{
  "hero": {
    "title": "Tractatus AI Safety Framework",
    "subtitle": "Some decisions require human judgment—architecturally enforced, not left to AI discretion, however well trained.<br>Not amoral AI systems, but plural moral values—enabling organizations to navigate value conflicts thoughtfully.<br><span class=\"text-lg opacity-90\">Now integrating with Agent Lightning for performance optimization.</span>",
    "cta_architecture": "System Architecture",
    "cta_docs": "Read Documentation",
    "cta_hf_space": "🤗 Explore Audit Logs",
    "cta_faq": "FAQ"
  },
  "community": {
    "heading": "Join the Community",
    "intro": "Connect with researchers, implementers, and leaders exploring agentic AI governance and Agent Lightning integration.",
    "tractatus_discord": {
      "title": "Tractatus Discord",
      "subtitle": "Governance-focused discussions",
      "description": "Explore architectural constraints, research gaps, and governance frameworks for agentic AI systems.",
      "cta": "Join Tractatus Server →"
    },
    "agent_lightning_discord": {
      "title": "Agent Lightning Discord",
      "subtitle": "Technical implementation help",
      "description": "Get support for Agent Lightning integration, RL optimization, and performance tuning questions.",
      "cta": "Join Agent Lightning Server →"
    },
    "welcome_message": "Both communities welcome researchers, implementers, and leaders at all experience levels."
  },
  "value_prop": {
    "heading": "A Starting Point",
    "text": "Aligning advanced AI with human values is among the most consequential challenges we face. As capability growth accelerates under big tech momentum, we confront a categorical imperative: preserve human agency over values decisions, or risk ceding control entirely.<br><br>Instead of hoping AI systems \"behave correctly,\" we propose structural constraints where certain decision types require human judgment. These architectural boundaries can adapt to individual, organizational, and societal norms—creating a foundation for bounded AI operation that may scale more safely with capability growth.<br><br>If this approach can work at scale, Tractatus may represent a turning point—a path where AI enhances human capability without compromising human sovereignty. Explore the framework through the lens that resonates with your work."
  },
  "alexander_principles": {
    "heading": "Built on Living Systems Principles",
    "subtitle": "Governance that evolves with your organization—not compliance theatre, but architectural enforcement woven into deployment.",
    "principles": {
      "deep_interlock": {
        "title": "Deep Interlock",
        "description": "Six governance services coordinate, not operate in silos. When one detects an issue, others reinforce—creating resilient enforcement through mutual validation."
      },
      "structure_preserving": {
        "title": "Structure-Preserving",
        "description": "Framework changes enhance without breaking. Audit logs remain interpretable, governance decisions stay valid—institutional memory preserved across evolution."
      },
      "gradients": {
        "title": "Gradients Not Binary",
        "description": "Governance operates on intensity levels (NORMAL/ELEVATED/HIGH/CRITICAL), not yes/no switches. Nuanced response to risk—avoiding alert fatigue and mechanical enforcement."
      },
      "living_process": {
        "title": "Living Process",
        "description": "Framework evolves from real failures, not predetermined plans. Grows smarter through operational experience—adaptive resilience, not static rulebook."
      },
      "not_separateness": {
        "title": "Not-Separateness",
        "description": "Governance woven into deployment architecture, not bolted on. Enforcement is structural, happening in the critical execution path before actions execute—bypasses require explicit flags and are logged."
      }
    },
    "cta_card": {
      "title": "Architectural Principles",
      "description": "These principles guide every framework change—ensuring coherence, adaptability, and structural enforcement rather than compliance theatre.",
      "architecture_link": "See Technical Architecture →",
      "values_link": "Values & Principles →"
    },
    "enforcement_distinction": {
      "heading": "Architectural Enforcement vs Compliance Theatre",
      "compliance_theatre": "Compliance theatre: Documented policies AI can bypass, post-execution monitoring, voluntary adherence.",
      "architectural_enforcement": "Architectural enforcement (Tractatus): Governance services intercept actions before execution in the critical path. Services coordinate in real-time, blocking non-compliant operations at the architectural level—bypasses require explicit --no-verify flags and are logged."
