---
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.