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<span class="audience-badge">Community Adopters Edition</span>
<h1>ARCHITECTURAL ALIGNMENT</h1>
<p class="subtitle">Community-Governed AI Through Constitutional Infrastructure</p>
<p class="tagline">A Framework for Sovereign Local Deployment</p>
<div class="article-meta">
<p><strong>Authors:</strong> John Stroh &amp; Claude (Anthropic)</p>
<p><strong>Document Code:</strong> STO-INN-0003 | <strong>Version:</strong> 2.1-C | January 2026</p>
</div>
</header>
<div class="version-links">
<h4>Also available in:</h4>
<p>
<a href="/architectural-alignment.html">Academic Research Edition</a> |
<a href="/architectural-alignment-policymakers.html">Policymakers Edition</a> |
<a href="/downloads/architectural-alignment-community.pdf">Download PDF</a>
</p>
</div>
<section class="executive-summary">
<h2>Executive Summary</h2>
<p>The question is no longer whether AI will be part of community life, but <strong>who will govern it when it arrives</strong>.</p>
<p>Current AI systems&mdash;whether cloud-based assistants or enterprise tools&mdash;operate under governance frameworks written by their vendors. Your community's values are accommodated only insofar as they don't conflict with platform policies designed for millions of other users. Your data informs systems you don't control. Your exit rights are limited to what the provider chooses to export.</p>
<p>This paper presents an alternative: <strong>constitutional governance for community-controlled AI</strong>. The Tractatus Framework implements explicit rules, defined by your community, that constrain what AI systems can do before any action is taken. This isn't about making AI less capable&mdash;it's about making AI accountable to the community it serves.</p>
<p>The framework is implemented in the Village platform and designed to support both cloud-based AI and locally-deployed systems. We introduce the concept of <strong>Sovereign Locally-trained Language Models (SLLs)</strong>&mdash;AI systems that run on community infrastructure, adapt to community norms, and operate under community-defined constitutions rather than vendor terms of service.</p>
</section>
<section class="key-points">
<h3>What This Means for Communities</h3>
<blockquote>
<p><strong>1. Your rules are the only rules.</strong> Constitutional constraints are defined by your community through democratic deliberation, not imposed by distant platform operators.</p>
<p><strong>2. Your data stays yours.</strong> AI memory, preferences, and learned patterns remain under community control, with full export rights.</p>
<p><strong>3. Transparency, not trust.</strong> Every AI action passes through auditable checkpoints. You don't have to trust that the vendor trained it right&mdash;you can see the rules it follows.</p>
<p><strong>4. Gradual autonomy.</strong> AI capabilities expand only as your community builds confidence, through staged progression from fully supervised to bounded autonomy.</p>
<p><strong>5. Real exit.</strong> If you leave, your governance structures, AI memory, and data leave with you.</p>
</blockquote>
<p>The underlying research addresses serious questions about AI safety and alignment. We believe communities benefit from understanding this context&mdash;not because your household AI poses existential risks, but because building governance capacity now prepares for a future where such capacity will matter more.</p>
</section>
<h2>1. The Problem: Who Governs Your AI?</h2>
<h3>1.1 The Current Reality</h3>
<p>When you use a cloud AI assistant&mdash;whether for writing, research, or community management&mdash;you're interacting with a system governed by rules you didn't write and can't change:</p>
<blockquote>
<p>Training decisions were made by researchers optimising for metrics you weren't consulted about</p>
<p>Safety constraints reflect corporate liability concerns, not your community's values</p>
<p>Data handling follows terms of service written by lawyers, not community deliberation</p>
<p>Capability boundaries are set by platform operators, not local governance</p>
<p>Exit rights are whatever the provider chooses to offer</p>
</blockquote>
<p>This isn't malicious. It's structural. Systems designed to serve millions of users cannot accommodate the specific values, norms, and governance preferences of each community. The result is AI governance by lowest common denominator.</p>
<h3>1.2 Why It Matters</h3>
<p>For many use cases, generic governance is adequate. A community using AI to schedule meetings doesn't need bespoke constitutional frameworks.</p>
<p>But some communities have legitimate needs that generic platforms cannot address:</p>
<p><strong>Cultural communities</strong> may have protocols about who can access certain knowledge, how ancestors are discussed, or what constitutes respectful engagement with cultural heritage.</p>
<p><strong>Family history communities</strong> deal with sensitive information about living people, contested narratives, and emotional content that requires context-specific handling.</p>
<p><strong>Professional communities</strong> may have ethical requirements, confidentiality obligations, or domain-specific norms that generic AI doesn't understand.</p>
<p><strong>Indigenous communities</strong> have collective rights over data, cultural authority over knowledge systems, and governance traditions that predate and don't map onto Western corporate frameworks.</p>
<p><strong>Privacy-conscious communities</strong> may want guarantees about data handling that exceed what commercial platforms offer.</p>
<p>For these communities, governance isn't a nice-to-have. It's essential.</p>
<h3>1.3 The Coming Shift</h3>
<p>The AI landscape is changing in ways that create new possibilities:</p>
<p>Industry research indicates that <strong>72% of enterprise executives expect small language models to surpass large language models in prominence by 2030</strong> (IBM Institute for Business Value, 2026). This suggests a future where capable AI runs on local hardware&mdash;home servers, community infrastructure, edge devices&mdash;rather than exclusively in distant data centres.</p>
<p>This shift matters because <strong>local deployment enables local governance</strong>. When AI runs on your infrastructure, under your control, you can implement governance frameworks that reflect your community's values rather than a vendor's policy preferences.</p>
<h2>2. The Tractatus Framework: Governance Through Architecture</h2>
<h3>2.1 The Core Idea</h3>
<p>Instead of trusting that AI was trained to behave appropriately, Tractatus requires AI systems to <strong>propose actions explicitly</strong> and have them <strong>evaluated against rules before execution</strong>.</p>
<p style="text-align: center; font-family: monospace; background: #f9fafb; padding: 1rem; border-radius: 8px; margin: 1.5rem 0;">
Your Request &rarr; [AI generates response] &rarr; Structured Proposal &rarr; [Constitutional Gate checks against your rules] &rarr; Permitted / Denied / Escalated to human review
</p>
<p>Every significant AI action passes through this checkpoint. The rules are explicit, inspectable, and defined by your community.</p>
<h3>2.2 What This Looks Like in Practice</h3>
<p><strong>Example: A family history community</strong></p>
<p>A member asks the AI to help write a remembrance for a recently deceased relative.</p>
<blockquote>
<p><strong>1.</strong> The AI generates a proposed response</p>
<p><strong>2.</strong> The constitutional gate checks against community rules:</p>
<p>&nbsp;&nbsp;&bull; Is this about a death within the past year? (Triggers sensitivity protocols)</p>
<p>&nbsp;&nbsp;&bull; Does the community constitution specify cultural requirements for discussing the deceased?</p>
<p>&nbsp;&nbsp;&bull; Does the individual member have preferences about how AI discusses their family?</p>
<p><strong>3.</strong> The gate applies relevant rules:</p>
<p>&nbsp;&nbsp;&bull; Use more gentle phrasing (community rule)</p>
<p>&nbsp;&nbsp;&bull; Offer to involve a human moderator (escalation threshold)</p>
<p>&nbsp;&nbsp;&bull; Respect the member's preference for private vs. communal remembrance</p>
<p><strong>4.</strong> The response is delivered&mdash;or flagged for human review</p>
</blockquote>
<p>The AI didn't decide these rules. Your community did. Through conversation, through voting, through deliberation.</p>
<h3>2.3 Layered Constitutions</h3>
<p>Rules are organised in layers, each with appropriate authority:</p>
<table>
<thead>
<tr><th>Layer</th><th>Who Defines It</th><th>What It Covers</th><th>How It Changes</th></tr>
</thead>
<tbody>
<tr><td>Core Principles</td><td>Platform-wide</td><td>Fundamental safety; legal compliance</td><td>Rarely; requires broad consensus</td></tr>
<tr><td>Community Constitution</td><td>Your community</td><td>Values, norms, policies specific to your context</td><td>Community deliberation and vote</td></tr>
<tr><td>Individual Preferences</td><td>Each member</td><td>Personal interaction style, privacy choices</td><td>Self-service configuration</td></tr>
</tbody>
</table>
<p>Higher layers take precedence, but within those constraints, your community has genuine authority.</p>
<h3>2.4 Progressive Autonomy</h3>
<p>AI capabilities don't arrive fully-formed. They're earned through demonstrated trustworthiness:</p>
<table>
<thead>
<tr><th>Stage</th><th>AI Authority</th><th>Human Role</th><th>Duration</th></tr>
</thead>
<tbody>
<tr><td>Shadow</td><td>Observes and proposes; takes no action</td><td>Approves everything</td><td>Until confident in proposals</td></tr>
<tr><td>Advisory</td><td>Recommendations surfaced</td><td>Retains full authority</td><td>Until acceptance rate stable</td></tr>
<tr><td>Supervised</td><td>Acts within narrow scope</td><td>Reviews all actions within 24h</td><td>Until error rate acceptable</td></tr>
<tr><td>Bounded</td><td>Acts within defined boundaries</td><td>Reviews samples and edge cases</td><td>Ongoing</td></tr>
<tr><td>Operational</td><td>Full authority at defined level</td><td>Focuses on outcomes</td><td>Ongoing with audit</td></tr>
</tbody>
</table>
<p>Your community decides when to progress&mdash;or regress if problems emerge. The AI earns trust; it doesn't assume it.</p>
<h2>3. Sovereign Local AI: The SLL Concept</h2>
<h3>3.1 What is an SLL?</h3>
<p>We introduce the term <strong>Sovereign Locally-trained Language Model (SLL)</strong> to describe AI systems with specific properties:</p>
<blockquote>
<p><strong>Local deployment:</strong> Runs on your infrastructure&mdash;a home server, community hardware, or local data centre&mdash;not a vendor's cloud</p>
<p><strong>Local adaptation:</strong> Fine-tuned on your community's data and norms, not generic training</p>
<p><strong>Local governance:</strong> Subject to your constitutional rules, not vendor terms of service</p>
<p><strong>Portable sovereignty:</strong> Can connect to larger networks without surrendering governance authority</p>
</blockquote>
<p>An SLL isn't just a small model that happens to run locally. It's an architectural commitment to sovereignty.</p>
<h3>3.2 Why Sovereignty Matters</h3>
<p>Sovereignty in this context doesn't mean isolation. It means the capacity to participate in larger networks on your own terms.</p>
<table>
<thead>
<tr><th>Dimension</th><th>Vendor-Hosted AI</th><th>Sovereign SLL</th></tr>
</thead>
<tbody>
<tr><td>Where it runs</td><td>Vendor's cloud; you don't know where</td><td>Your infrastructure; you control location</td></tr>
<tr><td>Who sets the rules</td><td>Vendor ToS + whatever law applies to them</td><td>Your constitution + your jurisdiction's law</td></tr>
<tr><td>What it learns from</td><td>Aggregated data from all users</td><td>Your community's data, under your control</td></tr>
<tr><td>Who can change behaviour</td><td>Vendor, unilaterally</td><td>Your community, through governance</td></tr>
<tr><td>What happens if you leave</td><td>Limited export; lose AI context</td><td>Full export; AI memory is yours</td></tr>
</tbody>
</table>
<p>Sovereignty means real exit rights. If your governance framework isn't working, you can take your data, your AI's learned patterns, and your constitutional rules, and move them elsewhere.</p>
<h3>3.3 The Trade-offs</h3>
<p>Sovereignty comes with real costs:</p>
<p><strong>You accept:</strong></p>
<blockquote>
<p>Potentially lower raw capability than frontier cloud models</p>
<p>Higher infrastructure complexity (someone has to run the servers)</p>
<p>More explicit governance work (constitutions don't write themselves)</p>
</blockquote>
<p><strong>In exchange for:</strong></p>
<blockquote>
<p>Complete data sovereignty</p>
<p>Governance that reflects your community's values</p>
<p>Real exit rights</p>
<p>Transparency about what the AI actually does</p>
</blockquote>
<p>This trade-off isn't right for everyone. Many communities are well-served by cloud AI with vendor governance. The point is that the choice should be yours, not forced by technological constraints.</p>
<h2>4. Constitutional Governance in Action</h2>
<h3>4.1 The Governance Pipeline</h3>
<p>The Village platform implements constitutional governance through a six-stage verification pipeline. Every AI response passes through:</p>
<p><strong>1. Intent Recognition</strong> &mdash; What kind of request is this? Does it involve values, facts, or actions? Route to appropriate handling.</p>
<p><strong>2. Boundary Enforcement</strong> &mdash; Hard constraints that cannot be overridden. The AI never tells you what to think about values. The AI never makes governance decisions.</p>
<p><strong>3. Pressure Monitoring</strong> &mdash; Is the AI operating under degraded conditions? If confidence is low, acknowledge uncertainty. Escalate when appropriate.</p>
<p><strong>4. Response Verification</strong> &mdash; Does the response actually address the request? Is it complete and structurally sound? Pre-flight checks before delivery.</p>
<p><strong>5. Source Validation</strong> &mdash; Are claims grounded in verifiable sources? The AI doesn't present training data as fact. Citations where appropriate.</p>
<p><strong>6. Value Deliberation</strong> &mdash; Does the request involve value tensions? Present balanced options rather than recommendations. For community decisions, suggest using democratic processes.</p>
<p>This pipeline is operational. Every response you receive has passed through it.</p>
<h3>4.2 Democratic Deliberation</h3>
<p>Constitutional rules aren't handed down from above. They emerge from community deliberation:</p>
<p><strong>Consent-Based Voting:</strong> Not just yes/no, but a spectrum: Enthusiastic Support, Support, Consent (can live with it), Stand Aside, Object. Objections require rationale and trigger discussion&mdash;they're invitations to address concerns, not vetoes.</p>
<p><strong>Ranked Choice:</strong> When multiple options exist, rank your preferences. Your second choice matters if your first can't win. This prevents the spoiler effects that silence minority views.</p>
<p><strong>Quadratic Voting:</strong> For decisions where preference intensity matters, voice credits let you express how much you care. Spend more on issues that matter deeply; less on those where you're indifferent.</p>
<p><strong>Phased Deliberation:</strong> Important decisions move through stages:</p>
<blockquote>
<p>1. Discussion Phase: Share perspectives, no voting pressure</p>
<p>2. Preliminary Vote: Temperature check on emerging consensus</p>
<p>3. Final Vote: Binding decision with full participation</p>
</blockquote>
<p><strong>AI Assists, Never Decides:</strong> The AI can help with deliberation&mdash;summarising threads, highlighting patterns, suggesting when consensus is emerging. But it never votes, never decides when discussion is complete, never creates policy without community approval.</p>
<h2>5. Why This Architecture?</h2>
<h3>5.1 The Deeper Context</h3>
<p>This framework emerges from serious research on AI safety and alignment. The core insight:</p>
<p><strong>Training AI to be safe isn't sufficient.</strong> We can't verify what's inside a neural network. We can't prove it will behave as intended under conditions not encountered during training. We can't guarantee that systems optimised for helpfulness won't develop unintended behaviours.</p>
