ruvnet--ruflo/v3/implementation/adrs/ADR-041-hyperbolic-reasoning-plugin.md

ADR-041: Hyperbolic Reasoning Plugin

Status: Proposed Date: 2026-01-24 Category: Exotic SOTA Author: Plugin Architecture Team Version: 1.0.0 Deciders: Plugin Architecture Team, Geometric ML Research Team Supersedes: None

Context

Many real-world data structures exhibit hierarchical properties: file systems, organizational charts, taxonomies, dependency trees, and concept hierarchies. Traditional Euclidean embeddings struggle to represent these structures efficiently, requiring exponentially more dimensions. Hyperbolic geometry, with its natural tree-like structure, can represent hierarchies with logarithmic distortion using far fewer dimensions.

Decision

Create a Hyperbolic Reasoning Plugin that leverages RuVector's hyperbolic WASM packages to provide superior hierarchical reasoning, taxonomy navigation, and semantic relationship modeling through Poincare ball and Lorentz model embeddings.

Plugin Name

@claude-flow/plugin-hyperbolic-reasoning

Description

An exotic reasoning plugin implementing hyperbolic neural networks for superior hierarchical understanding. The plugin enables efficient representation of tree structures, taxonomic reasoning, and hierarchical entailment using Poincare ball embeddings with Mobius operations. Applications include improved ontology navigation, hierarchical code understanding, and organizational relationship modeling.

Key WASM Packages

Package	Purpose
ruvector-hyperbolic-hnsw-wasm	Hyperbolic nearest neighbor search
ruvector-attention-wasm	Hyperbolic attention mechanisms
ruvector-gnn-wasm	Hyperbolic graph neural networks
micro-hnsw-wasm	Tangent space approximation search
sona	Adaptive curvature learning

MCP Tools

1. hyperbolic/embed-hierarchy

Embed hierarchical data in hyperbolic space.

{
  name: 'hyperbolic/embed-hierarchy',
  description: 'Embed hierarchical structure in Poincare ball',
  inputSchema: {
    type: 'object',
    properties: {
      hierarchy: {
        type: 'object',
        description: 'Tree structure to embed',
        properties: {
          nodes: {
            type: 'array',
            items: {
              type: 'object',
              properties: {
                id: { type: 'string' },
                parent: { type: 'string' },
                features: { type: 'object' }
              }
            }
          },
          edges: { type: 'array', items: { type: 'object' } }
        }
      },
      model: {
        type: 'string',
        enum: ['poincare_ball', 'lorentz', 'klein', 'half_plane'],
        default: 'poincare_ball'
      },
      parameters: {
        type: 'object',
        properties: {
          dimensions: { type: 'number', default: 32 },
          curvature: { type: 'number', default: -1.0 },
          learnCurvature: { type: 'boolean', default: true }
        }
      }
    },
    required: ['hierarchy']
  }
}

2. hyperbolic/taxonomic-reason

Perform taxonomic reasoning in hyperbolic space.

{
  name: 'hyperbolic/taxonomic-reason',
  description: 'Taxonomic reasoning using hyperbolic entailment',
  inputSchema: {
    type: 'object',
    properties: {
      query: {
        type: 'object',
        properties: {
          type: {
            type: 'string',
            enum: ['is_a', 'subsumption', 'lowest_common_ancestor', 'path', 'similarity']
          },
          subject: { type: 'string' },
          object: { type: 'string' }
        }
      },
      taxonomy: { type: 'string', description: 'Taxonomy identifier' },
      inference: {
        type: 'object',
        properties: {
          transitive: { type: 'boolean', default: true },
          fuzzy: { type: 'boolean', default: false },
          confidence: { type: 'number', default: 0.8 }
        }
      }
    },
    required: ['query']
  }
}

3. hyperbolic/semantic-search

Hierarchically-aware semantic search.

{
  name: 'hyperbolic/semantic-search',
  description: 'Semantic search with hierarchical awareness',
  inputSchema: {
    type: 'object',
    properties: {
      query: { type: 'string' },
      index: { type: 'string', description: 'Hyperbolic index identifier' },
      searchMode: {
        type: 'string',
        enum: ['nearest', 'subtree', 'ancestors', 'siblings', 'cone'],
        default: 'nearest'
      },
      constraints: {
        type: 'object',
        properties: {
          maxDepth: { type: 'number' },
          minDepth: { type: 'number' },
          subtreeRoot: { type: 'string' }
        }
      },
      topK: { type: 'number', default: 10 }
    },
    required: ['query', 'index']
  }
}

4. hyperbolic/hierarchy-compare

Compare hierarchical structures using hyperbolic alignment.

