dsh-rigorquant

Conducting empirical or computational mathematics research often poses a significant challenge: how can researchers ensure that a proposed method is mathematically rigorous and reliable before diving into numerical implementation? Traditional workflows rely on manual verification, which is prone to oversights or insufficient checks. The dsh-rigorquant plugin addresses this by transforming a DeepSeek Harness session into an unattended multi-agent research lab tailored for fields like economics, finance, and portfolio optimization. It leverages multiple independent agent roles—such as explorers, oracles, and adversaries—alongside a strict check battery to automatically validate methods numerically before any implementation, thereby preventing potential flaws.

The core capability of this plugin lies in its multi-agent collaboration framework: the orchestrator dispatches tasks, explorers propose candidate methods, oracles re-derive critical claims from first principles, and adversaries hunt counterexamples to eliminate unreliable paths. Before numerical implementation, a four-part check battery (including closed-form equality, exact invariants, analytic bounds, and statistical hardening) runs to ensure only validated methods proceed. This design emphasizes independent verification and audit trails, making the research process more transparent and reproducible.

dsh-rigorquant is ideal for researchers, quantitative analysts, and financial engineers who need complex mathematical modeling and verification. Whether in academic or industrial settings, if your work involves financial simulations, portfolio construction, or computational economics—and you demand high mathematical rigor—this plugin can automate your verification workflow, enhancing research quality and efficiency. It is especially suited for users who prefer to complete end-to-end research within a session without manually handling each verification step.