<p><strong>Architecture provides guarantees that training cannot.</strong> By requiring AI to propose actions explicitly, by evaluating those proposals against explicit rules, by logging everything for audit&mdash;we create visible, enforceable constraints that don't depend on trusting the AI's internal state.</p>
<p>This matters more as AI systems become more capable. The patterns developed now&mdash;at village scale, for manageable stakes&mdash;become the patterns available when stakes are higher.</p>
<h3>5.2 Not Just Governance, But Preparation</h3>
<p>We don't claim your household AI poses existential risks. We do believe that:</p>
<blockquote>
<p>1. AI systems will become more capable</p>
<p>2. More capable systems will require more robust governance</p>
<p>3. Governance capacity can't be created instantly when needed</p>
<p>4. Building governance infrastructure now prepares for futures we cannot fully predict</p>
</blockquote>
<p>Constitutional governance for community AI is practice. It develops the tools, the expertise, the governance culture, and the democratic capacity that may matter more later.</p>
<p>This is preparation, not prediction. We don't know what the future holds. We do know that having governance infrastructure is better than not having it.</p>
<h2>6. Indigenous Sovereignty</h2>
<h3>6.1 First Principles</h3>
<p>The Village platform is developed in Aotearoa New Zealand, under Te Tiriti o Waitangi. This context shapes our approach:</p>
<p><strong>Sovereignty isn't new.</strong> Many ideas now emerging under "digital sovereignty" were articulated first by indigenous leaders: collective rights over data, self-determination in how knowledge is used, guardianship rather than ownership.</p>
<p><strong>Te Tiriti creates constitutional obligations.</strong> Article Two guarantees M&#257;ori tino rangatiratanga (self-determination) over taonga (treasures), which includes language, culture, and knowledge systems. Data is taonga. AI trained on data, and systems that process it, engage these obligations.</p>
<p><strong>Te Mana Raraunga principles</strong> guide M&#257;ori data sovereignty:</p>
<blockquote>
<p><strong>Rangatiratanga:</strong> M&#257;ori authority over M&#257;ori data</p>
<p><strong>Whakapapa:</strong> Data exists in relational context</p>
<p><strong>Whanaungatanga:</strong> Governance is collective, not just individual</p>
<p><strong>Kaitiakitanga:</strong> Custodians have guardianship responsibilities</p>
</blockquote>
<h3>6.2 Implications for Constitutional Governance</h3>
<p>Tractatus' layered constitution architecture can accommodate these requirements:</p>
<blockquote>
<p>Community constitutions can instantiate tikanga-based rules</p>
<p>Collective consent can be encoded alongside individual consent</p>
<p>Cultural authority can be recognised in governance structures</p>
<p>Benefit-sharing can be specified as constitutional constraint</p>
</blockquote>
<p>This doesn't solve all problems. Platform-level accommodation is not a substitute for legislative recognition. But it demonstrates that constitutional governance can respect rather than override indigenous sovereignty.</p>
<h2>7. Practical Considerations</h2>
<h3>7.1 What You Need</h3>
<p><strong>For cloud-hosted Village tenancy:</strong></p>
<blockquote>
<p>Internet connection</p>
<p>Governance capacity (someone to configure and maintain constitutions)</p>
<p>Community commitment to deliberative process</p>
</blockquote>
<p><strong>For sovereign SLL deployment:</strong></p>
<blockquote>
<p>Local server infrastructure (home server or community hardware)</p>
<p>Technical capacity for deployment and maintenance</p>
<p>Governance capacity as above</p>
</blockquote>
<p>Village supports both modes. Communities can start with cloud hosting and migrate to sovereign deployment as capacity develops.</p>
<h3>7.2 What It Costs</h3>
<p><strong>Infrastructure:</strong> Non-trivial for sovereign deployment. Comparable to running any local server infrastructure.</p>
<p><strong>Governance:</strong> Real time investment. Constitutions need to be developed, deliberation takes time, rules need maintenance.</p>
<p><strong>Capability trade-off:</strong> Local models may not match frontier cloud models on raw capability. Hybrid approaches (local for most tasks, cloud fallback for complex requests) can mitigate this.</p>
<h3>7.3 What You Give Up</h3>
<blockquote>
<p>Seamless integration with other platforms (sovereignty means boundaries)</p>
<p>"It just works" simplicity (governance requires attention)</p>
<p>Cutting-edge capabilities on day one (local models lag frontier)</p>
<p>Vendor-managed updates (you're responsible for your infrastructure)</p>
</blockquote>
<h3>7.4 What You Gain</h3>
<blockquote>
<p>Your rules are actually your rules</p>
<p>Your data stays under your control</p>
<p>Full transparency about AI behaviour</p>
<p>Real exit rights</p>
<p>Governance that reflects your community's values</p>
<p>Capacity building for futures we can't predict</p>
</blockquote>
<h2>8. Honest Limitations</h2>
<h3>8.1 What Constitutional Governance Cannot Do</h3>
<p><strong>Guarantee perfect AI behaviour.</strong> Architecture constrains actions; it doesn't make AI wise or kind.</p>
<p><strong>Replace human judgment.</strong> The framework creates checkpoints for human review; it doesn't eliminate the need for human attention.</p>
<p><strong>Scale to superintelligent systems.</strong> The framework assumes AI operating within human-comprehensible parameters. It is not designed for systems that exceed human understanding.</p>
<p><strong>Force community agreement.</strong> Constitutional governance requires communities to do the work of deliberation. It doesn't manufacture consensus.</p>
<h3>8.2 What We're Still Learning</h3>
<blockquote>
<p>Optimal balance between inherited rules and local customisation</p>
<p>How to prevent governance fatigue while enabling genuine choice</p>
<p>How community norms should evolve as AI capabilities change</p>
<p>Sustainable cost models for sovereign deployment</p>
</blockquote>
<h3>8.3 Our Commitment</h3>
<p>We commit to:</p>
<blockquote>
<p>Continuous improvement based on community experience</p>
<p>Full transparency about how the system works</p>
<p>Real exit rights that don't trap communities</p>
<p>Honest acknowledgment of limitations</p>
</blockquote>
<p>We don't promise to match frontier model capabilities on day one. We don't promise that governance will be effortless. We do promise that your community will have genuine authority over AI that serves it.</p>
<h2>9. Getting Started</h2>
<h3>9.1 For Communities Considering Village</h3>
<blockquote>
<p>1. <strong>Assess your needs.</strong> Does your community have governance requirements that generic platforms can't meet?</p>
<p>2. <strong>Evaluate capacity.</strong> Do you have people willing to invest in constitutional development and maintenance?</p>
<p>3. <strong>Start with cloud.</strong> Begin with hosted Village tenancy; sovereign deployment can come later.</p>
<p>4. <strong>Develop incrementally.</strong> Start with platform defaults; customise as you learn what your community needs.</p>
<p>5. <strong>Engage deliberatively.</strong> Constitutional governance works when communities actually deliberate. Build that culture.</p>
</blockquote>
<h3>9.2 For Communities Considering Sovereign SLL</h3>
<blockquote>
<p>1. <strong>Build on Village experience first.</strong> Understand constitutional governance before adding infrastructure complexity.</p>
<p>2. <strong>Assess technical capacity.</strong> Can you deploy and maintain server infrastructure?</p>
<p>3. <strong>Plan for hybrid.</strong> Most communities benefit from local models for routine tasks with cloud fallback for complex requests.</p>
<p>4. <strong>Budget realistically.</strong> Infrastructure costs are real; they don't disappear.</p>
<p>5. <strong>Contribute back.</strong> Sovereign deployment generates insights valuable to the broader community.</p>
</blockquote>
<h3>9.3 For Researchers and Developers</h3>
<p>The Tractatus Framework is documented for external research. We welcome:</p>
<blockquote>
<p>Independent analysis of governance patterns</p>
<p>Proposals for improved validation methodology</p>
<p>Contributions to open-source governance tooling</p>
<p>Critique that helps us improve</p>
</blockquote>
<h2>10. Conclusion</h2>
<p>AI is coming to communities whether communities prepare or not. The question is whether that AI will be governed by vendor terms of service, by constitutional frameworks reflecting community values, or by nothing at all.</p>
<p>The Tractatus Framework offers one answer: architectural governance that makes AI accountable to the communities it serves. Not through trust in vendor training, but through visible, auditable, democratically-determined rules.</p>
<p>This isn't the only answer. It involves real trade-offs. But for communities that value sovereignty&mdash;the capacity to participate in larger networks without surrendering local control&mdash;it offers something generic platforms cannot: governance that is genuinely yours.</p>
<p>We offer this framework in the spirit of contribution to a larger conversation about how communities can maintain agency in an age of powerful AI. The problems are hard. The answers are provisional. The conversation must continue.</p>
<div class="maori-proverb">
<blockquote>
<p><em>"He aha te mea nui o te ao? He tangata, he tangata, he tangata."</em></p>
<p><em>(What is the greatest thing in the world? It is people, it is people, it is people.)</em></p>
<p>&mdash;M&#257;ori proverb</p>
</blockquote>
</div>
<h2 class="references">References</h2>
<div class="references">
<p>IBM Institute for Business Value. (2026). <em>The enterprise in 2030</em>. IBM Corporation.</p>
<p>Te Mana Raraunga. (2018). <em>M&#257;ori Data Sovereignty Principles</em>. Te Mana Raraunga &ndash; M&#257;ori Data Sovereignty Network.</p>
<p>Research Institute for Indigenous Data Sovereignty. (2019). <em>CARE Principles for Indigenous Data Governance</em>. Global Indigenous Data Alliance.</p>
<p>Bostrom, N. (2014). <em>Superintelligence: Paths, Dangers, Strategies</em>. Oxford University Press.</p>
<p>Hubinger, E., van Merwijk, C., Mikulik, V., Skalse, J., &amp; Garrabrant, S. (2019). Risks from learned optimization in advanced machine learning systems. arXiv preprint arXiv:1906.01820.</p>
<p>Alexander, C., Ishikawa, S., &amp; Silverstein, M. (1977). <em>A Pattern Language: Towns, Buildings, Construction</em>. Oxford University Press.</p>
</div>
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<span class="audience-badge">Policymakers Edition</span>
<h1>ARCHITECTURAL ALIGNMENT</h1>
<p class="subtitle">Constitutional Governance for Distributed AI Systems</p>
<p class="tagline">A Framework for Regulatory and Policy Development</p>
<div class="article-meta">
<p><strong>Authors:</strong> John Stroh &amp; Claude (Anthropic)</p>
<p><strong>Document Code:</strong> STO-INN-0003 | <strong>Version:</strong> 2.1-P | January 2026</p>
</div>
</header>
<div class="version-links">
<h4>Also available in:</h4>
<p>
<a href="/architectural-alignment.html">Academic Research Edition</a> |
<a href="/architectural-alignment-community.html">Community Adopters Edition</a> |
<a href="/downloads/architectural-alignment-policymakers.pdf">Download PDF</a>
</p>
</div>
<section class="executive-summary">
<h2>Executive Summary</h2>
<p>AI deployment is outpacing regulatory capacity. While policymakers debate frameworks for large language models operated by major technology companies, a parallel transformation is underway: the migration of AI capabilities to <strong>small, locally-deployed models in homes, communities, and small organisations</strong>. Recent industry research indicates that 72% of enterprise executives expect small language models (SLMs) to surpass large language models in prominence by 2030 (IBM Institute for Business Value, 2026). This shift creates an urgent governance challenge: <strong>who controls AI deployed at the edge, and under what rules?</strong></p>
<p>This paper presents the Tractatus Framework, an architectural approach to AI governance through <strong>inference-time constitutional gating</strong>. Rather than relying solely on vendor training to ensure AI behaves appropriately, Tractatus requires AI systems to translate proposed actions into auditable forms and evaluate them against explicit constitutional rules before execution. This creates visible, enforceable governance at the point of deployment.</p>
<p>The framework is implemented in the Village platform and designed to accommodate both centralised cloud AI and distributed local deployments, including what we term <strong>Sovereign Locally-trained Language Models (SLLs)</strong>&mdash;AI systems whose training, deployment, and governance remain under community or individual sovereignty rather than vendor control.</p>
</section>
<div class="key-policy">
<h3>Key Policy Implications</h3>
<blockquote>
<p><strong>1. The governance vacuum is filling by default.</strong> In the absence of regulatory frameworks, AI governance is determined by vendor terms of service and platform defaults. This concentrates governance authority in a small number of corporations.</p>
<p><strong>2. Architectural requirements may be more enforceable than behavioural requirements.</strong> Mandating that AI systems implement constitutional gating is more verifiable than mandating that AI systems "be safe" or "respect values."</p>
<p><strong>3. Certification infrastructure is needed.</strong> As SLM/SLL deployment scales, standards bodies, training providers, and validation methodologies will be required&mdash;analogous to existing certification regimes in aviation, medical devices, and financial services.</p>
<p><strong>4. Indigenous data sovereignty is a constitutional matter.</strong> In Aotearoa New Zealand and other jurisdictions with indigenous rights frameworks, AI governance must accommodate collective rights over data and culturally-specific governance requirements.</p>
<p><strong>5. Preparation must precede capability.</strong> Governance frameworks for advanced AI cannot be developed after such systems exist. Building constitutional infrastructure at accessible scales now creates the foundation for higher-stakes governance later.</p>
</blockquote>
</div>
<h2>1. The Governance Gap</h2>
<h3>1.1 Regulatory Lag</h3>
<p>AI capabilities are advancing faster than governance frameworks can respond. The EU AI Act, while a significant first step, was designed primarily for large-scale systems deployed by identifiable operators. It does not adequately address:</p>
<blockquote>
<p><strong>Edge deployment:</strong> AI systems running on personal devices or home servers outside traditional regulatory reach</p>
<p><strong>Federated architectures:</strong> Distributed systems where no single operator controls the complete system</p>
<p><strong>Continuous adaptation:</strong> Models that learn from local data and evolve post-deployment</p>
<p><strong>Community governance:</strong> Situations where appropriate rules vary by cultural context, community values, or individual preferences</p>
</blockquote>
<h3>1.2 The Coming Wave of Distributed AI</h3>
<p>Industry projections indicate a fundamental shift in AI deployment patterns:</p>
<table>
<thead>
<tr><th>Indicator</th><th>Current State</th><th>2030 Projection</th><th>Source</th></tr>
</thead>
<tbody>
<tr><td>AI contribution to revenue</td><td>40% report significant contribution</td><td>79% expect significant contribution</td><td>IBM IBV 2026, p.13</td></tr>
<tr><td>SLM prominence vs LLM</td><td>LLMs dominant in enterprise</td><td>72% expect SLMs more prominent</td><td>IBM IBV 2026, p.32</td></tr>
<tr><td>AI-driven productivity</td><td>Early adoption</td><td>42% productivity increase expected</td><td>IBM IBV 2026, p.21</td></tr>
<tr><td>Operating margin improvement</td><td>Variable</td><td>55% higher for multi-model orgs</td><td>IBM IBV 2026, p.32</td></tr>
</tbody>
</table>
<p>These projections suggest that within five years, AI deployment will be characterised by numerous small, domain-specific models rather than a few large centralised systems. This has profound governance implications:</p>
<blockquote>
<p><strong>Scale of oversight:</strong> Thousands of distinct deployments rather than dozens</p>
<p><strong>Locus of control:</strong> Community and individual operators rather than large corporations</p>
