Add a dataset or a baseline
The benchmark is open to contributions. The fastest way to make the comparison fairer is to add a dataset where it falls short, or a baseline it should be measured against.
What is documented
The harness — the datasets registry, the per-algorithm runners, the metric definitions, and the methodology — is documented in full and reproducible from the published data and the versioned static API. The Ufinq algorithm itself is closed; it is run as one baseline among several, on the same fixed configuration and the same held-out splits as every other algorithm.
Good contributions
- A dataset with a known ground-truth law, for the symbolic-recovery metric.
- A real-world dataset that exposes a failure mode the current corpus misses.
- A new baseline runner that meets the one-declared-configuration policy.
- A correction to a metric definition or a dataset's provenance.
Contributions follow the methodology: one declared configuration per algorithm, uniform across datasets and seeds, with failures reported rather than hidden.
How to propose one
Email research@ufinq.com with the dataset, or with a baseline's runner manifest and Dockerfile. Each new-baseline submission is gated by an automated conformance canary: it validates the runner manifest + Dockerfile, builds and scans the image, and runs the runner over a small canary set, checking that it produces well-formed, finite trials — conformance, not accuracy. The submission gets an automated approve / request-changes / block decision, and a human reviewer makes the final call.