{
  name: 'hyperbolic/hierarchy-compare',
  description: 'Compare hierarchies using hyperbolic alignment',
  inputSchema: {
    type: 'object',
    properties: {
      source: { type: 'object', description: 'First hierarchy' },
      target: { type: 'object', description: 'Second hierarchy' },
      alignment: {
        type: 'string',
        enum: ['wasserstein', 'gromov_wasserstein', 'tree_edit', 'subtree_isomorphism'],
        default: 'gromov_wasserstein'
      },
      metrics: {
        type: 'array',
        items: {
          type: 'string',
          enum: ['structural_similarity', 'semantic_similarity', 'coverage', 'precision']
        }
      }
    },
    required: ['source', 'target']
  }
}

5. hyperbolic/entailment-graph

Build and query entailment graphs.

{
  name: 'hyperbolic/entailment-graph',
  description: 'Build entailment graph using hyperbolic embeddings',
  inputSchema: {
    type: 'object',
    properties: {
      action: {
        type: 'string',
        enum: ['build', 'query', 'expand', 'prune']
      },
      concepts: {
        type: 'array',
        items: {
          type: 'object',
          properties: {
            id: { type: 'string' },
            text: { type: 'string' },
            type: { type: 'string' }
          }
        }
      },
      entailmentThreshold: { type: 'number', default: 0.7 },
      transitiveClosure: { type: 'boolean', default: true },
      pruneStrategy: {
        type: 'string',
        enum: ['none', 'transitive_reduction', 'confidence_threshold']
      }
    },
    required: ['action']
  }
}

Use Cases

1. Ontology Navigation: Efficiently traverse and query large knowledge graphs
2. Code Hierarchy Understanding: Model inheritance, module structure, and dependencies
3. Organizational Analysis: Understand reporting structures and influence networks
4. Taxonomic Classification: Improved classification with hierarchical awareness
5. Concept Entailment: Determine semantic relationships between concepts

Architecture

+------------------+     +----------------------+     +------------------+
| Hierarchical     |---->| Hyperbolic Engine    |---->| Reasoning        |
| Data Input       |     | (Poincare Ball)      |     | Results          |
+------------------+     +----------------------+     +------------------+
                                   |
              +--------------------+--------------------+
              |                    |                    |
       +------+------+     +-------+-------+    +------+------+
       | Poincare    |     | Hyperbolic    |    | Hyperbolic  |
       | Embeddings  |     | Attention     |    | GNN         |
       +-------------+     +---------------+    +-------------+
                                   |
                           +-------+-------+
                           | HNSW Index    |
                           | (Hyperbolic)  |
                           +---------------+

Hyperbolic Geometry Primer

Euclidean Space               Hyperbolic Space (Poincare Ball)

    *--*--*--*                         * (root at center)
    |  |  |  |                        /|\
    *--*--*--*           vs          * * * (children near edge)
    |  |  |  |                      /|\ |\
    *--*--*--*                     ****  **

Uniform density              Exponential capacity toward boundary

Key Properties

Property	Euclidean	Hyperbolic
Tree capacity	O(n^d)	O(exp(d))
Hierarchy distortion	High	Low
Dimensions needed	Many	Few
Parent-child	No natural	Radial distance
Sibling	No natural	Angular distance

Performance Targets

Metric	Target	Baseline (Euclidean)	Improvement
Embedding (10K nodes)	<10s	~30s (high-dim Euclidean)	3x
Hyperbolic search	<5ms for 1M embeddings	~50ms (Euclidean HNSW)	10x
Taxonomic query	<10ms per inference	~100ms (graph traversal)	10x
Hierarchy comparison	<1s for 10K nodes	~30s (tree edit distance)	30x
Entailment graph build	<30s for 100K concepts	~10min (pairwise comparison)	20x
Dimension efficiency	32-dim hyperbolic	512-dim Euclidean equivalent	16x memory

Security Considerations

Input Validation (CRITICAL)

All MCP tool inputs MUST be validated using Zod schemas:

// hyperbolic/embed-hierarchy input validation
const EmbedHierarchySchema = z.object({
  hierarchy: z.object({
    nodes: z.array(z.object({
      id: z.string().max(200),
      parent: z.string().max(200).nullable(),
      features: z.record(z.string(), z.unknown()).optional()
    })).min(1).max(1_000_000), // Max 1M nodes
    edges: z.array(z.object({
      source: z.string().max(200),
      target: z.string().max(200),
      weight: z.number().finite().optional()
    })).max(10_000_000).optional()
  }),
  model: z.enum(['poincare_ball', 'lorentz', 'klein', 'half_plane']).default('poincare_ball'),
  parameters: z.object({
    dimensions: z.number().int().min(2).max(512).default(32),
    curvature: z.number().min(-10).max(-0.01).default(-1.0),
    learnCurvature: z.boolean().default(true)
  }).optional()
});