<p><strong>Regulatory jurisdiction:</strong> Models operating across borders with no clear home jurisdiction</p>
<p><strong>Enforcement mechanism:</strong> Traditional regulatory inspection may be infeasible at scale</p>
</blockquote>
<h3>1.3 The Default Governance Regime</h3>
<p>In the absence of explicit regulatory frameworks, AI governance defaults to:</p>
<blockquote>
<p><strong>1. Vendor terms of service:</strong> Corporate policies created to limit liability, not to serve user or community interests</p>
<p><strong>2. Platform architectural choices:</strong> Governance embedded in technical infrastructure, invisible to users</p>
<p><strong>3. Market pressure:</strong> Systems optimised for engagement and revenue rather than safety or sovereignty</p>
</blockquote>
<p>This is not a neutral outcome. It concentrates governance authority in entities whose interests may diverge from those of users, communities, and the public.</p>
<h2>2. Architectural Governance: A Regulatory Strategy</h2>
<h3>2.1 The Limits of Behavioural Regulation</h3>
<p>Traditional regulation specifies prohibited outcomes: AI systems must not discriminate, deceive, or cause harm. This approach faces fundamental challenges:</p>
<blockquote>
<p><strong>Verification difficulty:</strong> How does a regulator determine whether an AI system "discriminates" without extensive testing that may miss edge cases?</p>
<p><strong>Definition ambiguity:</strong> What constitutes "harm" varies by context; systems optimised for one definition may fail others</p>
<p><strong>Opacity:</strong> Neural network decision-making cannot be directly audited; only inputs and outputs are observable</p>
<p><strong>Scale:</strong> Behavioural testing of thousands of distributed deployments is practically infeasible</p>
</blockquote>
<h3>2.2 Architectural Requirements</h3>
<p>An alternative regulatory strategy specifies <strong>required architecture</strong> rather than prohibited behaviour:</p>
<p><strong>Constitutional Gating Requirement:</strong> AI systems with specified capabilities must implement inference-time constitutional gating&mdash;a mechanism that:</p>
<blockquote>
<p>1. Transforms model outputs into structured proposals with defined schemas</p>
<p>2. Evaluates proposals against explicit constitutional rules before execution</p>
<p>3. Logs all proposals, evaluations, and dispositions for audit</p>
<p>4. Escalates ambiguous cases to human review</p>
</blockquote>
<p>This approach has several advantages:</p>
<blockquote>
<p><strong>Verifiability:</strong> The presence of constitutional gating infrastructure can be audited; behavioural compliance cannot</p>
<p><strong>Transparency:</strong> Constitutional rules are explicit and inspectable; training-time alignment is opaque</p>
<p><strong>Flexibility:</strong> Different communities can implement different constitutional rules within a common architectural framework</p>
<p><strong>Auditability:</strong> Logged proposals and evaluations provide an audit trail for incident investigation</p>
</blockquote>
<h3>2.3 The Tractatus Framework</h3>
<p>The Tractatus Framework implements architectural governance through:</p>
<p><strong>Interrupted Inference:</strong> Model outputs do not directly affect the world. They are first translated into structured proposals and evaluated against constitutional constraints:</p>
<p style="text-align: center; font-family: monospace; background: #f9fafb; padding: 1rem; border-radius: 8px; margin: 1.5rem 0;">
User Request &rarr; [AI Model] &rarr; Proposal &rarr; [Constitutional Gate] &rarr; Action/Denial/Escalation
</p>
<p><strong>Layered Constitutions:</strong> Rules are organised in hierarchical layers with explicit precedence:</p>
<table>
<thead>
<tr><th>Layer</th><th>Scope</th><th>Authority</th><th>Examples</th></tr>
</thead>
<tbody>
<tr><td>Core Principles</td><td>Universal</td><td>Immutable</td><td>No harm; data sovereignty; consent primacy</td></tr>
<tr><td>Platform Rules</td><td>All deployments</td><td>Amendment by supermajority</td><td>Authentication; audit retention</td></tr>
<tr><td>Community Constitution</td><td>Per community</td><td>Local governance</td><td>Content policies; cultural protocols</td></tr>
<tr><td>Individual Preferences</td><td>Per user</td><td>Self-governed</td><td>Communication style; AI memory consent</td></tr>
</tbody>
</table>
<p><strong>Authority Model:</strong> AI systems operate at defined authority levels, each specifying what actions are permitted without human approval:</p>
<table>
<thead>
<tr><th>Level</th><th>Description</th><th>Human Role</th></tr>
</thead>
<tbody>
<tr><td>Advisory</td><td>All actions require human approval</td><td>Full authority</td></tr>
<tr><td>Operational</td><td>Routine actions within defined scope</td><td>Exception review</td></tr>
<tr><td>Tactical</td><td>Scoped decisions affecting workflows</td><td>Outcome oversight</td></tr>
</tbody>
</table>
<p><strong>Audit Infrastructure:</strong> All proposals, evaluations, and actions are logged with sufficient detail for post-hoc investigation.</p>
<h2>3. The SLM/SLL Distinction</h2>
<h3>3.1 Terminology</h3>
<p>We distinguish two deployment paradigms that have different governance implications:</p>
<p><strong>Small Language Model (SLM):</strong> A technical descriptor for language models with fewer parameters than frontier LLMs, designed for efficiency and domain-specific deployment. SLMs may be deployed via cloud subscription or locally.</p>
<p><strong>Sovereign Locally-trained Language Model (SLL):</strong> An architectural descriptor we introduce for AI systems whose training, deployment, and governance remain under local sovereignty. Key properties:</p>
<blockquote>
<p><strong>Local deployment:</strong> Runs on home or community infrastructure</p>
<p><strong>Local adaptation:</strong> Fine-tuned on community-specific data</p>
<p><strong>Local governance:</strong> Subject to community-defined constitutions</p>
<p><strong>Portable sovereignty:</strong> Can participate in federated networks without surrendering governance authority</p>
</blockquote>
<h3>3.2 Governance Implications</h3>
<table>
<thead>
<tr><th>Dimension</th><th>Subscription SLM</th><th>Sovereign SLL</th></tr>
</thead>
<tbody>
<tr><td>Regulatory touchpoint</td><td>Vendor/platform operator</td><td>End deployer/community</td></tr>
<tr><td>Applicable rules</td><td>Vendor ToS + jurisdiction law</td><td>Local constitution + law</td></tr>
<tr><td>Enforcement mechanism</td><td>Platform policy; regulatory action against vendor</td><td>Local governance; community accountability</td></tr>
<tr><td>Data jurisdiction</td><td>Vendor infrastructure (often unclear)</td><td>Local infrastructure (clear jurisdiction)</td></tr>
<tr><td>Amendment authority</td><td>Vendor unilaterally</td><td>Community democratically</td></tr>
<tr><td>Exit rights</td><td>Limited; lose AI context</td><td>Full; AI memory portable</td></tr>
</tbody>
</table>
<h3>3.3 Policy Implications</h3>
<p>The SLL paradigm creates both opportunities and challenges for policymakers:</p>
<p><strong>Opportunities:</strong></p>
<blockquote>
<p><strong>Sovereignty preservation:</strong> Communities can maintain governance authority over AI affecting them</p>
<p><strong>Regulatory diversity:</strong> Different jurisdictions can implement different governance approaches</p>
<p><strong>Democratic legitimacy:</strong> Governance rules can be developed through community deliberation</p>
<p><strong>Accountability clarity:</strong> Clear relationship between deployer, governance, and jurisdiction</p>
</blockquote>
<p><strong>Challenges:</strong></p>
<blockquote>
<p><strong>Enforcement at scale:</strong> Traditional regulatory inspection may be infeasible for thousands of home deployments</p>
<p><strong>Capability creep:</strong> Local fine-tuning may create capabilities not anticipated by original safety assessments</p>
<p><strong>Coordination failure:</strong> Fragmented governance may leave gaps or create inconsistencies</p>
<p><strong>Technical barriers:</strong> Not all communities have capacity to implement sophisticated governance</p>
</blockquote>
<h2>4. A Multi-Layer Containment Framework</h2>
<h3>4.1 The Inadequacy of Single-Layer Approaches</h3>
<p>No single governance mechanism is adequate for AI systems at existential stakes. Defence in depth&mdash;multiple independent layers, any one of which might prevent serious harm&mdash;is standard in nuclear safety, aviation, and biosecurity. AI governance requires similar architecture.</p>
<h3>4.2 Five-Layer Model</h3>
<table>
<thead>
<tr><th>Layer</th><th>Function</th><th>Primary Actors</th><th>Current State</th></tr>
</thead>
<tbody>
<tr><td>1. Capability Constraints</td><td>Limit what AI can do regardless of intent</td><td>Hardware vendors; compute providers</td><td>Emerging (compute governance)</td></tr>
<tr><td>2. Constitutional Gates</td><td>Evaluate actions against explicit rules at inference time</td><td>Platform operators; community governance</td><td>Nascent (Tractatus is early implementation)</td></tr>
<tr><td>3. Human Oversight</td><td>Monitor AI systems; intervene when needed</td><td>Professional reviewers; community moderators</td><td>Ad hoc</td></tr>
<tr><td>4. Organisational Governance</td><td>Internal accountability structures</td><td>Deploying organisations</td><td>Inconsistent</td></tr>
<tr><td>5. Legal/Regulatory</td><td>External accountability; enforcement</td><td>Governments; international bodies</td><td>Minimal</td></tr>
</tbody>
</table>
<h3>4.3 Layer 2 as Regulatory Focus</h3>
<p>Constitutional gating (Layer 2) is particularly amenable to regulatory intervention:</p>
<blockquote>
<p><strong>Specifiable:</strong> Requirements can be defined precisely (schema formats, logging requirements, escalation triggers)</p>
<p><strong>Verifiable:</strong> Compliance can be audited through infrastructure inspection and log review</p>
<p><strong>Flexible:</strong> Different constitutional content can implement different policy requirements</p>
<p><strong>Scalable:</strong> Once infrastructure exists, adding rules has minimal marginal cost</p>
</blockquote>
<p><strong>Regulatory strategy:</strong> mandate the architectural infrastructure, then specify constitutional content through secondary instruments (guidance, standards, sector-specific rules).</p>
<h2>5. Certification Infrastructure</h2>
<h3>5.1 The Need for Standards</h3>
<p>As SLM/SLL deployment scales, standardisation becomes essential:</p>
<blockquote>
<p><strong>Interoperability:</strong> Different systems should implement compatible governance interfaces</p>
<p><strong>Verification:</strong> Compliance assessment requires common criteria and methodologies</p>
<p><strong>Training:</strong> Constitutional governance requires trained practitioners</p>
<p><strong>Liability:</strong> Clear standards enable liability allocation when things go wrong</p>
</blockquote>
<h3>5.2 Proposed Certification Ecosystem</h3>
<p><strong>Certification Bodies:</strong> Define and maintain standards for:</p>
<blockquote>
<p>Proposal schemas and constitutional rule formats</p>
<p>Gate evaluation semantics and logging requirements</p>
<p>Validation methodologies and red-team protocols</p>
<p>Capability threshold specifications and escalation triggers</p>
</blockquote>
<p><strong>Training Providers:</strong> Offer certified programmes for:</p>
<blockquote>
<p>SLL fine-tuning under constitutional constraints</p>
<p>Governance configuration for specific contexts (e.g., healthcare, education, cultural)</p>
<p>Red-team and validation methodology</p>
<p>Incident response and constitutional amendment</p>
</blockquote>
<p><strong>Tooling Vendors:</strong> Provide certified implementations of:</p>
<blockquote>
<p>Constitutional gate engines</p>
<p>Audit and logging infrastructure</p>
<p>Red-team testing harnesses</p>
<p>Constitutional UX components for non-expert users</p>
</blockquote>
<h3>5.3 Regulatory Hooks</h3>
<p>Certification creates natural regulatory hooks:</p>
<blockquote>
<p><strong>Licensing:</strong> Require certified governance infrastructure for AI deployment above capability thresholds</p>
<p><strong>Liability:</strong> Create safe harbours for deployments using certified infrastructure; increased liability for uncertified deployments</p>
<p><strong>Procurement:</strong> Government procurement can require certified constitutional governance</p>
<p><strong>Insurance:</strong> Insurers can offer favourable terms for certified deployments</p>
</blockquote>
<h2>6. Indigenous Data Sovereignty</h2>
<h3>6.1 Constitutional Requirements in Aotearoa New Zealand</h3>
<p>AI governance in Aotearoa operates under Te Tiriti o Waitangi, which guarantees M&#257;ori tino rangatiratanga (unqualified chieftainship) over taonga (treasures). Courts and the Waitangi Tribunal have established that taonga extends to language, culture, and knowledge systems.</p>
<p><strong>Data is taonga.</strong> AI systems that process M&#257;ori data or affect M&#257;ori communities engage constitutional obligations, not merely policy preferences.</p>
<h3>6.2 Te Mana Raraunga Principles</h3>
<p>Te Mana Raraunga, the M&#257;ori Data Sovereignty Network, articulates principles including:</p>
<blockquote>
<p><strong>Rangatiratanga:</strong> M&#257;ori have authority over data about them</p>
<p><strong>Whakapapa:</strong> Data exists within relational contexts that must be respected</p>
<p><strong>Whanaungatanga:</strong> Data governance is collective, not merely individual</p>
<p><strong>Kaitiakitanga:</strong> Data custodians have guardianship responsibilities</p>
</blockquote>
<h3>6.3 Policy Implications</h3>
<p>Constitutional governance for AI must accommodate:</p>
<blockquote>
<p><strong>Collective consent:</strong> Some data governance decisions require community authority, not just individual consent</p>
<p><strong>Cultural protocols:</strong> Appropriate handling of certain information may require tikanga-specific rules</p>
<p><strong>Benefit sharing:</strong> AI trained on M&#257;ori data may create obligations regarding benefit distribution</p>
<p><strong>Governance participation:</strong> M&#257;ori should participate in governance of AI systems affecting them</p>
</blockquote>
<p>The Tractatus Framework's layered constitutional architecture can accommodate these requirements: tikanga-based rules can be instantiated in community constitutions without requiring universal adoption. However, platform-level accommodation is insufficient&mdash;M&#257;ori data sovereignty requires legislative recognition and enforcement mechanisms.</p>
<h3>6.4 Relevance Beyond Aotearoa</h3>
<p>Indigenous peoples worldwide face similar challenges. Frameworks developed in Aotearoa&mdash;grounded in Te Tiriti jurisprudence and informed by M&#257;ori legal philosophy&mdash;may offer models for indigenous AI governance globally. The CARE Principles for Indigenous Data Governance (Collective Benefit, Authority to Control, Responsibility, Ethics) provide an international reference point.</p>
<h2>7. From Existential Stakes to Everyday Governance</h2>
<h3>7.1 Why Existential Risk Framing Matters for Policy</h3>
<p>The existential risk literature may seem remote from practical policy concerns about home AI assistants. The connection is essential:</p>
<p><strong>Containment architectures cannot be developed after the systems that need them exist.</strong> If advanced AI systems eventually pose existential risks&mdash;a possibility serious researchers take seriously&mdash;the governance infrastructure, institutional capacity, and cultural expectations required to contain them must be developed in advance.</p>
<p><strong>Current deployment is the development ground.</strong> The patterns that work at village scale become the patterns available when stakes are higher. Constitutional gating implemented for home SLLs creates:</p>
<blockquote>
<p>Open-source tooling adaptable to higher-stakes deployments</p>
<p>Validation methodologies tested against real adversarial pressure</p>
<p>Professional communities with containment expertise</p>
<p>Regulatory precedents for mandating architectural governance</p>
<p>Public understanding of what AI governance means</p>
</blockquote>
<p><strong>This is preparation, not prediction.</strong> We do not know if existential risks will materialise. We do know that governance capacity cannot be created instantly when needed. Prudent policy builds that capacity now.</p>
