| --- |
| license: apache-2.0 |
| task_categories: |
| - text-generation |
| - question-answering |
| language: |
| - en |
| tags: |
| - adaption-labs |
| - autoscientist |
| - science |
| - math-code |
| - verifier-grounded |
| - scientific-reasoning |
| - code-generation |
| - abstention |
| pretty_name: VeriSci Verified Science Math Code |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: train.jsonl |
| - split: validation |
| path: validation.jsonl |
| - split: test |
| path: test.jsonl |
| --- |
| |
| # VeriSci Verified Science Math Code |
|
|
| Verifier-grounded dataset for the Adaption AutoScientist Challenge Part 2, targeting the Science category with secondary Math and Code coverage. |
|
|
| ## Summary |
|
|
| VeriSci trains models to solve scientific computations, finite-difference PDE updates, numerical ODE steps, unit-checked mechanics, thermodynamics, circuits, chemistry stoichiometry, molarity, unit conversion, vector decomposition, two-point linear modeling, small Python code-generation tasks, and explicit abstention when required variables are missing. |
|
|
| Every row is generated by a deterministic Python program and checked by a verifier. This makes the dataset suitable for supervised fine-tuning and for objective base-vs-adapted evaluation. |
|
|
| ## Fields |
|
|
| - `id`: stable row id. |
| - `split`: train, validation, or test. |
| - `prompt`: instruction for the model. |
| - `completion`: target response with reasoning and final JSON. |
| - `reasoning_trace`: intermediate reasoning steps. |
| - `final_answer`: machine-readable target answer. |
| - `verifier`: verifier type and parameters. |
| - `variables`: source variables used to generate the row. |
| - `dedupe_signature`: canonical hash used for de-duplication and split assignment. |
| - `task_family`: task family. |
| - `difficulty`: easy, medium, or hard. |
| - `source`: generation provenance. |
| - `license`: row license. |
|
|
| ## Task Families |
|
|
| - `unit_checked_mechanics` |
| - `thermodynamics` |
| - `electric_circuits` |
| - `exponential_decay` |
| - `chemistry_stoichiometry` |
| - `chemistry_solutions` |
| - `numerical_ode` |
| - `numerical_integration` |
| - `finite_difference_pde` |
| - `unit_conversion` |
| - `vector_reasoning` |
| - `linear_modeling` |
| - `dimensional_analysis` |
| - `python_code_generation` |
| - `scientific_abstention` |
| - `code_abstention` |
|
|
| ## Reproduction |
|
|
| ```bash |
| git clone <repository-url> |
| cd verisci-autoscientist |
| PYTHONPATH=src python3 -m verisci.generate \ |
| --rows 8000 \ |
| --out data/generated/verisci_8k.jsonl \ |
| --csv data/generated/verisci_8k.csv \ |
| --summary data/generated/verisci_8k_summary.json |
| PYTHONPATH=src python3 -m verisci.evaluate --data data/generated/verisci_8k.jsonl |
| ``` |
|
|
| ## Release Integrity |
|
|
| The current 8k public release is split-safe: |
|
|
| | Metric | Value | |
| |---|---:| |
| | Rows | 8,000 | |
| | Unique prompts | 8,000 | |
| | Duplicate prompts | 0 | |
| | Train/validation/test prompt leakage rows | 0 | |
| | Dedupe signatures | 8,000 | |
| | Gold verifier accuracy | 100% | |
|
|
| ## Adaptive Data And AutoScientist |
|
|
| This dataset has been run through a low-credit Adaption pilot and is prepared for AutoScientist training. |
|
|
| Current platform evidence: |
|
|
| | Metric | Value | |
| |---|---:| |
| | Pilot dataset ID | `09749657-7dde-4e20-8988-1d9ee53e9132` | |
| | 50-row Adaptive Data estimate | 1 credit, 11 minutes | |
| | Enhanced-completion audit | Failed: 0/42 rows preserved `Final: {...}` | |
| | Source-column audit | Passed: 42/42 rows preserved `Final: {...}` | |
| | 12k source preflight dataset ID | `13b1c92a-a96b-4ce7-810b-4368ad6aa234` | |
| | 12k source-column audit | Passed: 12,000/12,000 rows preserved `Final: {...}` | |
| | AutoScientist recommendation | `google/gemma-3-4b-it`, 1-epoch LoRA | |
| | Diagnostic 8k Llama run | `43b5486d-0bfc-4e9f-869e-a9892a679386`; best win rate 49.48%; not final | |
| | Clean 8k candidate run | `7ea2c71b-94d7-472d-8288-795ab5e0a2c3` on `mistralai/Mistral-7B-Instruct-v0.2` | |
| | Clean 8k candidate dataset | `3103c6ac-7d61-4271-af62-41cb023de85e` | |
| | Clean 8k latest metric | Succeeded, 5/5 iterations, best win rate 52.45%, checkpoint packaged | |
|
|
| Fill after final AutoScientist run: |
|
|
| | Metric | Value | |
| |---|---:| |
| | Adaptive Data grade before | Pending | |
| | Adaptive Data grade after | Pending | |
| | Adaptive Data quality improvement | Pending | |
| | AutoScientist best win rate | 52.45%; below the 75% publish gate | |
| | Domain augmentation rows | 8,000 source rows in clean candidate | |
| | General augmentation rows | 0 in clean candidate | |
|
|
| ## Limitations |
|
|
| VeriSci is synthetic and deliberately narrow. It is designed to test exact scientific and code reasoning patterns, not to replace expert review for high-stakes engineering, laboratory, medical, financial, or safety decisions. |
|
|
| ## License |
|
|
| Apache-2.0. |
|
|
