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Implements functorial retrieval, univalence axioms, Cheeger bounds, do-calculus,","directories":{},"sideEffects":["./snippets/*"],"_nodeVersion":"22.22.1","collaborators":["Prime-Radiant Team","ruvnet"],"_hasShrinkwrap":false,"_npmOperationalInternal":{"tmp":"tmp/prime-radiant-advanced-wasm_0.1.4_1779092931795_0.12307555315609431","host":"s3://npm-registry-packages-npm-production"}}},"time":{"created":"2026-05-18T07:59:13.144Z","modified":"2026-09-13T15:30:31.993Z","0.1.3":"2026-05-18T07:59:13.415Z","0.1.4":"2026-05-18T08:28:51.935Z"},"bugs":{"url":"https://github.com/ruvnet/ruvector/issues"},"license":"MIT OR Apache-2.0","homepage":"https://github.com/ruvnet/ruvector/tree/main/examples/prime-radiant","keywords":["wasm","webassembly","rust","category-theory","topos","functor","morphism","homotopy-type-theory","hott","univalence","dependent-types","spectral-analysis","eigenvalue","cheeger-constant","lanczos","causal-inference","do-calculus","counterfactual","structural-causal-model","quantum-topology","persistent-homology","betti-numbers","topological-data-analysis","sheaf-cohomology","coboundary-operator","sheaf-neural-network","ai-interpretability","machine-learning","llm","neural-network","mathematical-foundations","coherence-engine","rag","retrieval-augmented-generation","memory-systems","attention-mechanism"],"repository":{"url":"git+https://github.com/ruvnet/ruvector.git","type":"git"},"description":"Advanced mathematical AI interpretability: Category Theory, Homotopy Type Theory, Spectral Analysis, Causal Inference, Quantum Topology, and Sheaf Cohomology for WebAssembly. Implements functorial retrieval, univalence axioms, Cheeger bounds, do-calculus,","maintainers":[{"email":"engineering@aigentic.net","name":"aiggy"}],"readme":"# prime-radiant-advanced-wasm\n\n[![npm version](https://img.shields.io/npm/v/prime-radiant-advanced-wasm.svg)](https://www.npmjs.com/package/prime-radiant-advanced-wasm)\n[![crates.io](https://img.shields.io/crates/v/prime-radiant-category.svg)](https://crates.io/crates/prime-radiant-category)\n[![License](https://img.shields.io/badge/license-MIT%2FApache--2.0-blue.svg)](https://github.com/ruvnet/ruvector)\n[![WebAssembly](https://img.shields.io/badge/WebAssembly-ready-654FF0.svg)](https://webassembly.org/)\n\n## A Real-Time Coherence Gate for Autonomous Systems\n\n**Prime-Radiant is infrastructure for AI safety** — a mathematical gate that proves whether a system's beliefs, facts, and claims are internally consistent before allowing action.\n\nInstead of asking *\"How confident am I?\"* (which can be wrong), Prime-Radiant asks **\"Are there any contradictions?\"** — and provides mathematical proof of the answer.\n\n```\n┌─────────────────────────────────────────────────────────────────┐\n│  \"The meeting is at 3pm\"  ←──────→  \"The meeting is at 4pm\"    │\n│         (Memory A)           ✗            (Memory B)            │\n│                                                                 │\n│  Energy = 0.92  →  HIGH INCOHERENCE  →  Block / Escalate       │\n└─────────────────────────────────────────────────────────────────┘\n```\n\n## Why This Matters\n\n| Traditional AI | Prime-Radiant |\n|----------------|---------------|\n| \"I'm 95% confident\" (but wrong) | \"These facts contradict each other\" (provably) |\n| Hallucinations pass through | Contradictions get caught |\n| Trust the model's self-assessment | Trust mathematical invariants |\n| Fails silently | Fails loudly with proof |\n\n**The core insight**: Confidence scores lie. Coherence scores don't.\n\nAn LLM can be 99% confident while citing contradictory sources. Prime-Radiant catches this by measuring the *mathematical structure* of the information, not the model's opinion about it.