Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
json
Languages:
English
Size:
10K - 100K
License:
File size: 2,213 Bytes
aab01d6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | ---
license: apache-2.0
language:
- en
task_categories:
- text-classification
pretty_name: System One Mini Synthetic Diagnostics
configs:
- config_name: default
data_files:
- split: train
path: data/train.jsonl
- split: validation
path: data/validation.jsonl
- split: calibration
path: data/calibration.jsonl
- split: development_renderer
path: data/development_renderer.jsonl
- split: test
path: data/test.jsonl
---
# System One Mini Synthetic Diagnostics
Deterministic synthetic controlled-intervention summaries used by `DavidHatley/system-one-mini`. The records contain no real coding-agent traces, private repositories, personal data, external model outputs, or teacher labels.
**This dataset and model are independent research artifacts, not reproductions of Jev or RLCD.**
## Splits
| Split | Rows |
|---|---:|
| `train` | 20,000 |
| `validation` | 2,000 |
| `calibration` | 2,000 |
| `development_renderer` | 2,000 |
| `test` | 2,000 |
`development_renderer` is the former sealed renderer split that was subsequently used to compare candidates. It must not be described as an unbiased final test. `test` is a separate renderer written and frozen after model selection, then evaluated once for the initial release.
## Fields
- `id`: SHA-256 fingerprint of the latent synthetic situation
- `state`: rendered diagnostic summary
- `labels`: five numeric labels in schema order
- Five named string label fields
- `latent`: complete generated situation used to compute labels
- `provenance`: generator, split, renderer, and seed
## Generation
Generation code and manifests are included in `generator/` and `provenance/`. The policy makes labels mechanically identifiable: every component trial starts independently from the same original failing snapshot, and exactly one clearing intervention identifies the operational failure source.
## Limitations
- All scenarios are synthetic and templated.
- The label taxonomy covers only four component classes plus `unknown`.
- Recurring logical patterns across splits do not represent natural incident prevalence.
- Public test labels make the split unsuitable as a permanently sealed benchmark for future model selection.
|