{
 "id": "uwm-software-engineering/complexity-kink-research",
 "owner": "uwm-software-engineering",
 "name": "complexity-kink-research",
 "downloads": 457,
 "likes": 2,
 "last_modified": "2026-10-31T16:59:06.000Z",
 "stated_licence": "cc-by-4.0",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [
  {
   "id": "nvidia/OpenCodeInstruct",
   "licence": "cc-by-4.0",
   "found": true,
   "evidence": "https://huggingface.co/datasets/nvidia/OpenCodeInstruct/blob/8f3ba5bafe4d6e8db46082cf7ae6741bc370604d/README.md"
  }
 ],
 "gated": false,
 "tasks": [
  "text-generation"
 ],
 "citation": "@inproceedings{complexitykink2026,\n  title     = {The Complexity Kink: LLM Rubric Instruments for Causal Inference on Code Generation Reliability},\n  author    = {Hernandez, Michael and Zhao, Tian},\n  booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track},\n  year      = {2026}\n}",
 "signal": "permissive",
 "reason": "The declared licence (cc-by-4.0) is classified as permissive.",
 "revision": "7b502f4b064573da8ca2bc4a9c9ff882f6fc38ff",
 "stated_evidence": "https://huggingface.co/datasets/uwm-software-engineering/complexity-kink-research/blob/7b502f4b064573da8ca2bc4a9c9ff882f6fc38ff/README.md",
 "file_evidence": "https://huggingface.co/datasets/uwm-software-engineering/complexity-kink-research/tree/7b502f4b064573da8ca2bc4a9c9ff882f6fc38ff",
 "card_nc": false
}