Add dataset card for DeepSeek V4 Pro rollouts

README.md

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+---
+language:
+  - en
+pretty_name: AutoDataBench DeepSeek V4 Pro Rollouts
+size_categories:
+  - n<1K
+tags:
+  - autodatabench
+  - agents
+  - agent-trajectories
+  - tool-use
+  - evaluation
+  - scientific-computing
+  - workflow-automation
+---
+
+# AutoDataBench: DeepSeek V4 Pro Rollouts
+
+[Project](https://autodatabench.com/) · [Blog](https://autodatabench.com/blog.html) · [Code](https://github.com/StarDewXXX/AutoDataBench) · [Download](https://huggingface.co/datasets/LordNoah/AutoDataBench/resolve/main/rollouts-deepseek-v4-pro.tar.gz)
+
+This dataset contains **144 DeepSeek V4 Pro attempts on the 24 original tasks used by AutoDataBench**, together with verifier results and 24 behavioral rubrics derived from the attempts. Each task has six independent attempts, including both successes and failures.
+
+AutoDataBench studies whether agents can produce useful executable tasks for agent training. An author agent receives an original task and the target model's attempts, then builds a new task aimed at the same behavioral challenges. The delivered task is evaluated for validity and novelty, suitable difficulty, and coverage of the intended behaviors. See the [project overview](https://autodatabench.com/) and [research blog](https://autodatabench.com/blog.html) for the motivation and evaluation method.
+
+The rollouts here provide the **target-model record on the original tasks**. They let users start an AutoDataBench run without regenerating its 144 preparation attempts. They are not the author agents' task-generation traces or the target model's attempts on newly generated tasks.
+
+## Dataset at a glance
+
+| Suite | Domain | Original tasks | Attempts per task | Total attempts | Solved attempts |
+|---|---|---:|---:|---:|---:|
+| AutomationBench | Business workflows through application tools | 8 | 6 | 48 | 24 |
+| Terminal-Bench | Terminal work and software engineering | 8 | 6 | 48 | 5 |
+| TB-Science | Scientific computing | 8 | 6 | 48 | 7 |
+| **Total** | | **24** | **6** | **144** | **36** |
+
+Counts are computed from the packaged `rollout/summary.json` files. All 144 attempts have a verifier verdict; all 24 tasks use a pass threshold of `1.0`. The overall solve rate is **25.0%**. These are results on the selected original tasks, not full-suite benchmark scores or AutoDataBench task-authoring scores.
+
+Every transcript identifies the solver as `deepseek-v4-pro`, running through `claude-code 2.1.263`. The rubric metadata uses the target-model identifier `anthropic-deepseek-v4-pro`. Attempts are independent, with no memory carried between them.
+
+## Files and format
+
+The release is a gzip-compressed tar archive, `rollouts-deepseek-v4-pro.tar.gz` (10,996,857 bytes), containing Markdown transcripts and JSON summaries and rubrics.
+
+```text
+rollouts-deepseek-v4-pro/
+├── README.md
+├── automationbench/
+├── terminal-bench/
+└── tb-science/
+    └── <task-name>/
+        ├── modes.json
+        └── rollout/
+            ├── README.md
+            ├── summary.json
+            ├── attempt-0.md
+            ├── attempt-1.md
+            ├── attempt-2.md
+            ├── attempt-3.md
+            ├── attempt-4.md
+            └── attempt-5.md
+```
+
+All three suite directories follow the same task layout.
+
+| File | Contents |
+|---|---|
+| `attempt-N.md` | Reward, solved status, agent/model metadata and token counts, followed by verifier output and the ordered transcript: user input, recorded reasoning, tool calls and tool results. |
+| `summary.json` | Task name, attempt count, missing-verdict count, pass threshold, per-attempt `rewards` and `solved` arrays, `n_solved`, and `pass_rate`. |
+| `rollout/README.md` | Per-attempt outcome table and guidance for reading the transcripts. |
+| `modes.json` | Analyst-derived behavioral rubric: model/task metadata and modes with descriptions, examples, and source evidence pointing to attempts and steps. |
+
+There are **125 rubric modes** across the 24 tasks. Modes can describe failures, detours, or challenges the model handled successfully; a mode is not necessarily a failed verifier assertion.
+
+These are sanitized reading copies. Credentials are masked, and long individual tool outputs may be shortened in the middle while retaining their beginning and end. The archive does not contain the original raw runtime directories or the analyst's container output. Executable task definitions and the evaluation harness are available in the [code repository](https://github.com/StarDewXXX/AutoDataBench).
+
+## Download and inspect
+
+```bash
+curl -fL --retry 3 -o rollouts-deepseek-v4-pro.tar.gz \
+  https://huggingface.co/datasets/LordNoah/AutoDataBench/resolve/main/rollouts-deepseek-v4-pro.tar.gz
+
+tar -xzf rollouts-deepseek-v4-pro.tar.gz
+```
+
+Archive SHA256:
+
+```text
+8ed8eb8c6890049e4e4877cd6f9ac5743b8836b3e307e94129d24148c9a8d314
+```
+
+For example, inspect the six attempts for a task and their outcomes:
+
+```python
+import json
+from pathlib import Path
+
+root = Path("rollouts-deepseek-v4-pro")
+rollout = root / "automationbench" / "cash-flow-forecast" / "rollout"
+summary = json.loads((rollout / "summary.json").read_text())
+
+print(f"Solved: {summary['n_solved']}/{summary['attempts']}")
+for i, (reward, solved) in enumerate(zip(summary["rewards"], summary["solved"])):
+    print(f"attempt-{i}.md: reward={reward}, solved={solved}")
+
+transcript = (rollout / "attempt-0.md").read_text()
+```
+
+## Use with AutoDataBench
+
+From an [AutoDataBench checkout](https://github.com/StarDewXXX/AutoDataBench), download and extract the archive as above, then place transcripts and rubrics in their separate expected locations:
+
+```bash
+for suite in automationbench terminal-bench tb-science; do
+  for task_dir in "rollouts-deepseek-v4-pro/$suite/"*/; do
+    task=$(basename "$task_dir")
+    prep_dir="prep/deepseek-v4-pro/$suite/$task"
+    rubric_dir="rubrics/deepseek-v4-pro/$suite/$task"
+    mkdir -p "$prep_dir" "$rubric_dir"
+    cp -R "$task_dir/rollout" "$prep_dir/"
+    cp "$task_dir/modes.json" "$rubric_dir/"
+  done
+done
+```
+
+Follow the code repository's [quick start](https://github.com/StarDewXXX/AutoDataBench#quick-start) to configure the endpoint and run an episode.
+
+The author agent receives the original-task transcripts; **`modes.json` is a hidden evaluation rubric and must remain outside the author agent's inputs**. To use a different target model, generate its own rollouts and rubrics with `run_rollout.py` and `run_analyst.py` rather than reusing this model's behavioral evidence.
+
+## Interpretation and use
+
+This release supports reproducing AutoDataBench's preparation stage, inspecting tool-use behavior, and auditing the evidence behind its rubrics. Passing a task's verifier does not mean every decision in its transcript was correct; rubric evidence can capture behavior outside the verifier's checks.
+
+The data covers a selected set of original benchmark tasks and is not a representative sample of all tasks in the three source suites. Keep these original-task records separate from training data when evaluating on the same tasks; some task instructions include explicit benchmark-contamination notices. For source-task attribution and applicable terms, consult the code repository's [attribution file](https://github.com/StarDewXXX/AutoDataBench/blob/main/ATTRIBUTION.md).