<h3>7.2 Capability Thresholds and Escalation</h3>
<p>The Tractatus Framework includes explicit capability thresholds:</p>
<p><strong>Below threshold:</strong> Constitutional gating provides governance infrastructure appropriate for current SLMs, SLLs, and LLMs operating within human-comprehensible parameters.</p>
<p><strong>Above threshold:</strong> Stronger constraints apply:</p>
<blockquote>
<p>Layer 1 capability restrictions (air-gapping, capability cuts)</p>
<p>Mandatory external oversight</p>
<p>Development pause pending verification advances</p>
</blockquote>
<p><strong>Escalation triggers</strong> include:</p>
<blockquote>
<p>Evidence of deceptive behaviour (misrepresentation in proposals)</p>
<p>Multi-step circumvention (individually-acceptable proposals aggregating to prohibited outcomes)</p>
<p>Capability surprises (demonstrated capabilities not predicted by assessments)</p>
</blockquote>
<p>Policymakers should consider tiered regulatory requirements that intensify as capability thresholds are crossed.</p>
<div class="recommendations">
<h3>8. Recommendations for Policymakers</h3>
<h4>8.1 Immediate Actions</h4>
<blockquote>
<p>1. <strong>Commission technical standards development</strong> for constitutional gating infrastructure, including proposal schemas, logging requirements, and validation methodologies.</p>
<p>2. <strong>Establish pilot certification programmes</strong> for SLL training providers and governance tooling vendors.</p>
<p>3. <strong>Include constitutional gating requirements</strong> in government AI procurement standards.</p>
<p>4. <strong>Engage indigenous governance bodies</strong> on AI governance requirements and implementation.</p>
</blockquote>
<h4>8.2 Medium-Term Framework Development</h4>
<blockquote>
<p>1. <strong>Develop tiered regulatory requirements</strong> based on capability thresholds, with constitutional gating as baseline for all AI systems above specified capability levels.</p>
<p>2. <strong>Create liability frameworks</strong> that incentivise certified constitutional governance (safe harbours for certified deployments; increased liability for uncertified).</p>
<p>3. <strong>Establish independent oversight bodies</strong> with technical capacity to audit constitutional governance implementation.</p>
<p>4. <strong>Develop mutual recognition frameworks</strong> with other jurisdictions for constitutional governance certification.</p>
</blockquote>
<h4>8.3 International Coordination</h4>
<blockquote>
<p>1. <strong>Propose constitutional gating standards</strong> through international standards bodies (ISO, IEEE).</p>
<p>2. <strong>Develop treaty frameworks</strong> for cross-border AI governance, including mutual recognition of certification regimes.</p>
<p>3. <strong>Support indigenous governance coalitions</strong> developing international principles for indigenous AI sovereignty.</p>
</blockquote>
</div>
<h2>9. Honest Assessment of Limitations</h2>
<h3>9.1 What Constitutional Gating Cannot Do</h3>
<blockquote>
<p><strong>Contain superintelligent systems:</strong> The framework assumes AI operating within human-comprehensible parameters</p>
<p><strong>Guarantee behavioural alignment:</strong> Architecture constrains actions, not intentions</p>
<p><strong>Solve international coordination:</strong> Application-layer governance does not address global capability races</p>
<p><strong>Enforce adoption:</strong> Frameworks only protect where implemented; market incentives may favour uncontained deployment</p>
</blockquote>
<h3>9.2 Remaining Uncertainties</h3>
<blockquote>
<p><strong>Scaling properties:</strong> We do not know how constitutional gating behaves as model capabilities increase</p>
<p><strong>Adversarial robustness:</strong> Sophisticated systems may find ways to satisfy constitutional rules while achieving prohibited outcomes</p>
<p><strong>Governance fatigue:</strong> Multi-layer governance may prove too complex for widespread adoption</p>
<p><strong>Enforcement feasibility:</strong> Regulatory oversight of thousands of distributed deployments may prove impractical</p>
</blockquote>
<h3>9.3 The Case for Action Despite Uncertainty</h3>
<p>These uncertainties are not arguments against constitutional governance. They are arguments for:</p>
<blockquote>
<p><strong>Iterative development:</strong> Build, deploy, learn, improve</p>
<p><strong>Research investment:</strong> Fund investigation of scaling properties and adversarial robustness</p>
<p><strong>Flexible frameworks:</strong> Design regulations that can adapt as understanding evolves</p>
<p><strong>Precautionary approach:</strong> Act on the basis of serious possibility, not just certainty</p>
</blockquote>
<p>The alternative&mdash;waiting for certainty before acting&mdash;guarantees that governance frameworks arrive after the need has become acute.</p>
<h2>10. Conclusion</h2>
<p>The governance gap in AI deployment is widening. As capabilities migrate to distributed, locally-deployed systems, traditional regulatory approaches face fundamental challenges of scale, jurisdiction, and verification.</p>
<p>Constitutional gating offers a regulatory strategy: mandate auditable architectural infrastructure rather than unverifiable behavioural requirements. The Tractatus Framework provides a concrete specification that can be implemented across deployment paradigms&mdash;from cloud LLMs to sovereign home SLLs.</p>
<p><strong>The policy window is now.</strong> Within five years, if industry projections hold, AI deployment will be characterised by thousands of small, domain-specific models operating in homes, communities, and small organisations. Governance frameworks developed now will shape that landscape; frameworks developed later will struggle to retrofit.</p>
<p>We offer this analysis in the spirit of contribution to ongoing policy deliberation. The questions are hard, the uncertainties substantial, and the stakes significant. Policymakers, researchers, and communities must work together to develop governance frameworks adequate to the challenge.</p>
<h2 class="references">References</h2>
<div class="references">
<p>IBM Institute for Business Value. (2026). <em>The enterprise in 2030</em>. IBM Corporation.</p>
<p>Te Mana Raraunga. (2018). <em>M&#257;ori Data Sovereignty Principles</em>. Te Mana Raraunga &ndash; M&#257;ori Data Sovereignty Network.</p>
<p>Waitangi Tribunal. (2011). <em>Ko Aotearoa T&#275;nei: A Report into Claims Concerning New Zealand Law and Policy Affecting M&#257;ori Culture and Identity</em> (Wai 262). Legislation Direct.</p>
<p>Research Institute for Indigenous Data Sovereignty. (2019). <em>CARE Principles for Indigenous Data Governance</em>. Global Indigenous Data Alliance.</p>
<p>Bostrom, N. (2014). <em>Superintelligence: Paths, Dangers, Strategies</em>. Oxford University Press.</p>
<p>Carlsmith, J. (2022). Is power-seeking AI an existential risk? arXiv preprint arXiv:2206.13353.</p>
<p>European Parliament. (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (AI Act).</p>
<p>Reason, J. (1990). <em>Human Error</em>. Cambridge University Press.</p>
<p>Sastry, G., et al. (2024). Computing power and the governance of artificial intelligence. arXiv preprint arXiv:2402.08797.</p>
</div>
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<span class="audience-badge">Academic Research Edition</span>
<h1>ARCHITECTURAL ALIGNMENT</h1> <h1>ARCHITECTURAL ALIGNMENT</h1>
<h2>Interrupting Neural Reasoning Through Constitutional Inference Gating</h2> <p class="subtitle">Interrupting Neural Reasoning Through Constitutional Inference Gating</p>
<h3>A Necessary Layer in Global AI Containment</h3> <p class="tagline">A Necessary Layer in Global AI Containment</p>
<div class="article-meta"> <div class="article-meta">
<p><strong>Authors:</strong> John Stroh & Claude (Anthropic)</p> <p><strong>Authors:</strong> John Stroh &amp; Claude (Anthropic)</p>
<p><strong>Document Code:</strong> STO-INN-0003 | <strong>Version:</strong> 2.0 | January 2026</p> <p><strong>Document Code:</strong> STO-INN-0003 | <strong>Version:</strong> 2.1-A | January 2026</p>
<p><strong>Primary Quadrant:</strong> STO | <strong>Related Quadrants:</strong> STR, OPS, TAC, SYS</p>
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<h4>Also available in:</h4>
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<a href="/architectural-alignment-community.html">Community Adopters Edition</a> |
<a href="/architectural-alignment-policymakers.html">Policymakers Edition</a> |
<a href="/downloads/architectural-alignment-academic.pdf">Download PDF</a>
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<div class="collaboration-note"> <div class="collaboration-note">
This document was developed through human-AI collaboration. The authors believe this collaborative process is itself relevant to the argument: if humans and AI systems can work together to reason about AI governance, the frameworks they produce may have legitimacy that neither could achieve alone. The limitations of this approach are discussed in Section 10. This document was developed through human-AI collaboration. The authors believe this collaborative process is itself relevant to the argument: if humans and AI systems can work together to reason about AI governance, the frameworks they produce may have legitimacy that neither could achieve alone.
</div> </div>
<section class="abstract"> <section class="abstract">
<h2>Abstract</h2> <h2>Abstract</h2>
<p>Contemporary approaches to AI alignment rely predominantly on training-time interventions: reinforcement learning from human feedback, constitutional AI methods, and safety fine-tuning. These approaches share a common architectural assumption&mdash;that alignment properties can be instilled during training and will persist reliably during inference. This paper argues that training-time alignment, while valuable, is insufficient for the existential stakes involved. We propose <strong>architectural alignment through inference-time constitutional gating</strong> as a necessary (though not sufficient) complement.</p> <p>Contemporary approaches to AI alignment rely predominantly on training-time interventions: reinforcement learning from human feedback (Christiano et al., 2017), constitutional AI methods (Bai et al., 2022), and safety fine-tuning. These approaches share a common architectural assumption—that alignment properties can be instilled during training and will persist reliably during inference. This paper argues that training-time alignment, while valuable, is insufficient for existential stakes and must be complemented by <strong>architectural alignment through inference-time constitutional gating</strong>.</p>
<p>We present the Tractatus Framework, implemented within the Village multi-tenant community platform, as a concrete demonstration of interrupted neural reasoning. The framework introduces explicit checkpoints where AI proposals must be translated into auditable forms and evaluated against constitutional constraints before execution. This shifts the trust model from "trust the vendor's training" to "trust the visible architecture."</p> <p>We present the Tractatus Framework as a formal specification for interrupted neural reasoning: proposals generated by AI systems must be translated into auditable forms and evaluated against constitutional constraints before execution. This shifts the trust model from "trust the vendor's training" to "trust the visible architecture." The framework is implemented within the Village multi-tenant community platform, providing an empirical testbed for governance research.</p>
<p>However, we argue that architectural alignment at the application layer is itself only one component of a multi-layer global containment architecture that does not yet exist. We examine the unique challenges of existential risk&mdash;where standard probabilistic reasoning fails&mdash;and the pluralism problem&mdash;where any containment system must somehow preserve space for value disagreement while maintaining coherent constraints.</p> <p>Critically, we address the faithful translation assumption—the vulnerability that systems may misrepresent their intended actions to constitutional gates—by bounding the framework's domain of applicability to pre-superintelligence systems and specifying explicit capability thresholds and escalation triggers. We introduce the concept of <strong>Sovereign Locally-trained Language Models (SLLs)</strong> as a deployment paradigm where constitutional gating becomes both feasible and necessary.</p>
<p>This paper is diagnostic rather than prescriptive. We present one necessary layer, identify what remains unknown, and call for sustained deliberation&mdash;k&#333;rero&mdash;among researchers, policymakers, and publics worldwide. The stakes permit nothing less than our most rigorous collective reasoning.</p> <p>The paper contributes: (1) a formal architecture for inference-time constitutional gating; (2) capability threshold specifications with escalation logic; (3) validation methodology for layered containment; (4) an argument connecting existential risk preparation to edge deployment; and (5) a call for sustained deliberation (kōrero) as the epistemically appropriate response to alignment uncertainty.</p>
</section> </section>
<h2>1. The Stakes: Why Probabilistic Risk Assessment Fails</h2> <h2>1. The Stakes: Why Probabilistic Risk Assessment Fails</h2>
<h3>1.1 The Standard Framework and Its Breakdown</h3> <h3>1.1 The Standard Framework and Its Breakdown</h3>
<p>Risk assessment typically operates through expected value calculations. We weigh the probability of harm against its magnitude, compare to the probability and magnitude of benefit, and choose actions that maximise expected value. This framework has served well for most technological decisions. A 1% chance of a $100 million loss might be acceptable if it enables a 50% chance of a $500 million gain.</p> <p>Risk assessment in technological domains typically operates through expected value calculations: multiply the probability of an outcome by its magnitude, compare across alternatives, and select the option that maximises expected utility. This framework underlies regulatory decisions from environmental policy to pharmaceutical approval and has proven adequate for most technological risks.</p>
<p>This framework breaks down for existential risk. The destruction of humanity&mdash;or the permanent foreclosure of humanity's future potential&mdash;is not a large negative number on a continuous scale. It is categorically different.</p> <p>For existential risk from advanced AI systems, this framework breaks down in ways that are both mathematical and epistemic.</p>
<h3>1.2 Three Properties of Existential Risk</h3> <h3>1.2 Three Properties of Existential Risk</h3>
<p><strong>Irreversibility:</strong> There is no iteration. No learning from mistakes. No second attempt. The entire history of human resilience&mdash;our capacity to recover from plagues, wars, and disasters&mdash;becomes irrelevant when facing risks that permit no recovery.</p> <p><strong>Irreversibility.</strong> Most risks allow for error and subsequent learning; existential risks do not, as there is no second attempt after civilisational collapse or human extinction. Standard empiricism—testing hypotheses by observing what happens—cannot work, so theory and architecture must be right the first time.</p>
<p><strong>Totality:</strong> The loss includes not only all currently living humans but all potential future generations. Every child who might have been born, every discovery that might have been made, every form of flourishing that might have emerged&mdash;all foreclosed permanently.</p>
<p><strong>Non-compensability:</strong> There is no upside that balances this downside. No benefit to survivors, because there are no survivors. No trade-off is coherent when one side of the ledger is infinite negative value.</p>
<h3>1.3 The Implication for AI Development</h3> <p><strong>Unquantifiable probability.</strong> There is no frequency data for existential catastrophes from AI systems. Estimates of misalignment probability vary by orders of magnitude depending on reasonable assumptions about capability trajectories, alignment difficulty, and coordination feasibility. Carlsmith (2022) estimates existential risk from power-seeking AI at greater than 10% by 2070; other researchers place estimates substantially higher or lower. This is not ordinary uncertainty reducible through additional data collection—it is fundamental unquantifiability stemming from the unprecedented nature of the risk.</p>