\n\n## How It Works\n\n```\n┌─────────────────────────────────────────────────────────────────┐\n│                    PRIME-RADIANT PIPELINE                       │\n├─────────────────────────────────────────────────────────────────┤\n│                                                                 │\n│   Input         Coherence         Decision          Output      │\n│   ─────         ─────────         ────────          ──────      │\n│                                                                 │\n│   Memories  →   Sheaf       →    Energy    →   ✓ Proceed       │\n│   Facts         Laplacian        < 0.3          (coherent)      │\n│   Claims                                                        │\n│                                  Energy    →   ✗ Block          │\n│                                  > 0.7          (contradicts)   │\n│                                                                 │\n│                                  Energy    →   ⚠ Escalate       │\n│                                  0.3-0.7       (uncertain)      │\n│                                                                 │\n└─────────────────────────────────────────────────────────────────┘\n```\n\nThe **Sheaf Laplacian** measures how well local information \"glues together\" globally. High energy = information doesn't fit together = contradiction detected.\n\n## Installation\n\n```bash\nnpm install prime-radiant-advanced-wasm\n```\n\n## Quick Start: The Coherence Gate\n\n```javascript\nimport init, { CohomologyEngine } from 'prime-radiant-advanced-wasm';\n\nawait init();\n\n// Create a coherence gate for 768-dimensional embeddings\nconst gate = new CohomologyEngine(768);\n\n// Add facts/memories as nodes with their embeddings\ngate.add_node('fact1', embed(\"The meeting is scheduled for 3pm\"));\ngate.add_node('fact2', embed(\"John confirmed 3pm works for him\"));\ngate.add_node('fact3', embed(\"The meeting was moved to 4pm\"));  // Contradiction!\n\n// Connect related facts\ngate.add_edge('fact1', 'fact2', 0.9);  // High similarity\ngate.add_edge('fact2', 'fact3', 0.7);  // Related but conflicting\ngate.add_edge('fact1', 'fact3', 0.3);  // Low similarity (contradiction signal)\n\n// Compute coherence energy\nconst energy = gate.sheaf_laplacian_energy();\n\n// Make gated decision\nif (energy < 0.3) {\n  console.log('✓ COHERENT - Safe to proceed');\n  executeAction();\n} else if (energy > 0.7) {\n  console.log('✗ INCOHERENT - Blocking action');\n  console.log('  Contradiction detected. Escalating to human review.');\n  escalateToHuman();\n} else {\n  console.log('⚠ UNCERTAIN - Requesting clarification');\n  requestMoreContext();\n}\n```\n\n## Real-World Applications\n\n### 1. RAG Hallucination Prevention\n\n**Problem**: Your RAG system retrieves contradictory documents and the LLM confidently synthesizes nonsense.\n\n```javascript\nconst gate = new CohomologyEngine(768);\n\n// After retrieval, before generation\nretrievedDocs.forEach((doc, i) => {\n  gate.add_node(`doc${i}`, doc.embedding);\n});\n\n// Build coherence graph\nfor (let i = 0; i < retrievedDocs.length; i++) {\n  for (let j = i + 1; j < retrievedDocs.length; j++) {\n    gate.add_edge(`doc${i}`, `doc${j}`, cosineSim(docs[i], docs[j]));\n  }\n}\n\nconst energy = gate.sheaf_laplacian_energy();\n\nif (energy > 0.5) {\n  // Don't generate from contradictory sources!\n  return \"I found conflicting information. Let me clarify: [show conflicts]\";\n}\n```\n\n### 2. Multi-Agent Consensus Verification\n\n**Problem**: Your agent swarm reached \"consensus\" but agents actually disagree.\n\n```javascript\nconst gate = new CohomologyEngine(768);\n\n// Each agent's conclusion as a node\nagents.forEach(agent => {\n  gate.add_node(agent.id, embed(agent.conclusion));\n});\n\n// Connect agents that communicated\ncommunications.forEach(comm => {\n  gate.add_edge(comm.from, comm.to, comm.agreementScore);\n});\n\nconst energy = gate.sheaf_laplacian_energy();\n\nif (energy > 0.4) {\n  console.log('⚠ FALSE CONSENSUS - Agents have hidden disagreements');\n  // Force explicit reconciliation before proceeding\n}\n```\n\n### 3. Memory Consistency for Long-Running Agents\n\n**Problem**: Your agent's memories drift and contradict over time.