Branching & Fork Control

Each Evolution Path (EP) undergoes clonal propagation across a fleet of parallel execution threads: the parent state vector is replicated into identical child trajectories, each submitted to the ideator ensemble for concurrent evaluation. A continuous divergence-detection module monitors agent-issued mutation kernels and architectural proposals; when the statistical variance between two child trajectories exceeds a predefined divergence threshold, the system automatically forks the branch—e.g., EPₐ bifurcates into EPₐ₁ and EPₐ₂—thereby growing a hierarchical tree of sub-branches.

Each fork inherits a MaxTry budget parameter, defining the upper bound on exploratory iterations or agent interactions permitted for that branch. Should a branch exhaust its MaxTry budget without surpassing a minimal fitness-improvement criterion, it is pruned to reclaim compute resources. This budget-gated forking strategy ensures both the preservation of promising evolutionary channels and the culling of unproductive search trajectories, maintaining high algorithmic efficiency and architectural diversity.