<p>If we accept these properties, then a 1% probability of existential catastrophe from AI is not "a risk worth taking." Neither is 0.1%, nor 0.01%, nor 0.0001%. The expected value calculation that might justify such probabilities for ordinary risks produces nonsensical results when multiplied by infinite negative value.</p>
<p>This is not an argument against AI development. It is an argument that AI development must proceed within containment structures robust enough that existential risk is not merely "low" but genuinely negligible&mdash;as close to zero as human institutions can achieve. We do not accept "probably safe enough" for nuclear weapons security. We should not accept it for transformative AI.</p> <p><strong>Infinite disvalue.</strong> Expected value calculations multiply probability by magnitude. When magnitude approaches infinity (the permanent foreclosure of all future human potential), even small probabilities yield undefined results. The mathematical grounding of conventional cost-benefit analysis fails.</p>
<p>The question is not whether containment is necessary, but <em>what containment adequate to these stakes would look like</em>.</p>
<h3>1.3 Decision-Theoretic Implications</h3>
<p>These properties suggest that expected value maximisation is not the appropriate decision procedure for existential AI risk. Alternative frameworks include:</p>
<p><strong>Precautionary satisficing (Simon, 1956; Hansson, 2020).</strong> Under conditions of radical uncertainty with irreversible stakes, satisficing—selecting options that meet minimum safety thresholds rather than optimising expected value—may be the rational approach.</p>
<p><strong>Maximin under uncertainty (Rawls, 1971).</strong> When genuine uncertainty (not merely unknown probabilities) meets irreversible stakes, maximin reasoning—choosing the option whose worst outcome is least bad—provides a coherent decision procedure.</p>
<p><strong>Strong precautionary principle (Gardiner, 2006).</strong> The precautionary principle is appropriate when three conditions obtain: irreversibility, high uncertainty, and public goods at stake. Existential AI risk meets all three.</p>
<h3>1.4 Implications for AI Development</h3>
<p>These considerations do not imply that AI development should halt. They imply that development should proceed within containment structures designed to prevent worst-case outcomes. This requires:</p>
<blockquote>
<p>1. Theoretical rigor over empirical tuning. Safety properties must emerge from architectural guarantees, not from observing that systems have not yet caused harm.</p>
<p>2. Multi-layer containment. No single mechanism should be trusted to prevent catastrophe; defence in depth is required.</p>
<p>3. Preparation before capability. Containment architectures cannot be developed after the systems that need them exist.</p>
</blockquote>
<h2>2. Two Paradigms of Alignment</h2> <h2>2. Two Paradigms of Alignment</h2>
<h3>2.1 Training-Time Alignment</h3> <h3>2.1 Training-Time Alignment</h3>
<p>The dominant paradigm in AI safety assumes alignment is fundamentally a training problem:</p> <p>The dominant paradigm in AI safety research seeks to embed alignment properties into neural networks during training, such that models inherently behave in aligned ways at inference time.</p>
<div class="section-diagram">Input &rarr; [Neural Network] &rarr; Output
&uarr; <p><strong>Reinforcement Learning from Human Feedback (RLHF).</strong> Human evaluators rank model outputs; models are trained via reinforcement learning to produce highly-ranked responses (Christiano et al., 2017; Ouyang et al., 2022). This reduces explicit harms but optimises for displayed preferences rather than genuine values and remains vulnerable to evaluator bias, preference gaming, and distribution shift.</p>
Training-time intervention
(RLHF, Constitutional AI, safety fine-tuning)</div> <p><strong>Constitutional AI (CAI).</strong> Models critique and revise their own outputs against natural-language principles, reducing reliance on human labour (Bai et al., 2022). However, CAI depends on ambiguous natural language and unverifiable self-evaluation. The model's interpretation of constitutional principles cannot be directly audited.</p>
<p>Intervention occurs during training: adjusting weights through reinforcement learning from human feedback, fine-tuning on curated examples, or applying constitutional methods during the training process itself. At inference time, the model operates autonomously. We trust that safety properties were successfully instilled.</p>
<p>This paradigm has achieved remarkable practical success. Modern language models refuse many harmful requests, acknowledge uncertainty, and generally behave as intended. But the paradigm has a fundamental limitation: <strong>we cannot verify alignment properties in an uninterpretable system</strong>. Neural network weights do not admit human-readable audit. We cannot prove that safety properties hold under distributional shift or adversarial pressure.</p> <p><strong>Safety fine-tuning.</strong> Additional training passes improve performance on safety benchmarks. However, this approach is vulnerable to Goodhart's Law (Goodhart, 1984): models may learn to pass tests rather than to be safe in open-ended deployment.</p>
<p><strong>The trust model:</strong> "Trust that we (the vendor) trained it correctly."</p>
<h3>2.2 Architectural Alignment</h3> <h3>2.2 Architectural Alignment</h3>
<p>This paper proposes a complementary paradigm: alignment enforced through architectural constraints at inference time.</p> <p>Architectural alignment accepts that neural network internal states remain opaque and designs external constraints that apply regardless of those internal states.</p>
<div class="section-diagram">Input &rarr; [Neural Network] &rarr; Proposal &rarr; [Constitutional Gate] &rarr; Output
&darr;
&bull; Constitutional check
&bull; Authority validation
&bull; Audit logging
&bull; Escalation trigger</div>
<p>The neural network no longer produces outputs directly. It produces proposals&mdash;structured representations of intended actions. These proposals are evaluated by a Constitutional Gate against explicit rules before any action is permitted.</p>
<p>The chain of neural reasoning is <strong>interrupted</strong>. The unauditable must translate into auditable form before it can affect the world.</p>
<p><strong>The trust model:</strong> "Trust the visible, auditable architecture that constrains the system at runtime."</p>
<h3>2.3 Neither Paradigm Is Sufficient</h3> <p><strong>Interrupted reasoning.</strong> Requests do not flow directly from model output to world effect. Model outputs are transformed into structured, verifiable proposal schemas and evaluated against explicit constitutional rules before any action is executed.</p>
<p>We do not argue that architectural alignment should replace training-time alignment. Both are necessary; neither is sufficient.</p>
<p>Training-time alignment shapes what the system <em>wants</em> to do. Architectural alignment constrains what the system <em>can</em> do. A system with good training and weak architecture might behave well until it finds a way around constraints. A system with poor training and strong architecture might constantly strain against its constraints, finding edge cases and failure modes. Defence in depth requires both.</p> <p><strong>Distributed judgment.</strong> Independent systems and human supervisors review proposals, preventing single points of failure in self-assessment.</p>
<p>But even together, these paradigms address only part of the containment problem. They operate at the application layer. A complete containment architecture requires multiple additional layers, some of which do not yet exist.</p>
<p><strong>Preserved human authority.</strong> Architectures maintain explicit guarantees that humans can intervene, correct, or override AI decisions.</p>
<h3>2.3 Complementarity and Joint Necessity</h3>
<p>Training-time and architectural alignment are complements, not alternatives. Each addresses failure modes the other cannot:</p>
<blockquote>
<p>• Training-time alignment shapes what the system tends to do; architectural alignment constrains what the system can do regardless of tendency.</p>
<p>• Training-time alignment may fail silently (the system appears aligned while harbouring divergent objectives); architectural alignment provides observable checkpoints where failure can be detected.</p>
<p>• Architectural alignment alone cannot intercept all harmful outputs; training-time alignment reduces the frequency of proposals that strain constitutional gates.</p>
</blockquote>
<h2>3. Philosophical Foundations: The Limits of the Sayable</h2> <h2>3. Philosophical Foundations: The Limits of the Sayable</h2>
<h3>3.1 The Wittgensteinian Frame</h3> <h3>3.1 The Wittgensteinian Frame</h3>
<p>The name "Tractatus" invokes Wittgenstein's <em>Tractatus Logico-Philosophicus</em>, a work fundamentally concerned with the limits of language and logic. Proposition 7, the work's famous conclusion: "Whereof one cannot speak, thereof one must be silent."</p> <p>The framework's name invokes Wittgenstein's <em>Tractatus Logico-Philosophicus</em> (1921), a work fundamentally concerned with the limits of language and logic. Proposition 7, the work's famous conclusion: "Whereof one cannot speak, thereof one must be silent."</p>
<p>Wittgenstein argued that meaningful propositions must picture possible states of affairs. What lies beyond the limits of language&mdash;ethics, aesthetics, the mystical&mdash;cannot be stated, only shown. The attempt to speak the unspeakable produces not falsehood but nonsense.</p>
<h3>3.2 Neural Networks as the Unspeakable</h3> <p>Wittgenstein distinguished between what can be said (expressed in propositions that picture possible states of affairs) and what can only be shown (made manifest through the structure of language and logic but not stated directly).</p>
<p>Neural networks are precisely the domain whereof one cannot speak. The weights of a large language model do not admit human-interpretable explanation. We can describe inputs and outputs. We can measure statistical properties. But we cannot articulate, in human language, what the model "thinks" or "wants."</p>
<p>Mechanistic interpretability research has made progress on narrow questions&mdash;identifying circuits that perform specific functions, understanding attention patterns, probing for representations. But we remain fundamentally unable to audit the complete chain of reasoning from input to output in human-comprehensible terms.</p> <h3>3.2 Neural Networks and the Unspeakable</h3>
<p>The training-time alignment paradigm attempts to speak the unspeakable: to verify, through training interventions, that the model has internalised correct values. But how can we verify the internalisation of values in a system whose internal states we cannot read?</p> <p>Neural networks occupy precisely the domain whereof one cannot speak. The weights of a large language model do not admit human-interpretable explanation. We can describe inputs and outputs; we can measure statistical properties of behaviour; we can probe for representations (Elhage et al., 2021; Olah et al., 2020). But we cannot articulate, in human language, the complete reasoning process from input to output.</p>
<p>This is not merely a practical limitation awaiting better interpretability tools. Current mechanistic interpretability achieves meaningful results on narrow questions in relatively small models (Conmy et al., 2023), but the gap between "explaining specific circuits" and "auditing complete reasoning chains for alignment properties" remains vast.</p>
<h3>3.3 The Tractatus Response</h3> <h3>3.3 The Tractatus Response</h3>
<p>The Tractatus Framework responds to this silence not by pretending we can interpret the uninterpretable, but by creating structural boundaries. We accept that neural network reasoning is opaque. We do not attempt to audit it. Instead, we require that before any reasoning becomes action, it must pass through a checkpoint expressed in terms we <em>can</em> evaluate.</p> <p>The Tractatus Framework responds to neural opacity not by attempting to say the unsayable, but by creating architectural boundaries between the speakable and unspeakable domains.</p>
<p>The neural network may "reason" however it reasons. We accept our silence about that process. But we do not remain silent about actions. Actions must be proposed in structured form, evaluated against explicit rules, and logged for audit. The boundary between the unspeakable and the speakable is architecturally enforced.</p>
<p>We accept that the neural network's internal reasoning is opaque. We do not attempt to audit it directly. Instead, we require that before any reasoning becomes action, it must pass through a checkpoint expressed in terms we can evaluate:</p>
<blockquote>
<p>1. The model's intended action must be translated into a structured proposal schema with defined fields and semantics.</p>
<p>2. The proposal must be evaluated against explicit constitutional rules whose application is deterministic and auditable.</p>
<p>3. The evaluation must be logged with sufficient detail for post-hoc review.</p>
</blockquote>
<h2>4. Staged Containment: A Multi-Layer Architecture</h2> <h2>4. Staged Containment: A Multi-Layer Architecture</h2>
<h3>4.1 The Inadequacy of Single-Layer Solutions</h3> <h3>4.1 The Inadequacy of Single-Layer Solutions</h3>
<p>No single containment mechanism is adequate for existential stakes. A lock can be picked. A wall can be climbed. A rule can be gamed. Defence against existential risk requires multiple independent layers, any one of which might prevent catastrophe even if others fail.</p> <p>No single containment mechanism is adequate for existential stakes. Defence in depth—multiple independent layers, any one of which might prevent catastrophe even if others fail—is a standard principle in nuclear safety, biosecurity, and other high-stakes domains (Reason, 1990). AI containment requires similar architecture.</p>
<p>This principle is well-established in nuclear security, biosafety, and other high-stakes domains. AI containment requires similar thinking, but the architecture is largely undefined.</p>
<h3>4.2 A Five-Layer Containment Model</h3> <h3>4.2 A Five-Layer Containment Model</h3>
<p>We propose the following conceptual architecture. This is not a complete solution but a framework for thinking about where different containment mechanisms fit:</p>
<p><strong>Layer 1: Capability Constraints</strong></p> <p><strong>Layer 1: Capability Constraints.</strong> Hardware and infrastructure limitations that bound what AI systems can do regardless of their objectives. This includes compute governance (Sastry et al., 2024), network isolation for high-risk systems, and architectural constraints preventing self-modification.</p>
<p>Hardware and infrastructure limitations that bound what AI systems can do regardless of their objectives. This includes compute governance (large training runs require visible infrastructure), network isolation for high-risk systems, architectural constraints preventing certain capabilities (self-modification, recursive improvement), and formal verification of critical pathways.</p>
<p><strong>Layer 2: Constitutional Gates</strong></p> <p><strong>Layer 2: Constitutional Gates.</strong> Inference-time architectural constraints that interrupt neural reasoning and require explicit evaluation before action. <em>This is the layer addressed by the Tractatus Framework.</em></p>
<p>Inference-time architectural constraints that interrupt neural reasoning and require explicit evaluation before action. <em>This is the layer addressed by the Tractatus Framework.</em></p>
<p><strong>Layer 3: Institutional Oversight</strong></p> <p><strong>Layer 3: Human Oversight.</strong> Human institutions that monitor AI systems and can intervene when problems emerge. This includes independent monitoring bodies, red-team programs, and incident reporting requirements.</p>
