1.md
         ├── prompt_b2.md
         ├── prompt_b3.md
@@ -79,109 +37,17 @@ generated benchmark instances under `tasks/`:
         └── framework_task_info.json
 ```
 
-- `prompt_b1.md` through `prompt_b4.md` are four disclosure levels of the task
-  prompt.
-- `data/` contains the files that may be provided to the evaluated agent.
-- `reference/` contains generated answers, diagnostics, or other validation
-  artifacts and must be isolated from the agent during evaluation.
-- `instance_meta.json` and `framework_task_info.json` record instance and
-  framework provenance.
+Each directory under `tasks/` is one generated benchmark instance.
 
-Schemas vary by task. Files include Markdown, JSON, CSV, Python, NumPy arrays
-(`.npy` and `.npz`), and images. Consumers should treat each task as a directory
-contract rather than concatenate the repository into a single table.
-
-## Download
-
-Using the Hugging Face CLI:
-
-```bash
-hf download Apexintelligence-AI/ASI-Bench-seed31415 \
-  --repo-type dataset \
-  --local-dir ASI-Bench-seed31415
-```
-
-Or with Git and Git LFS:
-
-```bash
-git lfs install
-git clone https://huggingface.co/datasets/Apexintelligence-AI/ASI-Bench-seed31415
-```
+- `prompt_b1.md` through `prompt_b4.md` contain the four prompt variants.
+- `data/` contains the task input files.
+- `reference/` contains the reference answer and validation artifacts.
+- `instance_meta.json` records generation metadata.
+- `framework_task_info.json` records framework and re-evaluation metadata.
+- `metadata.json` is the dataset-level manifest.
 
-## Recommended evaluation use
-
-1. Select exactly one prompt level (`b1`–`b4`) for a run.
-2. Give the agent only that prompt and the corresponding `data/` directory.
-3. Keep `reference/` and framework-only metadata outside the agent's readable
-   workspace.
-4. Run the matching scorer from the recorded Agent-AI4Sci-Bench source commit.
-5. Record the task ID, prompt level, source commit, actual generation seed,
-   runtime, dependencies, and agent configuration with every result.
-
-The repository contains instances and reference artifacts, not a complete
-leaderboard protocol. Reproducible comparisons additionally require the
-matching benchmark code, scorer configuration, runtime dependencies, and
-execution limits.
-
-## Intended uses
-
-- Reproducing and auditing AI-for-Science benchmark tasks.
-- Developing agent harnesses, sandboxes, and scorer integrations.
-- Studying how prompt disclosure level affects agent behavior.
-- Testing data loading and output validation for heterogeneous scientific
-  workflows.
-- Creating demonstrations or tutorials where public reference answers are
-  acceptable.
-
-## Out-of-scope uses
-
-- Reporting this public snapshot as a contamination-resistant benchmark.
-- Allowing an evaluated agent to read `reference/` or framework-only metadata.
-- Treating scores from different prompt levels, scorer versions, or runtime
-  limits as directly comparable without disclosure.
-- Assuming the task collection represents all scientific fields, methods, or
-  real-world deployment conditions.
-
-## Limitations and responsible use
-
-- The snapshot contains only one generated instance per task, so it does not
-  measure variation across seeds.
-- Two tasks use a generation seed different from their folder suffix; consult
-  `metadata.json`.
-- One included task has source status `test`, while the others have status
-  `final`.
-- Public reference artifacts create a substantial risk of evaluation leakage,
-  memorization, and benchmark gaming.
-- Tasks have heterogeneous schemas, dependencies, resource demands, scoring
-  logic, and numerical tolerances.
-- Prompts are in English and the domain distribution is not balanced.
-- Generated or simulated scientific tasks may simplify assumptions found in
-  real research workflows and should not be used to support scientific,
-  medical, safety-critical, or policy decisions without independent review.
-- The dataset was not designed to contain personal information, but no formal
-  privacy audit is claimed. Users publishing derived artifacts should review
-  them for unintended sensitive content.
-
-## Licensing
+## License
 
 Except where otherwise noted, this dataset repository is licensed under the
-[Apache License 2.0](LICENSE). The license permits use, reproduction,
-modification, and distribution subject to its conditions, including applicable
-notice and attribution requirements.
-
-The Apache License applies only to material for which the dataset maintainers
-have authority to grant those rights. Some task inputs, dependencies, or bundled
-third-party components may have separate terms that continue to apply; the
-dataset license does not replace them. Users should review the
-[source repository](https://github.com/apexin-ai/Agent-AI4Sci-Bench), relevant
-task directories, and third-party notices before reuse.
-
-## Maintenance and versioning
-
-This is a pinned generated snapshot, not a continuously synchronized mirror of
-the upstream `main` branch. Changes to the upstream benchmark after the recorded
-commit are not automatically reflected here. Consult `metadata.json` and the
-Hugging Face commit ID when reporting results.
-
-For questions or corrections, use the repository's Hugging Face Discussions or
-the upstream GitHub issue tracker.
+[Apache License 2.0](LICENSE). Third-party components remain subject to their
+respective licenses and terms.