Datasets:

DavidHatley
/

system-one-mini-data

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.