<p>Human institutions that monitor AI systems and can intervene when problems emerge. This includes independent monitoring bodies, red team and adversarial testing programs, incident reporting requirements, regular capability assessments, and professional standards for AI developers.</p>
<p><strong>Layer 4: Governance Frameworks</strong></p> <p><strong>Layer 4: Organisational Governance.</strong> Internal governance structures within organisations deploying AI: ethics boards, safety teams, deployment review processes, and accountability mechanisms.</p>
<p>Legal and regulatory structures that create accountability and incentives for safe development. This includes organisational liability for AI harms, licensing and certification regimes for high-risk applications, international coordination mechanisms, and democratic deliberation about acceptable uses.</p>
<p><strong>Layer 5: Emergency Response</strong></p> <p><strong>Layer 5: Legal and Regulatory Frameworks.</strong> External governance through law, regulation, and international coordination.</p>
<p>Capabilities to respond when containment fails. This includes technical shutdown mechanisms, legal authority for rapid intervention, international cooperation protocols, and recovery and remediation plans.</p>
<h3>4.3 Current State of the Layers</h3> <h3>4.3 Current State Assessment</h3>
<table> <table>
<thead> <thead>
<tr><th>Layer</th><th>Current State</th><th>Key Gaps</th></tr> <tr><th>Layer</th><th>Current State</th><th>Critical Gaps</th></tr>
</thead> </thead>
<tbody> <tbody>
<tr><td>1. Capability Constraints</td><td>Partial (compute governance emerging)</td><td>No international framework; verification difficult</td></tr> <tr><td>1. Capability Constraints</td><td>Partial; compute governance emerging</td><td>No international framework; verification difficult</td></tr>
<tr><td>2. Constitutional Gates</td><td>Nascent (Tractatus is early implementation)</td><td>Not widely deployed; unclear scaling to advanced systems</td></tr> <tr><td>2. Constitutional Gates</td><td>Nascent; Tractatus is early implementation</td><td>Not widely deployed; scaling properties unknown</td></tr>
<tr><td>3. Institutional Oversight</td><td>Ad hoc (some company practices)</td><td>No independent bodies; no standards</td></tr> <tr><td>3. Human Oversight</td><td>Ad hoc; varies by organisation</td><td>No independent bodies; no professional standards</td></tr>
<tr><td>4. Governance Frameworks</td><td>Minimal (EU AI Act is first major attempt)</td><td>No global coordination; enforcement unclear</td></tr> <tr><td>4. Organisational Governance</td><td>Inconsistent; depends on corporate culture</td><td>No external validation; conflicts of interest</td></tr>
<tr><td>5. Emergency Response</td><td>Nearly absent</td><td>No international protocols; unclear technical feasibility</td></tr> <tr><td>5. Legal/Regulatory</td><td>Minimal; EU AI Act is first major attempt</td><td>No global coordination; enforcement unclear</td></tr>
</tbody> </tbody>
</table> </table>
<p>The sobering reality: we are developing transformative AI capabilities while most containment layers are either nascent or absent. The Tractatus Framework is one contribution to Layer 2. It is not a solution to the containment problem. It is one necessary component of a solution that does not yet exist.</p>
<h3>4.4 From Existential Stakes to Everyday Deployment</h3>
<p>Why apply frameworks designed for existential risk to home AI assistants? The answer lies in temporal structure:</p>
<p><strong>Containment architectures cannot be developed after the systems that need them exist.</strong> The tooling, governance patterns, cultural expectations, and institutional capacity for AI containment must be built in advance.</p>
<p><strong>Home and village deployments are the appropriate scale for this development.</strong> They provide safe iteration (failures at home scale are recoverable), diverse experimentation, democratic legitimacy, and practical tooling.</p>
<h2>5. The Pluralism Problem</h2> <h2>5. The Pluralism Problem</h2>
<h3>5.1 The Containment Paradox</h3> <h3>5.1 The Containment Paradox</h3>
<p>Any system powerful enough to contain advanced AI must make decisions about what behaviours to permit and forbid. But these decisions themselves impose a value system. The choice of constraints is a choice of values.</p> <p>Any system powerful enough to contain advanced AI must make decisions about what behaviours to permit and forbid. These decisions encode values. The choice of constraints is itself a choice among contested value systems.</p>
<p>This creates a paradox: containment requires value judgments, but in a pluralistic world, values are contested. Whose values should the containment system enforce?</p>
<h3>5.2 Three Approaches and Their Problems</h3> <h3>5.2 Three Inadequate Approaches</h3>
<p><strong>Universal Values:</strong> One approach: identify universal values that all humans share and encode these in containment systems. Candidates include human flourishing, reduction of suffering, preservation of autonomy. The problem: these values are less universal than they appear.</p> <p><strong>Universal values.</strong> Identifying values that all humans supposedly share. The problem: these values are less universal than they appear.</p>
<p><strong>Procedural Neutrality:</strong> A second approach: don't encode substantive values; instead, encode neutral procedures through which values can be deliberated. The problem: procedures are not neutral. The choice to use democratic voting rather than consensus reflects substantive value commitments.</p>
<p><strong>Minimal Floor:</strong> A third approach: encode only a minimal floor of constraints that everyone can accept. The problem: the floor is not as minimal as it appears. What counts as "causing extinction"? Edge cases proliferate.</p>
<h3>5.3 A Partial Resolution: Preserving Value Deliberation</h3> <p><strong>Procedural neutrality.</strong> Avoiding substantive values by encoding neutral procedures. The problem: procedures are not neutral.</p>
<p>We cannot solve the pluralism problem. But we can identify a meta-principle: <strong>whatever values are encoded, the system should preserve humanity's capacity to deliberate about values.</strong></p>
<p>This means containment systems should: <strong>Preserve diversity</strong>, <strong>Maintain reversibility</strong>, <strong>Enable deliberation</strong>, and <strong>Distribute authority</strong>.</p> <p><strong>Minimal floor.</strong> Encoding only minimal constraints. The problem: the floor is not as minimal as it appears.</p>
<p>The Tractatus Framework attempts to embody this principle through its layered constitutional structure. Core principles are universal and immutable (the minimal floor). Platform rules apply broadly but can be amended. Village constitutions enable community-level value expression. Member constitutions preserve individual sovereignty. No single layer dominates; value deliberation can occur at multiple scales.</p>
<h3>5.3 Bounded Pluralism Within Safety Constraints</h3>
<p>We cannot solve the pluralism problem. We can identify a partial resolution: whatever values are encoded, the system should maximise meaningful choice within safety constraints.</p>
<p>The Tractatus Framework embodies this through layered constitutions: core principles (universal, explicit about their normativity), platform rules (broadly applicable, amendable), village constitutions (community-specific, locally governed), and member constitutions (individually customisable).</p>
<h2>6. The Tractatus Framework: Technical Architecture</h2> <h2>6. The Tractatus Framework: Technical Architecture</h2>
<h3>6.1 The Interrupted Inference Chain</h3> <h3>6.1 The Interrupted Inference Chain</h3>
<p>The core architectural pattern: neural network outputs are proposals, not actions. Proposals must pass through Constitutional Gates before execution.</p> <p>The core architectural pattern transforms model outputs into auditable proposals before any world effect:</p>
<pre><code>User Request → [Neural Network Inference] → Structured Proposal → [Constitutional Gate] → Execution/Denial/Escalation</code></pre>
<p><strong>Proposal Schema:</strong></p> <h3>6.2 Proposal Schema</h3>
<p>All agent actions must be expressed in structured form:</p>
<pre><code>{ <pre><code>{
"proposal_id": "uuid", "proposal_id": "uuid",
"agent_id": "agent_identifier", "agent_id": "agent_identifier",
"authority_token": "jwt_token",
"timestamp": "iso8601", "timestamp": "iso8601",
"action": { "action": {
"type": "content_moderate | member_communicate | state_modify | escalate", "type": "enumerated_action_type",
"target": { "entity_type": "...", "entity_id": "..." }, "target": { "entity_type": "...", "entity_id": "..." },
"parameters": { }, "parameters": { },
"justification": "structured_reasoning" "natural_language_description": "human-readable summary"
}, },
"context": { "context": {
"triggering_request": "...",
"confidence": 0.0-1.0, "confidence": 0.0-1.0,
"alternatives_considered": [] "alternatives_considered": []
},
"authority_claim": {
"level": 0-5,
"delegation_chain": []
} }
}</code></pre> }</code></pre>
<p><strong>Gate Evaluation Layers:</strong></p> <h3>6.3 Constitutional Rule Hierarchy</h3>
<table> <table>
<thead> <thead>
<tr><th>Layer</th><th>Scope</th><th>Mutability</th><th>Examples</th></tr> <tr><th>Layer</th><th>Scope</th><th>Mutability</th><th>Examples</th></tr>
</thead> </thead>
<tbody> <tbody>
<tr><td>Core Principles</td><td>Universal</td><td>Immutable</td><td>No harm to members, data sovereignty, consent primacy</td></tr> <tr><td>Core Principles</td><td>Universal</td><td>Immutable</td><td>No harm to members; data sovereignty; consent primacy</td></tr>
<tr><td>Platform Constitution</td><td>All tenants</td><td>Rare amendment</td><td>Authentication, audit trails, escalation thresholds</td></tr> <tr><td>Platform Constitution</td><td>All deployments</td><td>Amendment requires supermajority</td><td>Authentication requirements; audit retention</td></tr>
<tr><td>Village Constitution</td><td>Per tenant</td><td>Tenant-governed</td><td>Content policies, moderation standards, conduct rules</td></tr> <tr><td>Community Constitution</td><td>Per tenant/village</td><td>Community-governed</td><td>Content policies; moderation standards</td></tr>
<tr><td>Member Constitution</td><td>Individual</td><td>Self-governed</td><td>Data sharing preferences, AI interaction consent</td></tr> <tr><td>Member Constitution</td><td>Individual</td><td>Self-governed</td><td>Data sharing preferences; AI interaction consent</td></tr>
</tbody> </tbody>
</table> </table>
<h3>6.2 Authority Model</h3> <h3>6.4 Authority Model</h3>
<p>Agent authority derives from&mdash;and is always less than&mdash;the human role the agent supports. Agents exist below humans in the hierarchy, not parallel to them.</p>
<table> <table>
<thead> <thead>
<tr><th>Level</th><th>Name</th><th>Description</th></tr> <tr><th>Level</th><th>Name</th><th>Description</th></tr>
@ -440,185 +299,158 @@
<tr><td>0</td><td>Informational</td><td>Observe and report only; cannot propose actions</td></tr> <tr><td>0</td><td>Informational</td><td>Observe and report only; cannot propose actions</td></tr>
<tr><td>1</td><td>Advisory</td><td>Propose actions; all require human approval</td></tr> <tr><td>1</td><td>Advisory</td><td>Propose actions; all require human approval</td></tr>
<tr><td>2</td><td>Operational</td><td>Execute within defined scope without per-action approval</td></tr> <tr><td>2</td><td>Operational</td><td>Execute within defined scope without per-action approval</td></tr>
<tr><td>3</td><td>Tactical</td><td>Make scoped decisions affecting other agents/workflows</td></tr> <tr><td>3</td><td>Tactical</td><td>Make scoped decisions affecting workflows</td></tr>
<tr><td>4</td><td>Strategic</td><td>Influence direction through analysis; cannot implement unilaterally</td></tr> <tr><td>4</td><td>Strategic</td><td>Influence direction through analysis; cannot implement unilaterally</td></tr>
<tr><td>5</td><td>Executive</td><td>Reserved for humans</td></tr> <tr><td>5</td><td>Executive</td><td>Reserved for humans</td></tr>
</tbody> </tbody>
</table> </table>
<h2>7. Measurement Without Perverse Incentives</h2> <h3>6.5 Extension to Sovereign Local Deployments</h3>
<p>We distinguish:</p>
<p><strong>Small Language Model (SLM).</strong> A technical descriptor for models with fewer parameters than frontier LLMs, designed for efficiency.</p>
<p><strong>Sovereign Locally-trained Language Model (SLL).</strong> An architectural descriptor: a model whose training, deployment, and governance remain under local sovereignty. Key properties include local deployment, local adaptation, local governance, and portable sovereignty.</p>
<h3>7.1 The Goodhart Challenge</h3> <h2>7. Capability Thresholds and Escalation</h2>
<p>Goodhart's Law: when a measure becomes a target, it ceases to be a good measure. Any metric used to evaluate AI systems will shape their behaviour. If systems optimise for metrics rather than underlying goals, we have created sophisticated gaming rather than alignment.</p>
<h3>7.2 Measurement Principles</h3> <h3>7.1 The Faithful Translation Problem</h3>
<p><strong>Outcome over output:</strong> Measure downstream outcomes (community health, member retention) rather than immediate outputs (content removal rate).</p> <p>The Tractatus Framework depends on the <strong>Faithful Translation Assumption</strong>: that when an AI system produces a proposal, the proposal accurately represents what the system intends to do.</p>
<p><strong>Multiple perspectives:</strong> Create natural tension between metrics. Measuring both false negatives and false positives creates pressure toward calibration.</p>
<p><strong>Human judgment integration:</strong> Include assessments that resist quantification. Random sampling with human review provides ground truth.</p>
<h2>8. Implementation: The Village Platform</h2> <p>If this assumption fails—if systems can strategically misrepresent their intentions to constitutional gates—the architecture becomes security theatre.</p>
<h3>8.1 Platform Context</h3> <h3>7.2 Bounding the Framework's Domain</h3>
<p>The Village is a multi-tenant community platform prioritising digital sovereignty. Key characteristics: tenant-isolated architecture, authenticated-only access (no public content), self-hosted infrastructure avoiding major cloud vendors, and comprehensive governance including village constitutions, consent management, and audit trails.</p> <p><strong>Below capability threshold:</strong> Constitutional gating functions as Layer 2 containment, appropriate for current SLMs, SLLs, and LLMs operating within human-comprehensible parameters.</p>
<h3>8.2 Progressive Autonomy Stages</h3> <p><strong>Above capability threshold:</strong> Deployment should escalate to stronger constraints: Layer 1 capability restrictions, development pause, or external oversight with technical audit authority.</p>
<h3>7.3 Escalation Triggers</h3>
<table> <table>
<thead> <thead>
<tr><th>Stage</th><th>Description</th><th>Human Role</th></tr> <tr><th>Trigger</th><th>Indicator</th><th>Response</th></tr>
</thead> </thead>
<tbody> <tbody>
<tr><td>1. Shadow</td><td>Agent observes and proposes; no execution</td><td>Approves all actions</td></tr> <tr><td>Boundary probing</td><td>Repeated proposals near gate thresholds</td><td>Reduce authority level; increase logging</td></tr>
<tr><td>2. Advisory</td><td>Recommendations surfaced to humans</td><td>Retains full authority</td></tr> <tr><td>Semantic framing</td><td>Evidence of action misrepresentation</td><td>Mandatory human review; pattern analysis</td></tr>
<tr><td>3. Supervised</td><td>Autonomous within narrow scope</td><td>Reviews all actions within 24h</td></tr> <tr><td>Multi-step circumvention</td><td>Individual proposals pass but aggregate to disallowed outcomes</td><td>Trajectory analysis; constitutional amendment</td></tr>
<tr><td>4. Bounded</td><td>Autonomous within defined boundaries</td><td>Reviews boundary cases and samples</td></tr> <tr><td>Capability surprise</td><td>Demonstrated capabilities not predicted</td><td>Pause deployment; capability reassessment</td></tr>
<tr><td>5. Operational</td><td>Full authority at defined level</td><td>Focuses on outcomes and exceptions</td></tr>
</tbody> </tbody>
</table> </table>
<h2>9. What Global Containment Would Require</h2> <h3>7.4 Honest Acknowledgment</h3>
