OpenSimula — open implementation of Simula-style mechanism design for synthetic data (in AfterImage) [P]

Hi r/MachineLearning,

We added OpenSimula to our open-source dataset tool AfterImage: an experimental Python implementation of the Simula mechanism-design recipe from Davidson et al. (TMLR, PDF; framing also in this research blog).

Problem it targets:

For some SFT/eval setups you care less about “one prompt → one answer” and more about controlled diversity over a reasoning space: which axes of variation exist, how you joint-sample them, and how you stress-test generations before they land in a JSONL file.

What the code actually does (high level):

LLM-built factor taxonomies → weighted mix sampling over factors → meta-prompt diversification (+ optional complexification) → requirement critic loop with refinement → optional double-critic gate for verifiable MCQ. Artifacts are a versioned opensimula/ checkpoint (manifest, taxonomy bundle, sampling strategy) plus append-only JSONL for accepted points. You can plug in the same GenerationMonitor we use elsewhere for observability into generation metrics, or bridge scenarios into ConversationGenerator via a small callback.

Hard disclaimers (please read):

• This is not a Google product, not a reference port of anything internal—just our read of the published recipe in the paper.
• API is explicitly experimental and may change.
• Cost and latency explode if you remove the caps on taxonomy width/depth; wide trees are many structured calls unless you tune bounds.
• “Mechanism design” here helps structure the data-generating process; it does not magically fix model collapse or bad teacher models.

Code & docs:

• Repo (whole library): https://github.com/altaidevorg/afterimage
• Simula examples: https://github.com/altaidevorg/afterimage/tree/main/examples/simula
• Short overview: https://afterimage.altai.dev/opensimula.html
• API reference: https://afterimage.altai.dev/api/simula.html

I genuinely would love hear your feedback if any.