pracha

Symplex is an AI-native platform for investigating complex systems under uncertainty. It turns a problem into competing evidence-grounded hypotheses, compiles them into executable mathematical and computational models, and uses simulation, inference, and verification to evaluate their consequences. A meta-reasoning controller chooses the next best investigation action—such as gathering data, testing a rival model, or running an experiment—while preserving uncertainty and provenance. Symplex also supports bounded recursive self-improvement, allowing its investigation workflow to improve only through protected, auditable evaluation on fresh tasks.