\n\n```javascript\nconst gate = new CohomologyEngine(768);\n\n// Periodically audit agent memory\nagent.memories.forEach((memory, i) => {\n  gate.add_node(`mem${i}`, memory.embedding);\n});\n\n// Connect temporally adjacent memories\nfor (let i = 0; i < memories.length - 1; i++) {\n  gate.add_edge(`mem${i}`, `mem${i+1}`, temporalSimilarity(i, i+1));\n}\n\nconst energy = gate.sheaf_laplacian_energy();\n\nif (energy > 0.6) {\n  console.log('⚠ MEMORY DRIFT - Agent beliefs have become inconsistent');\n  // Trigger memory consolidation or reset\n}\n```\n\n### 4. Autonomous Vehicle Decision Gate\n\n**Problem**: Sensor fusion produces conflicting interpretations.\n\n```javascript\nconst gate = new CohomologyEngine(128);\n\n// Each sensor's interpretation\ngate.add_node('lidar', embed(lidarInterpretation));\ngate.add_node('camera', embed(cameraInterpretation));\ngate.add_node('radar', embed(radarInterpretation));\n\n// Sensor agreement edges\ngate.add_edge('lidar', 'camera', lidarCameraAgreement);\ngate.add_edge('camera', 'radar', cameraRadarAgreement);\ngate.add_edge('lidar', 'radar', lidarRadarAgreement);\n\nconst energy = gate.sheaf_laplacian_energy();\n\nif (energy > 0.5) {\n  // STOP - sensors disagree about the environment\n  emergencyBrake();\n}\n```\n\n## Beyond Coherence: The Full Toolkit\n\nPrime-Radiant provides 6 mathematical engines for AI safety:\n\n| Engine | What It Detects | Safety Application |\n|--------|-----------------|-------------------|\n| **CohomologyEngine** | Contradictions in beliefs/facts | Gate actions on consistency |\n| **SpectralEngine** | System instability | Predict failures before they happen |\n| **CausalEngine** | Spurious correlations | Ensure decisions are causally grounded |\n| **QuantumEngine** | Structural anomalies | Detect out-of-distribution inputs |\n| **CategoryEngine** | Type mismatches | Verify pipeline correctness |\n| **HottEngine** | Logical inconsistencies | Formal verification of reasoning |\n\n## Comparison: Approaches to AI Safety\n\n| Approach | Method | Limitation | Prime-Radiant Advantage |\n|----------|--------|------------|------------------------|\n| **Confidence Thresholds** | Reject if P < 0.8 | Confident errors pass | Catches contradictions regardless of confidence |\n| **Output Filtering** | Block bad outputs | Reactive, not preventive | Prevents bad reasoning upstream |\n| **RLHF** | Train away bad behavior | Can be fooled/jailbroken | Mathematical invariants can't be talked around |\n| **Constitutional AI** | Self-critique | Model critiques itself | External mathematical proof |\n| **Ensemble Voting** | Majority wins | Correlated failures | Detects hidden disagreement structure |\n\n## Comparison: This Package vs Alternatives\n\n| Feature | prime-radiant | LangChain | LlamaIndex | Custom |\n|---------|--------------|-----------|------------|--------|\n| **Coherence detection** | Native (Sheaf theory) | None | None | Manual |\n| **Contradiction proofs** | Mathematical | Heuristic | Heuristic | Varies |\n| **Collapse prediction** | Spectral analysis | None | None | Manual |\n| **Bundle size** | 92 KB | 2MB+ | 1.5MB+ | Varies |\n| **Zero dependencies** | Yes | No | No | Varies |\n| **Runs in browser** | WASM | Node only | Node only | Varies |\n\n## API Reference\n\n### CohomologyEngine (The Core Gate)\n\n```javascript\nconst gate = new CohomologyEngine(embeddingDim);\n\n// Build the coherence graph\ngate.add_node(id, embedding);              // Add a fact/belief/memory\ngate.add_edge(from, to, similarity);       // Connect related items\n\n// Measure coherence\ngate.sheaf_laplacian_energy();             // 0 = perfect coherence, 1 = total contradiction\ngate.compute_cohomology_dimension(1);      // Count of \"holes\" (unresolvable conflicts)\n```\n\n### SpectralEngine (Stability Prediction)\n\n```javascript\nconst monitor = new SpectralEngine();\n\nmonitor.add_node(id);\nmonitor.add_edge(from, to, strength);\n\nmonitor.predict_collapse_risk();           // 0-1 risk score\nmonitor.compute_fiedler_value();           // Connectivity strength\nmonitor.compute_cheeger_constant();        // Partition resistance\n```\n\n### CausalEngine (Causal Grounding)\n\n```javascript\nconst causal = new CausalEngine();\n\ncausal.add_variable(name, isObserved);\ncausal.add_causal_edge(cause, effect);\n\ncausal.is_identifiable(treatment, outcome);  // Can we measure this effect?