// hyperbolic/taxonomic-reason input validation
const TaxonomicReasonSchema = z.object({
  query: z.object({
    type: z.enum(['is_a', 'subsumption', 'lowest_common_ancestor', 'path', 'similarity']),
    subject: z.string().max(500),
    object: z.string().max(500).optional()
  }),
  taxonomy: z.string().max(200),
  inference: z.object({
    transitive: z.boolean().default(true),
    fuzzy: z.boolean().default(false),
    confidence: z.number().min(0).max(1).default(0.8)
  }).optional()
});

// hyperbolic/semantic-search input validation
const SemanticSearchSchema = z.object({
  query: z.string().max(5000),
  index: z.string().max(200),
  searchMode: z.enum(['nearest', 'subtree', 'ancestors', 'siblings', 'cone']).default('nearest'),
  constraints: z.object({
    maxDepth: z.number().int().min(0).max(100).optional(),
    minDepth: z.number().int().min(0).max(100).optional(),
    subtreeRoot: z.string().max(200).optional()
  }).optional(),
  topK: z.number().int().min(1).max(10000).default(10)
});

// hyperbolic/entailment-graph input validation
const EntailmentGraphSchema = z.object({
  action: z.enum(['build', 'query', 'expand', 'prune']),
  concepts: z.array(z.object({
    id: z.string().max(200),
    text: z.string().max(5000),
    type: z.string().max(100).optional()
  })).max(100000).optional(),
  entailmentThreshold: z.number().min(0).max(1).default(0.7),
  transitiveClosure: z.boolean().default(true),
  pruneStrategy: z.enum(['none', 'transitive_reduction', 'confidence_threshold']).optional()
});

WASM Security Constraints

Constraint	Value	Rationale
Memory Limit	2GB max	Handle large hierarchies
CPU Time Limit	300 seconds for embedding	Large graph embedding takes time
Node Limit	1M nodes max	Bound computational complexity
Edge Limit	10M edges max	Prevent memory exhaustion
Dimension Limit	512 max	Reasonable embedding size

Numerical Security (CRITICAL for Hyperbolic)

// Hyperbolic operations have numerical instabilities near boundary
// MUST implement defensive numerical handling

const POINCARE_BALL_EPS = 1e-10;  // Minimum distance from boundary
const MAX_NORM = 1 - POINCARE_BALL_EPS;  // Maximum vector norm

// Clip vectors to stay within Poincare ball
function clipToBall(vector: number[], curvature: number): number[] {
  const norm = Math.sqrt(vector.reduce((sum, v) => sum + v * v, 0));
  const maxNorm = MAX_NORM / Math.sqrt(-curvature);

  if (norm > maxNorm) {
    const scale = maxNorm / norm;
    return vector.map(v => v * scale);
  }

  return vector;
}

// Safe Mobius addition (handles edge cases)
function safeMobiusAdd(x: number[], y: number[], c: number): number[] {
  // Check inputs are finite
  if (!x.every(Number.isFinite) || !y.every(Number.isFinite)) {
    throw new NumericalError('Non-finite input to Mobius addition');
  }

  // Clip to ball before operation
  x = clipToBall(x, c);
  y = clipToBall(y, c);

  // Perform operation with numerical guards
  // ... implementation with overflow checking
}

Output Validation

// Validate all hyperbolic outputs for numerical sanity
function validateHyperbolicOutput(result: HyperbolicResult): ValidationResult {
  // Check embeddings are within Poincare ball
  for (const embedding of result.embeddings) {
    const norm = Math.sqrt(embedding.reduce((sum, v) => sum + v * v, 0));
    if (norm >= 1.0) {
      return { valid: false, error: 'Embedding outside Poincare ball' };
    }
    if (!embedding.every(Number.isFinite)) {
      return { valid: false, error: 'Non-finite embedding values' };
    }
  }

  // Check distances are non-negative
  for (const distance of result.distances || []) {
    if (distance < 0 || !Number.isFinite(distance)) {
      return { valid: false, error: 'Invalid distance value' };
    }
  }

  return { valid: true };
}

Identified Security Risks

Risk ID	Severity	Description	Mitigation
HYPER-SEC-001	HIGH	DoS via deeply nested hierarchies	Depth limits, progressive processing
HYPER-SEC-002	HIGH	Numerical overflow near boundary	Boundary clipping, epsilon guards
HYPER-SEC-003	MEDIUM	NaN/Infinity propagation	Input/output validation
HYPER-SEC-004	MEDIUM	Memory exhaustion via large graphs	Node/edge limits
HYPER-SEC-005	LOW	Curvature manipulation attacks	Curvature bounds (-10 to -0.01)