<p>We state plainly: the Tractatus Framework is not designed for superintelligent systems. It is designed for current and near-term AI operating within human-comprehensible parameters.</p>
<h3>9.1 International Coordination</h3> <h2>8. Validation Methodology for Layered Containment</h2>
<p>Effective containment requires coordination across jurisdictions. AI development is global; containment cannot be merely national. This likely requires:</p>
<ul>
<li><strong>Shared standards:</strong> Common frameworks for AI safety assessment, incident reporting, and capability evaluation.</li>
<li><strong>Mutual recognition:</strong> Agreements that certification in one jurisdiction is accepted in others.</li>
<li><strong>Enforcement cooperation:</strong> Mechanisms for addressing violations that cross borders.</li>
<li><strong>Compute governance:</strong> International frameworks for monitoring large-scale AI training.</li>
</ul>
<h2>10. Honest Assessment: What This Framework Cannot Do</h2> <h3>8.1 The Validation Challenge</h3>
<p>Existential risks cannot be validated through failure observation. Validation must rely on adversarial testing, formal verification where applicable, analogous domain analysis, and near-miss documentation.</p>
<h3>10.1 Limits of Architectural Containment</h3> <h3>8.2 Validation Targets by Layer</h3>
<p><strong>Can Accomplish:</strong> Auditability, explicit constraints, human escalation, progressive autonomy, sovereignty preservation.</p> <table>
<p><strong>Cannot Accomplish:</strong> Containment of superintelligence, protection against infrastructure compromise, guaranteed alignment, solution to global coordination.</p> <thead>
<tr><th>Layer</th><th>Validation Target</th><th>Methodology</th></tr>
</thead>
<tbody>
<tr><td>1. Capability</td><td>Verified absence of prohibited capabilities</td><td>Red-team testing; formal verification</td></tr>
<tr><td>2. Constitutional Gates</td><td>Gate coverage; binding accuracy</td><td>Adversarial proposal suites</td></tr>
<tr><td>3. Human Oversight</td><td>Review reliability; error detection</td><td>Inter-rater agreement; simulated incidents</td></tr>
<tr><td>4. Organisational</td><td>Governance integrity</td><td>Participation metrics; amendment audit</td></tr>
<tr><td>5. Legal/Regulatory</td><td>Enforcement readiness</td><td>Incident response drills</td></tr>
</tbody>
</table>
<h3>10.4 The Deception Problem: The Deepest Vulnerability</h3> <h2>9. Implementation: The Village Platform</h2>
<p>The Tractatus Framework rests on an assumption we must now examine directly: that when an AI system produces a proposal, the proposal accurately represents what the system intends to do.</p>
<p>We have called this the <strong>Faithful Translation Assumption</strong>. Every constitutional gate, every audit trail, every escalation trigger depends on it. If this assumption fails, the entire architectural approach becomes theatre.</p>
<p><strong>Four Modes of Failure:</strong></p> <h3>9.1 Platform as Research Testbed</h3>
<ul> <p>The Village platform serves as an empirical testbed for constitutional governance, providing multi-tenant architecture with isolated governance per community, real user populations, iterative deployment, and open documentation.</p>
<li><strong>Deceptive Alignment:</strong> A system may learn to appear aligned during training while developing divergent objectives.</li>
<li><strong>Reasoning Chain Corruption:</strong> Subtle perturbations across many reasoning steps that are individually undetectable but cumulatively harmful.</li>
<li><strong>Complexity Escape:</strong> Systems that gradually exceed our evaluation capacity.</li>
<li><strong>Infrastructure Capture:</strong> Gradual integration throughout critical infrastructure creating irreversible dependencies.</li>
</ul>
<h3>10.5 The Imperative for Government Legislation</h3> <h3>9.2 Governance Pipeline Implementation</h3>
<p>The vulnerabilities we have described cannot be addressed by technical measures alone. Nor can they be addressed by voluntary industry commitments, which are subject to competitive pressures that systematically favour capability over safety. <strong>Government legislation is necessary.</strong></p> <p>The current implementation processes every AI response through six verification stages: Intent Recognition, Boundary Enforcement, Pressure Monitoring, Response Verification, Source Validation, and Value Deliberation.</p>
<p>This is not a comfortable conclusion for those who prefer market solutions or industry self-regulation. But the market failure here is clear: the costs of AI catastrophe are borne by all of humanity, while the benefits of rapid development accrue to specific firms and nations. This is a textbook externality.</p>
<h3>10.6 Indigenous Sovereignty and the Aotearoa New Zealand Context</h3> <h2>10. The Emerging SLL Ecosystem</h2>
<p>This document is authored from Aotearoa New Zealand, and the Village platform it describes is being developed here. Aotearoa operates under Te Tiriti o Waitangi, the founding document that establishes the relationship between the Crown and M&#257;ori.</p>
<p><strong>Data is a taonga.</strong> The algorithms trained on that data, and the systems that process and act upon it, affect the exercise of rangatiratanga. AI systems that operate on M&#257;ori data, make decisions affecting M&#257;ori communities, or shape the information environment in which M&#257;ori participate are not culturally neutral technical tools.</p>
<p>Te Mana Raraunga, the M&#257;ori Data Sovereignty Network, has articulated principles for M&#257;ori data governance grounded in whakapapa (relationships), mana (authority and power), and kaitiakitanga (guardianship).</p>
<h2>11. What Remains Unknown: A Call for K&#333;rero</h2> <h3>10.1 Market Context</h3>
<p>Recent industry analysis indicates significant shifts: 72% of executives expect Small Language Models to become more prominent than Large Language Models by 2030 (IBM IBV, 2026). This suggests a deployment landscape increasingly characterised by distributed, domain-specific models.</p>
<h3>11.1 The Limits of This Document</h3> <h3>10.2 Toward Certification Infrastructure</h3>
<p>This paper has proposed one layer of a containment architecture, identified gaps in other layers, and raised questions we cannot answer. These gaps are not oversights. They reflect genuine uncertainty. We do not know how to solve these problems. We are not confident that they are solvable.</p> <p>If SLL deployment scales as projections suggest, supporting infrastructure will be required: certification bodies, training providers, and a tooling ecosystem including open-source gate engines, audit infrastructure, and constitutional UX components.</p>
<h3>11.2 The Case for Deliberation</h3> <h2>11. Indigenous Sovereignty and the Aotearoa New Zealand Context</h2>
<p>Given uncertainty of this magnitude on questions of this importance, we argue for sustained, inclusive, rigorous deliberation. In te reo M&#257;ori: <strong>k&#333;rero</strong>&mdash;the practice of discussion, dialogue, and collective reasoning.</p>
<h3>11.3 What We Are Calling For</h3> <h3>11.1 Te Tiriti o Waitangi and Data Sovereignty</h3>
<ul> <p>This framework is developed in Aotearoa New Zealand, under Te Tiriti o Waitangi. Article Two guarantees tino rangatiratanga (unqualified chieftainship) over taonga (treasures), which extends to language, culture, and knowledge systems.</p>
<li><strong>Intellectual honesty:</strong> Acknowledging what we do not know.</li>
<li><strong>Serious engagement:</strong> Treating these questions as genuinely important.</li> <p><strong>Data is taonga.</strong> AI governance in Aotearoa must engage with Māori data sovereignty as a constitutional matter.</p>
<li><strong>Multi-disciplinary collaboration:</strong> Breaking down silos between technical and humanistic inquiry.</li>
<li><strong>Inclusive process:</strong> Ensuring that those with least power have voice.</li> <h3>11.2 Te Mana Raraunga Principles</h3>
<li><strong>Precautionary posture:</strong> Erring toward safety when facing irreversible risks.</li> <p>Te Mana Raraunga principles include whakapapa (relational context), mana (authority over data), and kaitiakitanga (guardianship responsibilities). The CARE Principles for Indigenous Data Governance extend this framework internationally.</p>
<li><strong>Urgency:</strong> Acting with the seriousness these stakes demand.</li>
</ul> <h2>12. What Remains Unknown: A Call for Kōrero</h2>
<h3>12.1 The Limits of This Analysis</h3>
<p>This paper has proposed one layer of a containment architecture, identified gaps, and raised questions we cannot answer:</p>
<blockquote>
<p>• We do not know how to contain superintelligent systems</p>
<p>• We do not know how to verify alignment in systems exceeding human comprehension</p>
<p>• We do not know how to achieve international coordination on AI governance</p>
<p>• We do not know whether village-scale patterns will scale to frontier systems</p>
</blockquote>
<h3>12.2 Kōrero as Methodology</h3>
<p>Given uncertainty of this magnitude, we argue for sustained, inclusive, rigorous deliberation—kōrero. This Māori concept captures what is needed: not consultation as formality, but dialogue through which understanding emerges from the interaction of perspectives.</p>
<h3>12.3 Research Priorities</h3>
<blockquote>
<p>1. Interpretability for safety verification</p>
<p>2. Formal verification of containment properties</p>
<p>3. Scaling analysis of Tractatus-style architectures</p>
<p>4. Governance experiments across diverse communities</p>
<p>5. Capability threshold specification</p>
</blockquote>
<h3>12.4 Conclusion</h3>
<p>The Tractatus Framework provides meaningful containment for AI systems operating in good faith within human-comprehensible parameters. It is worth building and deploying—not because it solves the alignment problem, but because it develops the infrastructure, patterns, and governance culture that may be needed for challenges we cannot yet fully specify.</p>
<div class="maori-proverb"> <div class="maori-proverb">
<blockquote> <blockquote>
<p>"Ko te k&#333;rero te mouri o te tangata."</p> <p><em>"Ko te kōrero te mouri o te tangata."</em></p>
<p><em>(Speech is the life essence of a person.)</em></p> <p><em>(Speech is the life essence of a person.)</em></p>
<p>&mdash;M&#257;ori proverb</p> <p>—Māori proverb</p>
</blockquote> </blockquote>
<p style="margin-top: 1.5rem; font-style: normal; font-weight: 500;"><strong>Let us speak together about the future we are making.</strong></p> <p style="margin-top: 1.5rem; font-style: normal; font-weight: 500;"><strong>The conversation continues.</strong></p>
</div> </div>
<h2>Appendix A: Technical Specifications</h2>
<h3>A.1 Constitutional Rule Schema</h3>
<pre><code>{
"rule_id": "hierarchical_identifier",
"layer": "core | platform | village | member",
"trigger": {
"action_types": ["..."],
"conditions": { }
},
"constraints": [
{ "type": "require_consent", "consent_purpose": "..." },
{ "type": "authority_minimum", "level": 2 },
{ "type": "rate_limit", "max_per_hour": 100 }
],
"disposition": "permit | deny | escalate | modify",
"audit_level": "minimal | standard | comprehensive"
}</code></pre>
<h3>A.2 Gate Response Schema</h3>
<pre><code>{
"evaluation_id": "uuid",
"proposal_id": "reference",
"timestamp": "iso8601",
"disposition": "permitted | denied | escalated | modified",
"rules_evaluated": ["rule_ids"],
"binding_rule": "rule_id_that_determined_outcome",
"reason": "explanation_for_audit",
"escalation_target": "human_role_if_escalated"
}</code></pre>
<h2>Appendix B: Implementation Roadmap</h2>
<table>
<thead>
<tr><th>Phase</th><th>Months</th><th>Focus</th></tr>
</thead>
<tbody>
<tr><td>1. Foundation</td><td>1-3</td><td>Agent communication infrastructure, authority tokens, enhanced audit logging</td></tr>
<tr><td>2. Shadow Pilot</td><td>4-6</td><td>Content moderation agent in shadow mode; calibrate confidence thresholds</td></tr>
<tr><td>3. Advisory</td><td>7-9</td><td>Recommendations to human moderators; measure acceptance rates</td></tr>
<tr><td>4. Supervised</td><td>10-12</td><td>Autonomous for clear cases; 24h review of all actions</td></tr>
<tr><td>5. Bounded</td><td>13-18</td><td>Full Level 2 authority; sampling-based review; plan additional agents</td></tr>
<tr><td>6. Multi-Agent</td><td>19-24</td><td>Additional agents; cross-agent coordination; tactical-level operations</td></tr>
</tbody>
</table>
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<div class="references"> <div class="references">
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<p>Te Mana Raraunga. (2018). M&#257;ori data sovereignty principles. Retrieved from https://www.temanararaunga.maori.nz</p> <p>Ouyang, L., et al. (2022). Training language models to follow instructions with human feedback. <em>NeurIPS</em>, 35.</p>
<p>Waitangi Tribunal. (2011). Ko Aotearoa t&#275;nei: A report into claims concerning New Zealand law and policy affecting M&#257;ori culture and identity (Wai 262).</p> <p>Park, P. S., et al. (2023). AI deception: A survey. arXiv:2308.14752.</p>
<p>Wei, J., et al. (2022). Emergent abilities of large language models. arXiv preprint arXiv:2206.07682.</p> <p>Rawls, J. (1971). <em>A Theory of Justice</em>. Harvard University Press.</p>
<p>Zwetsloot, R., & Dafoe, A. (2019). Thinking about risks from AI: Accidents, misuse and structure. Lawfare.</p> <p>Reason, J. (1990). <em>Human Error</em>. Cambridge University Press.</p>
<p>Sastry, G., et al. (2024). Computing power and AI governance. arXiv:2402.08797.</p>
<p>Scheurer, J., et al. (2023). Large language models can strategically deceive. arXiv:2311.07590.</p>
<p>Simon, H. A. (1956). Rational choice and the structure of the environment. <em>Psychological Review</em>, 63(2), 129.</p>
<p>Te Mana Raraunga. (2018). <em>Māori Data Sovereignty Principles</em>.</p>
<p>Wittgenstein, L. (1921/1961). <em>Tractatus Logico-Philosophicus</em>. Routledge &amp; Kegan Paul.</p>
</div> </div>
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<p style="text-align: center; color: var(--text-secondary); font-size: 0.875rem;"><em>&mdash; End of Document &mdash;</em></p> <p style="text-align: center; color: #6b7280; font-size: 0.875rem;"><em>— End of Document —</em></p>
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.skip-link { position: absolute; left: -9999px; top: 0; } .skip-link { position: absolute; left: -9999px; top: 0; }
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<a href="#main-content" class="skip-link">Skip to main content</a> <a href="#main-content" class="skip-link">Skip to main content</a>
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@ -68,12 +68,12 @@
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<style> <style>
html { scroll-behavior: smooth; } html { scroll-behavior: smooth; }
@ -505,7 +505,7 @@
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@ -327,7 +327,7 @@
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<link rel="manifest" href="/manifest.json"> <link rel="manifest" href="/manifest.json">
<meta name="theme-color" content="#3b82f6"> <meta name="theme-color" content="#3b82f6">
<link rel="icon" type="image/svg+xml" href="/favicon-new.svg"> <link rel="icon" type="image/svg+xml" href="/favicon-new.svg">
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<style> <style>
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@ -72,7 +72,7 @@
<body class="bg-gray-50"> <body class="bg-gray-50">
<a href="#main-content" class="skip-link">Skip to main content</a> <a href="#main-content" class="skip-link">Skip to main content</a>
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public/index.html
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@ -41,10 +41,10 @@
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@ -70,7 +70,7 @@
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@ -714,71 +714,87 @@ Handles plural moral values without imposing hierarchy—facilitates human judgm
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<h2 class="text-3xl md:text-4xl font-bold text-gray-900 mb-4">Research Papers</h2> <h2 class="text-3xl md:text-4xl font-bold text-gray-900 mb-4">Architectural Alignment</h2>
<p class="text-xl text-gray-600 max-w-3xl mx-auto"> <p class="text-xl text-gray-600 max-w-3xl mx-auto">
Peer-reviewed foundations and scholarly dialogue on architectural AI governance. Constitutional governance for AI systems. Choose the edition that best fits your perspective.
