team-adversarial-swarm

Adversarial scoring validates multi-agent codebase analysis by applying prosecutor, defender, and judge roles to evaluate candidate paths. This debate-based judgment produces calibrated verified scores instead of relying on a single opinion.

Ant colony optimization drives iterative solution search by applying pheromone state persistence to track evidence-backed paths. It combines multi-agent parallel investigation with convergence control to decide when to stop or continue exploring.

Initialize the adversarial swarm workflow for suspicious-pattern discovery by generating a session config. Run iterative exploration, scoring, and convergence debate until the swarm produces a final report with calibrated results.

Use multi-agent convergence control when a single pass cannot adequately explore a task space. It suits codebase analysis, suspicious-pattern hunting, and solution comparison tasks needing parallel investigation with evidence-backed validation.

Yes, adversarial swarm optimization requires strict JSON ant outputs to maintain pheromone state persistence. This structured formatting enables the 3-vote scoring and convergence debate to produce calibrated results.

Pheromone-based workflow orchestration demands iterative exploration and 3-vote scoring cycles, making it slower than single-pass analysis. It adds overhead for simple decision tasks that do not require evidence-backed validation.