    }
  },
  "paths": {
    "intro": "",
    "researcher": {
      "title": "Researcher",
      "subtitle": "Academic & technical depth",
      "tooltip": "For AI safety researchers, academics, and scientists investigating LLM failure modes and governance architectures",
      "description": "Explore the theoretical foundations, architectural constraints, and scholarly context of the Tractatus framework.",
      "features": [
        "Technical specifications & proofs",
        "Academic research review",
        "Failure mode analysis",
        "Mathematical foundations"
      ],
      "cta": "Explore Research"
    },
    "implementer": {
      "title": "Implementer",
      "subtitle": "Code & integration guides",
      "tooltip": "For software engineers, ML engineers, and technical teams building production AI systems",
      "description": "Get hands-on with implementation guides, API documentation, and reference code examples.",
      "features": [
        "Working code examples",
        "API integration patterns",
        "Service architecture diagrams",
        "Deployment patterns & operational procedures"
      ],
      "cta": "View Implementation Guide"
    },
    "leader": {
      "title": "Leader",
      "subtitle": "Strategic AI Safety",
      "tooltip": "For AI executives, research directors, startup founders, and strategic decision makers setting AI safety policy",
      "description": "Navigate the business case, compliance requirements, and competitive advantages of structural AI safety.",
      "features": [
        "Executive briefing & business case",
        "Risk management & compliance (EU AI Act)",
        "Implementation roadmap & ROI",
        "Competitive advantage analysis"
      ],
      "cta": "View Leadership Resources"
    }
  },
  "capabilities": {
    "heading": "Framework Capabilities",
    "items": [
      {
        "title": "Instruction Classification",
        "description": "Quadrant-based classification (STR/OPS/TAC/SYS/STO) with time-persistence metadata tagging"
      },
      {
        "title": "Cross-Reference Validation",
        "description": "Validates AI actions against explicit user instructions to prevent pattern-based overrides"
      },
      {
        "title": "Boundary Enforcement",
        "description": "Implements Tractatus 12.1-12.7 boundaries - values decisions architecturally require humans"
      },
      {
        "title": "Pressure Monitoring",
        "description": "Detects degraded operating conditions (token pressure, errors, complexity) and adjusts verification"
      },
      {
        "title": "Metacognitive Verification",
        "description": "AI self-checks alignment, coherence, safety before execution - structural pause-and-verify"
      },
      {
        "title": "Pluralistic Deliberation",
        "description": "Multi-stakeholder values deliberation without hierarchy - facilitates human decision-making for incommensurable values"
      }
    ]
  },
  "validation": {
    "heading": "Real-World Validation",
    "performance_evidence": {
      "heading": "Preliminary Evidence: Safety and Performance May Be Aligned",
      "paragraph_1": "Production deployment reveals an unexpected pattern: <strong>structural constraints appear to enhance AI reliability rather than constrain it</strong>. Users report completing in one governed session what previously required 3-5 attempts with ungoverned Claude Code—achieving significantly lower error rates and higher-quality outputs under architectural governance.",
      "paragraph_2": "The mechanism appears to be <strong>prevention of degraded operating conditions</strong>: architectural boundaries stop context pressure failures, instruction drift, and pattern-based overrides before they compound into session-ending errors. By maintaining operational integrity throughout long interactions, the framework creates conditions for sustained high-quality output.",
      "paragraph_3": "<strong>If this pattern holds at scale</strong>, it challenges a core assumption blocking AI safety adoption—that governance measures trade performance for safety. Instead, these findings suggest structural constraints may be a path to <em>both</em> safer <em>and</em> more capable AI systems. Statistical validation is ongoing.",
      "methodology_note": "<strong>Methodology note:</strong> Findings based on qualitative user reports from production deployment. Controlled experiments and quantitative metrics collection scheduled for validation phase."
    },
    "case_27027": {
      "badge": "Pattern Bias Incident",
      "type": "Interactive Demo",
      "title": "The 27027 Incident",
      "description": "Real production incident where Claude Code defaulted to port 27017 (training pattern) despite explicit user instruction to use port 27027. CrossReferenceValidator detected the conflict and blocked execution—demonstrating how pattern recognition can override instructions under context pressure.",
      "why_matters": "Why this matters: This failure mode gets worse as models improve—stronger pattern recognition means stronger override tendency. Architectural constraints remain necessary regardless of capability level.",
      "cta": "View Interactive Demo"
    },
    "resources": {
      "text": "Additional case studies and research findings documented in technical papers",
      "cta": "Browse Case Studies →"
    }
  },
  "share_cta": {
    "heading": "Help us reach the right people.",
    "description": "If you know researchers, implementers, or leaders who need structural AI governance solutions, share this with them.",
    "copy_link": "Copy Link",
    "email": "Email",
    "linkedin": "LinkedIn"
  },
  "footer": {
    "about_heading": "Tractatus Framework",
    "about_text": "Architectural constraints for AI safety that preserve human agency through structural, not aspirational, enforcement.",
    "documentation_heading": "Documentation",
    "documentation_links": {
      "framework_docs": "Framework Docs",
      "about": "About",
      "core_values": "Core Values",
      "interactive_demo": "Interactive Demo"
    },
    "support_heading": "Support",
    "support_links": {
      "koha": "Support (Koha)",
      "transparency": "Transparency",
      "media_inquiries": "Media Inquiries",
      "submit_case": "Submit Case Study"
    },
    "legal_heading": "Legal",
    "legal_links": {
      "privacy": "Privacy Policy",
      "contact": "Contact Us",
      "github": "GitHub"
    },
    "te_tiriti_label": "Te Tiriti o Waitangi:",
    "te_tiriti_text": "We acknowledge Te Tiriti o Waitangi and our commitment to partnership, protection, and participation. This project respects Māori data sovereignty (rangatiratanga) and collective guardianship (kaitiakitanga).",
    "copyright": "John G Stroh. Licensed under",
    "license": "Apache 2.0",
    "location": "Made in Aotearoa New Zealand 🇳🇿"
  }
}