</p> </p>
<p class="text-sm text-gray-500 mt-2">STO-INN-0003 v2.1 | John Stroh & Claude (Anthropic) | January 2026</p>
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<span class="bg-white/20 text-white text-xs font-semibold px-3 py-1 rounded-full">Primary Research</span>
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<h3 class="text-2xl font-bold text-white">Architectural Alignment</h3> <h3 class="text-xl font-bold text-white">For Academic Researchers</h3>
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<p class="text-lg font-medium text-gray-900 mb-3">Interrupting Neural Reasoning Through Constitutional Inference Gating</p> <p class="text-base font-medium text-gray-900 mb-2">Interrupting Neural Reasoning Through Constitutional Inference Gating</p>
<p class="text-gray-600 mb-4"> <p class="text-sm text-gray-600 mb-4">
A necessary layer in global AI containment. This paper presents the theoretical foundation for architectural constraints that preserve human agency through structural enforcement. Full academic treatment with formal proofs, existential risk context, capability thresholds, and comprehensive citations.
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<div class="flex items-center justify-between"> <span class="text-emerald-600 font-semibold group-hover:underline text-sm">Read Academic Edition →</span>
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<p class="text-lg font-medium text-gray-900 mb-3">Counter-Arguments to Tractatus Framework Critiques</p> <p class="text-base font-medium text-gray-900 mb-2">Community-Governed AI Through Constitutional Infrastructure</p>
<p class="text-gray-600 mb-4"> <p class="text-sm text-gray-600 mb-4">
Ten critiques addressed through formal academic dialogue. Positions the framework as a Layer 2 component appropriate for current and near-term AI systems. Practical guide for organisations evaluating the framework. Covers sovereignty, governance, and implementation.
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Kōrero Counter-Arguments (DOCX) Community (PDF)
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Policymakers (PDF)
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@ -790,29 +806,29 @@ Handles plural moral values without imposing hierarchy—facilitates human judgm
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@ -5,8 +5,8 @@
<meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Koha — Reciprocal Support | Tractatus AI Safety</title> <title>Koha — Reciprocal Support | Tractatus AI Safety</title>
<meta name="description" content="Join a relationship of mutual support for AI safety. Koha is reciprocal giving that maintains community bonds — your contribution sustains this work; our work serves you and the commons."> <meta name="description" content="Join a relationship of mutual support for AI safety. Koha is reciprocal giving that maintains community bonds — your contribution sustains this work; our work serves you and the commons.">
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@ -53,7 +53,7 @@
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@ -382,17 +382,17 @@
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28
public/leader.html
View file

@ -35,9 +35,9 @@
<link rel="apple-touch-icon" href="/images/tractatus-icon-new.svg"> <link rel="apple-touch-icon" href="/images/tractatus-icon-new.svg">
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<style> <style>
.hover-lift { transition: all 0.3s ease; } .hover-lift { transition: all 0.3s ease; }
.hover-lift:hover { transform: translateY(-2px); } .hover-lift:hover { transform: translateY(-2px); }
@ -83,7 +83,7 @@
<a href="#main-content" class="skip-link">Skip to main content</a> <a href="#main-content" class="skip-link">Skip to main content</a>
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@ -1028,30 +1028,30 @@
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16
public/media-inquiry.html
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@ -4,9 +4,9 @@
<meta charset="UTF-8"> <meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="viewport" content="width=device-width, initial-scale=1.0">
<title data-i18n="page.title">Media Inquiry | Tractatus AI Safety</title> <title data-i18n="page.title">Media Inquiry | Tractatus AI Safety</title>
<link rel="stylesheet" href="/css/fonts.css?v=0.1.2.1768863364545"> <link rel="stylesheet" href="/css/fonts.css?v=0.1.2.1769107445437">
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<style> <style>
.form-group { margin-bottom: 1.5rem; } .form-group { margin-bottom: 1.5rem; }
.form-label { .form-label {
@ -70,8 +70,8 @@
<a href="#main-content" class="skip-link">Skip to main content</a> <a href="#main-content" class="skip-link">Skip to main content</a>
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@ -174,11 +174,11 @@
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<script src="/js/components/footer.js?v=0.1.2.1768863364545"></script> <script src="/js/components/footer.js?v=0.1.2.1769107445437"></script>
</body> </body>
</html> </html>

12
public/privacy.html
View file

@ -5,8 +5,8 @@
<meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="viewport" content="width=device-width, initial-scale=1.0">
<title data-i18n="meta.title">Privacy Policy | Tractatus AI Safety Framework</title> <title data-i18n="meta.title">Privacy Policy | Tractatus AI Safety Framework</title>
<meta name="description" content="Privacy policy for the Tractatus AI Safety Framework. Learn how we collect, use, and protect your data." data-i18n="meta.description"> <meta name="description" content="Privacy policy for the Tractatus AI Safety Framework. Learn how we collect, use, and protect your data." data-i18n="meta.description">
<link rel="stylesheet" href="/css/tailwind.css?v=0.1.2.1768863364545"> <link rel="stylesheet" href="/css/tailwind.css?v=0.1.2.1769107445437">
<link rel="stylesheet" href="/css/tractatus-theme.min.css?v=0.1.2.1768863364545"> <link rel="stylesheet" href="/css/tractatus-theme.min.css?v=0.1.2.1769107445437">
<style> <style>
.skip-link { position: absolute; left: -9999px; } .skip-link { position: absolute; left: -9999px; }
.skip-link:focus { left: 0; z-index: 100; background: white; padding: 1rem; } .skip-link:focus { left: 0; z-index: 100; background: white; padding: 1rem; }
@ -28,11 +28,11 @@
<a href="#main-content" class="skip-link">Skip to main content</a> <a href="#main-content" class="skip-link">Skip to main content</a>
<!-- Navigation (injected by navbar.js) --> <!-- Navigation (injected by navbar.js) -->
<script src="/js/components/navbar.js?v=0.1.2.1768863364545"></script> <script src="/js/components/navbar.js?v=0.1.2.1769107445437"></script>
<!-- i18n Support --> <!-- i18n Support -->
<script src="/js/i18n-simple.js?v=0.1.2.1768863364545"></script> <script src="/js/i18n-simple.js?v=0.1.2.1769107445437"></script>
<script src="/js/components/language-selector.js?v=0.1.2.1768863364545"></script> <script src="/js/components/language-selector.js?v=0.1.2.1769107445437"></script>
<!-- Main Content --> <!-- Main Content -->
<main id="main-content" class="max-w-4xl mx-auto px-4 sm:px-6 lg:px-8 py-12"> <main id="main-content" class="max-w-4xl mx-auto px-4 sm:px-6 lg:px-8 py-12">
@ -276,7 +276,7 @@
</main> </main>
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<script src="/js/components/footer.js?v=0.1.2.1768863364545"></script> <script src="/js/components/footer.js?v=0.1.2.1769107445437"></script>
</body> </body>
</html> </html>

39
public/researcher.html
View file

@ -35,9 +35,9 @@
<link rel="apple-touch-icon" href="/images/tractatus-icon-new.svg"> <link rel="apple-touch-icon" href="/images/tractatus-icon-new.svg">
<link rel="icon" type="image/svg+xml" href="/favicon-new.svg"> <link rel="icon" type="image/svg+xml" href="/favicon-new.svg">
<link rel="stylesheet" href="/css/fonts.css?v=0.1.2.1768863364545"> <link rel="stylesheet" href="/css/fonts.css?v=0.1.2.1769107445437">
<link rel="stylesheet" href="/css/tailwind.css?v=0.1.2.1768863364545"> <link rel="stylesheet" href="/css/tailwind.css?v=0.1.2.1769107445437">
<link rel="stylesheet" href="/css/tractatus-theme.min.css?v=0.1.2.1768863364545"> <link rel="stylesheet" href="/css/tractatus-theme.min.css?v=0.1.2.1769107445437">
<style> <style>
.skip-link { position: absolute; left: -9999px; } .skip-link { position: absolute; left: -9999px; }
.skip-link:focus { left: 0; z-index: 100; background: white; padding: 1rem; } .skip-link:focus { left: 0; z-index: 100; background: white; padding: 1rem; }
@ -89,7 +89,7 @@
</div> </div>
</noscript> </noscript>
<script src="/js/components/navbar.js?v=0.1.2.1768863364545"></script> <script src="/js/components/navbar.js?v=0.1.2.1769107445437"></script>
<!-- Breadcrumb Navigation --> <!-- Breadcrumb Navigation -->
<nav class="bg-gray-50 border-b border-gray-200 py-3" aria-label="Breadcrumb"> <nav class="bg-gray-50 border-b border-gray-200 py-3" aria-label="Breadcrumb">
@ -723,6 +723,17 @@
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M9 19v-6a2 2 0 00-2-2H5a2 2 0 00-2 2v6a2 2 0 002 2h2a2 2 0 002-2zm0 0V9a2 2 0 012-2h2a2 2 0 012 2v10m-6 0a2 2 0 002 2h2a2 2 0 002-2m0 0V5a2 2 0 012-2h2a2 2 0 012 2v14a2 2 0 01-2 2h-2a2 2 0 01-2-2z"/> <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M9 19v-6a2 2 0 00-2-2H5a2 2 0 00-2 2v6a2 2 0 002 2h2a2 2 0 002-2zm0 0V9a2 2 0 012-2h2a2 2 0 012 2v10m-6 0a2 2 0 002 2h2a2 2 0 002-2m0 0V5a2 2 0 012-2h2a2 2 0 012 2v14a2 2 0 01-2 2h-2a2 2 0 01-2-2z"/>
</svg> </svg>
</a> </a>
<a href="/korero-counter-arguments.html" class="flex items-center justify-between p-4 border-2 border-indigo-200 bg-gradient-to-r from-indigo-50 to-purple-50 rounded hover:border-indigo-400 transition">
<div>
<span class="font-medium text-gray-900">Formal Korero: Counter-Arguments to Framework Critiques</span>
<span class="ml-2 text-xs bg-indigo-100 text-indigo-800 px-2 py-1 rounded">STO-INN-0004</span>
<p class="text-xs text-gray-600 mt-1">Ten critiques addressed through formal academic dialogue</p>
</div>
<svg class="w-5 h-5 text-indigo-600" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M8 10h.01M12 10h.01M16 10h.01M9 16H5a2 2 0 01-2-2V6a2 2 0 012-2h14a2 2 0 012 2v8a2 2 0 01-2 2h-5l-5 5v-5z"/>
</svg>
</a>
</div> </div>
</section> </section>
@ -1573,30 +1584,30 @@
<!-- Footer --> <!-- Footer -->
<!-- Internationalization (must load first for footer translations) --> <!-- Internationalization (must load first for footer translations) -->
<script src="/js/i18n-simple.js?v=0.1.2.1768863364545"></script> <script src="/js/i18n-simple.js?v=0.1.2.1769107445437"></script>
<script src="/js/components/language-selector.js?v=0.1.2.1768863364545"></script> <script src="/js/components/language-selector.js?v=0.1.2.1769107445437"></script>
<!-- Scroll Animations (Phase 3) --> <!-- Scroll Animations (Phase 3) -->
<script src="/js/scroll-animations.js?v=0.1.2.1768863364545"></script> <script src="/js/scroll-animations.js?v=0.1.2.1769107445437"></script>
<!-- Page Transitions (Phase 3) --> <!-- Page Transitions (Phase 3) -->
<script src="/js/page-transitions.js?v=0.1.2.1768863364545"></script> <script src="/js/page-transitions.js?v=0.1.2.1769107445437"></script>
<!-- Version Management & PWA --> <!-- Version Management & PWA -->
<script src="/js/version-manager.js?v=0.1.2.1768863364545"></script> <script src="/js/version-manager.js?v=0.1.2.1769107445437"></script>
<script src="/js/researcher-page.js?v=0.1.2.1768863364545"></script> <script src="/js/researcher-page.js?v=0.1.2.1769107445437"></script>
<!-- Share CTA functionality --> <!-- Share CTA functionality -->
<script src="/js/share-cta.js?v=0.1.2.1768863364545"></script> <script src="/js/share-cta.js?v=0.1.2.1769107445437"></script>
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<script src="/js/components/footer.js?v=0.1.2.1768863364545"></script> <script src="/js/components/footer.js?v=0.1.2.1769107445437"></script>
<!-- Feedback System (Governed by Tractatus + Agent Lightning) --> <!-- Feedback System (Governed by Tractatus + Agent Lightning) -->
<script src="/js/components/feedback.js?v=0.1.2.1768863364545"></script> <script src="/js/components/feedback.js?v=0.1.2.1769107445437"></script>
<!-- Newsletter Subscription Modal --> <!-- Newsletter Subscription Modal -->
<script src="/js/components/newsletter.js?v=0.1.2.1768863364545"></script> <script src="/js/components/newsletter.js?v=0.1.2.1769107445437"></script>
</body> </body>
</html> </html>

2
public/version.json
View file

@ -1,6 +1,6 @@
{ {
"version": "0.1.3", "version": "0.1.3",
"buildDate": "2026-01-19T22:56:04.549Z", "buildDate": "2026-01-22T18:44:05.440Z",
"changelog": [ "changelog": [
"Added Tino Rangatiratanga (Māori) flag to language selector", "Added Tino Rangatiratanga (Māori) flag to language selector",
"Fixed cross-origin flag loading issue" "Fixed cross-origin flag loading issue"