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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.
|