legion:polymath-engine

Structured exploration reduces ambiguity by applying research-first clarification to detect knowledge gaps and organize choices. It uses guided onboarding and crystallization outputs to accelerate decision-making in complex problems.

To start guided exploration, initiate the engine and request a guided onboarding. You then choose exploration options to begin the crystallization process and generate planning documents.

Yes, gap detection supports team workflows by guiding members through clarifying questions and managing responsible trade-offs. It structures choices to ensure comprehensive decision support.

The best way to manage knowledge checks is using an exploration engine with DPO-inspired debate scoring. This protocol evaluates exchanges to detect gaps and clarify understanding across multiple phases.

An exploration workflow produces crystallized artifacts including exploration summaries and planning documents. These outputs feed directly into the next phases of structured decision workflows.

You should avoid this polymath approach for simple, well-defined tasks that lack ambiguity. It is designed for complex problems requiring multi-phase gap detection and trade-off management.