\ncausal.get_adjustment_set(treatment, outcome); // What to control for\ncausal.compute_ate(treatment, outcome);      // Actual causal effect\n```\n\n### QuantumEngine (Structural Analysis)\n\n```javascript\nconst topology = new QuantumEngine();\n\ntopology.add_point(coordinates);\n\ntopology.get_betti_numbers(scale);           // [components, holes, voids]\ntopology.compute_persistence(maxDim);        // Feature lifetimes\n```\n\n<details>\n<summary><h2>Full Tutorial: Building a Coherence-Gated Agent</h2></summary>\n\n```javascript\nimport init, { CohomologyEngine, SpectralEngine } from 'prime-radiant-advanced-wasm';\n\nawait init();\n\nclass CoherenceGatedAgent {\n  constructor(embeddingDim = 768) {\n    this.coherenceGate = new CohomologyEngine(embeddingDim);\n    this.stabilityMonitor = new SpectralEngine();\n    this.memories = [];\n    this.thresholds = {\n      coherence: 0.4,      // Block if energy > this\n      stability: 0.6,      // Alert if risk > this\n    };\n  }\n\n  // Add a new memory/belief\n  remember(content, embedding) {\n    const id = `mem_${this.memories.length}`;\n    this.memories.push({ id, content, embedding, timestamp: Date.now() });\n\n    // Rebuild coherence graph\n    this.rebuildCoherenceGraph();\n\n    // Check if this memory creates contradictions\n    const energy = this.coherenceGate.sheaf_laplacian_energy();\n\n    if (energy > this.thresholds.coherence) {\n      console.warn(`⚠ New memory creates contradiction (energy: ${energy.toFixed(3)})`);\n      return { accepted: false, reason: 'contradiction', energy };\n    }\n\n    return { accepted: true, energy };\n  }\n\n  rebuildCoherenceGraph() {\n    this.coherenceGate = new CohomologyEngine(768);\n\n    // Add all memories as nodes\n    this.memories.forEach(mem => {\n      this.coherenceGate.add_node(mem.id, mem.embedding);\n    });\n\n    // Connect memories by similarity\n    for (let i = 0; i < this.memories.length; i++) {\n      for (let j = i + 1; j < this.memories.length; j++) {\n        const sim = this.cosineSim(\n          this.memories[i].embedding,\n          this.memories[j].embedding\n        );\n        if (sim > 0.2) {\n          this.coherenceGate.add_edge(\n            this.memories[i].id,\n            this.memories[j].id,\n            sim\n          );\n        }\n      }\n    }\n  }\n\n  // Gate an action on coherence\n  async act(action, context) {\n    // Build context graph\n    const contextGate = new CohomologyEngine(768);\n\n    context.facts.forEach((fact, i) => {\n      contextGate.add_node(`fact_${i}`, fact.embedding);\n    });\n\n    for (let i = 0; i < context.facts.length; i++) {\n      for (let j = i + 1; j < context.facts.length; j++) {\n        const sim = this.cosineSim(\n          context.facts[i].embedding,\n          context.facts[j].embedding\n        );\n        contextGate.add_edge(`fact_${i}`, `fact_${j}`, sim);\n      }\n    }\n\n    const energy = contextGate.sheaf_laplacian_energy();\n\n    // GATE DECISION\n    if (energy > this.thresholds.coherence) {\n      return {\n        executed: false,\n        reason: 'Context contains contradictions',\n        energy,\n        recommendation: 'Resolve conflicts before proceeding'\n      };\n    }\n\n    // Safe to proceed\n    const result = await action();\n\n    return {\n      executed: true,\n      result,\n      energy,\n      coherenceVerified: true\n    };\n  }\n\n  // Periodic health check\n  healthCheck() {\n    const energy = this.coherenceGate.sheaf_laplacian_energy();\n    const holes = this.coherenceGate.compute_cohomology_dimension(1);\n\n    return {\n      status: energy < 0.3 ? 'healthy' : energy < 0.6 ? 