--- pretty_name: MolP2SBench license: cc-by-4.0 language: - en size_categories: - 1K_labels` configuration. ## Fields and scoring Tasks contain `record_id`, `masked_smiles`, `mask`, `properties`, and `candidate_completed_smiles`. Property metadata records units and representations; NMR fields describe calculated magnetic shieldings, not a general set of experimental chemical shifts. Answer files contain `record_id` and `label`. The original prompts and Python scorer are included. Run from a downloaded repository snapshot: ```bash python scripts/score_tasks.py --property dipole --condition correct --predictions predictions.jsonl ``` The scorer reports exact-match outcomes and invalid or missing predictions. It does not score free-text reasoning, infer why a model failed, or implement the paper's family-weighted PGR/CFR metrics. See `SOURCE_README.md` for the original prediction schema and protocol. ## Using the data for improvement research Compare outcomes by property and evidence condition to form testable hypotheses about evidence use. A change in one answer is not a diagnosis of an internal reasoning mechanism. Evaluate revisions on independent molecular families under matched budgets; the views here do not supply an independent held-out split. This release supports diagnostics and experiments, and does not establish that recursive self-improvement or open-ended molecular discovery has been achieved. ## Source and permissions The data, prompts and scripts are copied without changes from GitHub commit `1ede82bf68f634097b296b1937d66a9b04bbf269`. `source-manifest.json` records their SHA-256 hashes. Dataset construction and preprocessing are described in the linked paper. The upstream sources retain their respective terms: - **QM9**: HOMO–LUMO gaps; [data record](https://figshare.com/articles/dataset/Data_for_6095_constitutional_isomers_of_C7H10O2/1057646), CC0. - **QM9S**: dipole, IR, Raman and UV–Vis properties; [data record](https://figshare.com/articles/dataset/QM9S_dataset/24235333), CC BY 4.0; [source article](https://doi.org/10.1038/s43588-023-00550-y). - **QM9NMR**: gas-phase computed magnetic shieldings; [data record](https://doi.org/10.17172/NOMAD/2021.10.16-1), CC BY 4.0; [project and citation information](https://moldis-group.github.io/qm9nmr/). This mirror preserves the released task payloads and does not redistribute the complete upstream databases. The benchmark constructs masked-scaffold candidate-selection tasks and condition-specific property views from those sources; the upstream measurements and calculations are not newly generated here. The MolP2SBench benchmark data, task formulations, prompts and accompanying documentation in this release are licensed under [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/). Reuse and adaptation, including commercial use, are permitted with attribution, a link to the license and an indication of changes. See `LICENSE`. The upstream data retain the terms listed above; this release does not impose new restrictions on CC0 material. Please credit MolP2SBench and the relevant upstream sources when reusing their material. The original Python scripts in `scripts/` are included unchanged for reference; this data license does not assign them a software license. ## Citation ```bibtex @misc{xie2026molp2sbench, title={MolP2SBench: Do LLMs Really Reason from Molecular Properties to Structure?}, author={Yuhang Xie and Zhiyi Shi and Zeyi Zhang and Zhisheng Qi and Yushun Dong and Yu Wang and Xiongye Xiao}, year={2026}, eprint={2610.12522}, archivePrefix={arXiv}, primaryClass={physics.chem-ph}, url={https://arxiv.org/abs/2610.12522} } ```