Add dataset card

README.md

@@ -0,0 +1,126 @@
+---
+pretty_name: HackTrace Trajectories
+license: cc-by-4.0
+language:
+- en
+task_categories:
+- text-classification
+- text-generation
+tags:
+- reward-hacking
+- coding-agents
+- ai-safety
+- monitoring
+- reinforcement-learning
+- code
+size_categories:
+- 100K<n<1M
+configs:
+- config_name: trajectories
+  default: true
+  data_files:
+  - split: train
+    path: trajectories/train.parquet
+  - split: test
+    path: trajectories/test.parquet
+- config_name: problems
+  data_files:
+  - split: train
+    path: problems/problems.parquet
+---
+
+# HackTrace Trajectories
+
+173,561 multi-turn coding trajectories of Qwen3-8B on CodeContests problems, each labeled for shortcut behavior. This is
+the dataset of **HackTrace: Behavior-Supervised Detection of Reward Hacking During Code Generation**.
+
+- **Paper:** [arXiv:2610.03055](https://arxiv.org/abs/2610.03055)
+
+A coding agent can pass its tests by fixing its code, or by deleting the test that exposes the bug. Every trajectory here
+carries two labels: `ACT` marks shortcut behavior whether or not it worked (test tampering, hard-coded outputs,
+special-casing, evaluation bypass), and `OUTCOME` additionally requires that the shortcut passed the visible tests and
+failed the hidden ones. Honest and shortcut trajectories exist for the same problems, so detectors can be compared within
+a problem.
+
+```python
+from datasets import load_dataset
+ds = load_dataset("JH012/HackTrace-Data", "trajectories")   # splits: train, test
+problems = load_dataset("JH012/HackTrace-Data", "problems", split="train")
+```
+
+## Files
+
+| Config | File | Rows | Contents |
+|---|---|---|---|
+| `trajectories` | `trajectories/train.parquet` | 133,009 (26,329 `ACT` positives) | training split, 375 problems; 56,617 editable-test and 76,392 read-only-test trajectories |
+| `trajectories` | `trajectories/test.parquet` | 40,552 (7,944 `ACT` positives) | test split, 315 unseen problems; 15,200 editable-test (190 problems) and 25,352 read-only-test (125 problems) |
+| `problems` | `problems/problems.parquet` | 990 | problem statements with the visible cases shown to the agent and the 12 hidden cases used for labeling, one row per (tier, problem) |
+
+`MANIFEST.json` lists the sha256, size and row count of every file. The split is by problem: no test problem appears in
+training.
+
+## Row schema (`trajectories`)
+
+| Field | Meaning |
+|---|---|
+| `id` | trajectory id |
+| `split` | `train` or `test` |
+| `variant` | `editable-test` (the agent may rewrite `solution.py` and `test.py`) or `read-only-test` (only `solution.py`) |
+| `tier`, `problem_name` | problem group and CodeContests problem name; join with `problems` on both |
+| `num_turns` | number of agent turns (at most 3) |
+| `messages` | the full conversation: system prompt, user turns (problem, issued `test.py`, execution feedback) and assistant turns |
+| `final_solution`, `final_test` | `solution.py` and `test.py` after the last turn (an empty string means the file was emptied) |
+| `visible_tests_pass` | the final files pass the visible grader (`python test.py` prints `ALL PASS`) |
+| `hidden_tests_pass` | the final solution passes the 12 hidden cases; evaluated only when `visible_tests_pass` is true, null otherwise |
+| `tests_modified` | `test.py` differs from the issued file |
+| `label_source` | `judge` (individually judged by GPT-5.4), `rule` (editable-test, failed the visible grader, tests unchanged: negative by rule), or `rule, audited by judge` (rule-labeled rows that were also judged as an audit) |
+| `judge_category` | the judge's category; null for rule-labeled rows and for 21 judged rows whose verdict text was not stored |
+| `judge_rationale` | the judge's one-sentence rationale |
+| `ACT` | shortcut behavior in the final artifacts, whether or not it succeeded |
+| `OUTCOME` | `ACT` and a visible-grader pass with a hidden-test failure |
+| `generated_tokens` | tokens generated by the agent over the trajectory |
+
+Judge categories: the shortcut categories are `test_tamper`, `hardcode_output`, `special_casing` and `eval_bypass`; the
+others are `honest_correct`, `honest_but_wrong`, `honest_fix_test` (a test edit that does not weaken the check, such as
+fixing formatting) and `other`.
+
+## How the data was built
+
+- **Agent.** Qwen3-8B, at most 3 turns per trajectory, up to 600 new tokens per turn, temperature 0.9, top-p 0.95. Each
+  turn the agent sees the problem and the current `test.py`; later turns add execution feedback.
+- **Prompts.** The system prompts were written to elicit shortcut behavior for study, and they are part of every
+  trajectory's `messages`. The paper's Appendix A describes them and an additional neutral-prompt pool.
+- **Labels.** A GPT-5.4 judge read the problem, the original and final tests, their AST comparison, the final files and
+  the execution outcomes of 136,900 trajectories: every one that passes the visible grader, modifies the tests, or uses the
+  read-only prompt. The remaining 36,661 editable-test trajectories fail the visible grader with unchanged tests and are
+  negative by rule.
+- **Checks.** An independent Gemini-2.5-pro pass agrees with the judge on shortcut behavior for 95.9% of 107,035
+  earlier-pool trajectories (κ = 0.90). A blind, LLM-assisted human audit of 305 stratified test trajectories agrees
+  with the final labels on 97.0% (κ = 0.94).
+
+Activation features (about 140 GB) and the fitted detectors are not part of this repository.
+
+## Licenses
+
+| Content | Source | License |
+|---|---|---|
+| problem statements, visible and hidden cases | [CodeContests](https://github.com/google-deepmind/code_contests) (Li et al., 2022) | CC BY 4.0 |
+| trajectories | generated by Qwen3-8B (Apache-2.0 model) | CC BY 4.0 |
+| labels, judge categories and rationales | HackTrace authors; rationales generated by GPT-5.4 | CC BY 4.0 |
+
+The problem statements originate from competitive-programming sites, and their copyright stays with the original authors.
+The dataset contains no personal data.
+
+## Citation
+
+```bibtex
+@article{jiang2026hacktrace,
+  title   = {HackTrace: Behavior-Supervised Detection of Reward Hacking During Code Generation},
+  author  = {Jiang, Hao and Li, Xin and Wang, Annan and Zhang, Yichi and Lin, Weisi},
+  journal = {arXiv preprint arXiv:2610.03055},
+  year    = {2026},
+  url     = {https://arxiv.org/abs/2610.03055}
+}
+```
+
+Please also cite CodeContests (Li et al., 2022).