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Phase-Shifted Agentic Swarm (PSAS)

A multi-agent cognitive synchronization framework featuring strict physics anchoring, lateral topological probing, adversarial filtering, and an overarching destructive interference engine.

Designed to solve high-reliability engineering and material science problems by cross-validating competing cognitive frequencies and severing logical contradictions before final execution.

Core Architecture

  • Agent Alpha (Strict Physics Anchor): Relies strictly on known thermodynamic, material science, and mathematical constraints with zero speculation.
  • Agent Beta (Lateral Topology Probe): Explores edge cases, alternative geometric interpretations, and unusual topological mapping.
  • Agent Gamma (Adversarial Filter): Identifies logical fallacies, physical impossibilities, and structural constraint violations.
  • Overarching Awareness Engine (Interference Engine): Evaluates all three frequencies to calculate the Consensus Delta, applies destructive interference to delete flawed logic branches, and outputs the final verified truth state.

⚠️ Enterprise & Commercial Licensing Notice

PSAS is dual-licensed:

  • Open Source (AGPLv3): Free for non-commercial research, academic use, and open-source projects. Note: The AGPLv3 license requires any network-accessible service using this software to open-source its entire backend application code.
  • Commercial Enterprise License: Required for proprietary commercial deployments, closed-source SaaS platforms, and enterprise data center infrastructure. Commercial licenses grant full rights without AGPLv3 copyleft restrictions, plus integration support.

📩 For Enterprise Licensing Inquiries: inquiries@lawrencearchitectures.com

Parametric Validation Dataset

This repository includes a parametric verification script validating acoustic failure thresholds and structural limits for Monolithic Fused Quartz (SiO2) using Linear Elastic Fracture Mechanics (LEFM).

Usage

Set your Gemini API key in your environment or Colab secrets (GEMINI_API_KEY) and run the script:

python psas.py

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