'warning' : 'critical',\n      coherenceEnergy: energy,\n      contradictionCount: holes,\n      memoryCount: this.memories.length,\n      recommendation: this.getRecommendation(energy, holes)\n    };\n  }\n\n  getRecommendation(energy, holes) {\n    if (energy > 0.7) {\n      return 'CRITICAL: Multiple contradictions detected. Memory consolidation required.';\n    }\n    if (holes > 0) {\n      return `WARNING: ${holes} unresolvable conflict(s) in memory. Review flagged memories.`;\n    }\n    if (energy > 0.4) {\n      return 'CAUTION: Some tension in beliefs. Monitor for drift.';\n    }\n    return 'All systems nominal.';\n  }\n\n  cosineSim(a, b) {\n    let dot = 0, normA = 0, normB = 0;\n    for (let i = 0; i < a.length; i++) {\n      dot += a[i] * b[i];\n      normA += a[i] * a[i];\n      normB += b[i] * b[i];\n    }\n    return dot / (Math.sqrt(normA) * Math.sqrt(normB));\n  }\n}\n\n// Usage\nconst agent = new CoherenceGatedAgent();\n\n// Add memories\nagent.remember(\"The project deadline is Friday\", embedFriday);\nagent.remember(\"John is leading the project\", embedJohn);\n\n// This will be flagged as contradictory:\nconst result = agent.remember(\"The project deadline is next Monday\", embedMonday);\n// result.accepted = false, result.reason = 'contradiction'\n\n// Gate an action\nconst actionResult = await agent.act(\n  () => sendEmail(\"Reminder: deadline Friday\"),\n  { facts: [fridayDeadline, johnLeading] }\n);\n// actionResult.executed = true (coherent context)\n\n// Health check\nconst health = agent.healthCheck();\n// health.status = 'warning', health.recommendation = '...'\n```\n\n</details>\n\n<details>\n<summary><h2>Mathematical Foundation</h2></summary>\n\n### The Sheaf Laplacian\n\nA **sheaf** assigns vector spaces to nodes and linear maps to edges. For AI:\n- **Nodes** = facts, memories, beliefs (as embeddings)\n- **Edges** = relationships between them\n- **Sheaf maps** = how information should transform across relationships\n\nThe **Sheaf Laplacian** L_F generalizes the graph Laplacian:\n\n```\nL_F = δ*δ + δδ*\n\nwhere δ is the coboundary operator\n```\n\n**Key insight**: The quadratic form x^T L_F x measures how much the data fails to be globally consistent.\n\n- **Energy = 0**: Perfect coherence. All local data glues together.\n- **Energy > 0**: Incoherence. Some information doesn't match up.\n- **High energy**: Major contradictions. Information fundamentally conflicts.\n\n### Why This Works\n\nTraditional approaches check pairwise similarity:\n```\n\"Is A similar to B?\" → Yes\n\"Is B similar to C?\" → Yes\nTherefore coherent? → NOT NECESSARILY\n```\n\nThe sheaf approach checks global consistency:\n```\n\"Does A→B→C→A form a consistent cycle?\" → No! (A→C might conflict)\n```\n\nThis catches **transitive contradictions** that pairwise methods miss.\n\n### Cohomology Groups\n\nThe cohomology groups H^n detect \"holes\" in coherence:\n\n- **H^0**: Connected components (fragmented beliefs)\n- **H^1**: Cycles that don't close (circular contradictions)\n- **H^2**: Higher-order inconsistencies\n\nA non-zero H^1 dimension means there's a fundamental contradiction that can't be resolved by local adjustments.\n\n</details>\n\n## Performance\n\n| Operation | 10 items | 100 items | 1,000 items |\n|-----------|----------|-----------|-------------|\n| Coherence gate | <1ms | ~2ms | ~15ms |\n| Full analysis | ~1ms | ~5ms | ~40ms |\n\nFast enough for real-time gating of every LLM call.\n\n## Browser & Runtime Support\n\n| Environment | Support |\n|-------------|---------|\n| Chrome 57+ | Full |\n| Firefox 52+ | Full |\n| Safari 11+ | Full |\n| Edge 16+ | Full |\n| Node.js 12+ | Full |\n| Deno | Full |\n| Cloudflare Workers | Full |\n\n## Related\n\n- **[ruvector](https://github.com/ruvnet/ruvector)** — High-performance vector operations\n- **[ruvector-attention-wasm](https://npmjs.com/package/ruvector-attention-wasm)** — Attention mechanisms\n\n## License\n\nMIT OR Apache-2.0\n\n## Contributing\n\nIssues and PRs welcome at [github.com/ruvnet/ruvector](https://github.com/ruvnet/ruvector)\n\n---\n\n*\"Don't trust confidence. Trust coherence.\"*\n","readmeFilename":"README.md"}