Seedbaselines

Seedbaselines is a term used in data science, software research, and computational experiments to denote a standardized set of random seeds or seed configurations used to initialize stochastic processes across experiments. The goal is to improve reproducibility, comparability, and transparency by ensuring that results can be rerun under the same initial conditions.

Seedbaselines apply to any workflow that relies on randomness, including training neural networks, hyperparameter searches, genetic algorithms, Monte Carlo simulations, and other simulations. By establishing a common set of seeds, researchers and practitioners can better isolate the effects of algorithms and methods from random variation introduced by stochastic initialization.

A seedbaselines framework typically includes a seed registry, baseline configurations, documentation, and tooling to log seeds used in each run. Practices involve recording seeds in experiment configuration files, reporting the seed set in publications or reports, and providing a straightforward way to reproduce results by using the same seeds.

Benefits of seedbaselines include improved reproducibility and fairer comparisons between methods. Limitations include the risk of seed bias if a small or unrepresentative set of seeds is used, and the need to run experiments across multiple seeds to capture variability. Seedbaselines should complement broader replication efforts and nondeterministic aspects of workflows, rather than replace them.

Related concepts include reproducibility, random seed, baselines, and benchmarking. Seedbaselines are most effective when documented, versioned, and shared within a research or development community to support transparent evaluation.