linxichen/dsh-rigorquant

README 摘要

dsh-rigorquant English 简体中文 Unattended-within-a-session, long-running empirical/computational mathematics research for DeepSeek Harness — economics, finance, portfolio construction/optimization, simulation, computational econ/finance. RigorQuant is an agent preset + bundled skill that turns one DSH session into a context-isolated multi-agent research lab: - Parallel explorers propose candidate methods ( subagent , blank context). - A ground-truth track re-derives the analytic closed forms, invariants, and bounds for simplified cases — twice, by different means (two independent subagent ground truth calls). - An adversary eliminates routes by counterexample only. - A four-part check battery (closed-form equality, exact invariants, analytic bounds, statistical hardening) runs BEFORE numerical implementation. - Fixed-seed + LLN conventions for stochastic work. - A jacobian MCP escalation lane (opt-in; Lean as a manual external lane) settles proof-critical claims before implementation. - PASS → auto-implement and proceed; BLOCKED → 3 rounds of the same gap → strongest derivation + exact gap; BUDGET → 5 rounds → checkpoint + report. The operating pattern adapts Shanmu Jin's Crouzeix-con…

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