math-modeling-pipeline/phase-5.5

Multi-agent deep optimization is automated by coordinating six specialized advisor agents that fuse physics, data, and engineering insights to refine model performance and produce a consolidated optimization plan.

Consolidating cross-phase modeling results involves loading outputs from earlier phases, running six advisor agents for cross-agent reviews, and generating audit-ready optimization stories with a unified execution roadmap.

Automated model optimization pipelines require MATLAB R2024a and Python 3.12 to coordinate prompts, cross-phase inputs, evaluation, and logging for the six advisor agents.

Integrating brainstorming and reference materials is supported by loading Phase 1 through Phase 5 outputs into the workflow, allowing the six advisor agents to evaluate and fuse them into a deep optimization plan.

Generating validation artifacts and execution roadmaps is achieved by running the six advisor agents across the deep optimization workflow, which automatically logs cross-agent reviews and outputs a consolidated roadmap.

Deep optimization requires inputs from multiple earlier workflow phases because the six advisor agents need brainstorming, selected paths, reference materials, and audit feedback to evaluate model depth, novelty, and performance accurately.