Denial of Service Prevention

// Hierarchies can be crafted to cause exponential blowup
function validateHierarchy(hierarchy: Hierarchy): ValidationResult {
  // Check for cycles (would cause infinite traversal)
  if (hasCycle(hierarchy)) {
    return { valid: false, error: 'Hierarchy contains cycles' };
  }

  // Check depth (very deep hierarchies cause numerical issues)
  const maxDepth = computeMaxDepth(hierarchy);
  if (maxDepth > 100) {
    return { valid: false, error: `Hierarchy too deep: ${maxDepth} > 100` };
  }

  // Check branching factor (wide trees cause memory issues)
  const maxBranching = computeMaxBranching(hierarchy);
  if (maxBranching > 10000) {
    return { valid: false, error: `Branching factor too high: ${maxBranching}` };
  }

  return { valid: true };
}

Rate Limiting

const HyperbolicRateLimits = {
  'hyperbolic/embed-hierarchy': { requestsPerMinute: 5, maxConcurrent: 1 },
  'hyperbolic/taxonomic-reason': { requestsPerMinute: 60, maxConcurrent: 5 },
  'hyperbolic/semantic-search': { requestsPerMinute: 60, maxConcurrent: 5 },
  'hyperbolic/hierarchy-compare': { requestsPerMinute: 10, maxConcurrent: 2 },
  'hyperbolic/entailment-graph': { requestsPerMinute: 10, maxConcurrent: 2 }
};

Risk Assessment

Risk	Likelihood	Impact	Mitigation
Numerical instability	Medium	Medium	Boundary clipping, numerical stabilization
Non-hierarchical data	Medium	Low	Automatic fallback to Euclidean
Curvature tuning	Medium	Low	Adaptive curvature via SONA
Training complexity	High	Medium	Pretrained embeddings, transfer learning

Mobius Operations

The plugin implements core Mobius operations for the Poincare ball:

// Mobius addition: x + y in hyperbolic space
mobius_add(x: Vector, y: Vector, c: number): Vector

// Mobius scalar multiplication
mobius_scalar(r: number, x: Vector, c: number): Vector

// Exponential map: tangent space -> hyperbolic
exp_map(v: Vector, c: number): Vector

// Logarithmic map: hyperbolic -> tangent space
log_map(x: Vector, c: number): Vector

// Hyperbolic distance
hyperbolic_distance(x: Vector, y: Vector, c: number): number

Implementation Notes

Phase 1: Core Embeddings

• Poincare ball model implementation
• Hyperbolic distance metrics
• Basic HNSW adaptation

Phase 2: Neural Operations

• Hyperbolic attention mechanisms
• Hyperbolic GNN layers
• Adaptive curvature learning

Phase 3: Applications

• Taxonomic reasoning engine
• Hierarchy comparison tools
• Entailment graph construction

Dependencies

{
  "dependencies": {
    "ruvector-hyperbolic-hnsw-wasm": "^0.1.0",
    "ruvector-attention-wasm": "^0.1.0",
    "ruvector-gnn-wasm": "^0.1.0",
    "micro-hnsw-wasm": "^0.2.0",
    "sona": "^0.1.0"
  }
}

Curvature Learning

The plugin supports learning optimal curvature per hierarchy:

Flat hierarchy (organization chart)  --> Low curvature (-0.1)
Deep hierarchy (taxonomy)            --> High curvature (-2.0)
Mixed hierarchy                      --> Adaptive curvature via SONA

Consequences

Positive

• Orders of magnitude better hierarchy representation
• Natural modeling of taxonomic relationships
• Significant dimension reduction for tree-like data

Negative

• Numerical instability near boundary (mitigated by clipping)
• Not all data is hierarchical
• Learning requires specialized optimization

Neutral

• Can fallback to Euclidean for non-hierarchical data

Related ADRs

ADR	Relationship
ADR-004: Plugin Architecture	Foundation - Defines plugin structure
ADR-017: RuVector Integration	Dependency - Provides WASM packages
ADR-023: ONNX Hyperbolic Embeddings	Dependency - Embedding initialization
ADR-032: Healthcare Clinical	Related - Medical ontology hierarchies
ADR-034: Legal Contracts	Related - Legal taxonomy navigation
ADR-035: Code Intelligence	Related - Code hierarchy analysis

References

• Poincare Embeddings: https://arxiv.org/abs/1705.08039
• Hyperbolic Neural Networks: https://arxiv.org/abs/1805.09112
• Hyperbolic Attention: https://arxiv.org/abs/1905.09786
• ADR-017: RuVector Integration
• ADR-023: ONNX Hyperbolic Embeddings Init
• ADR-004: Plugin Architecture

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Last Updated: 2026-01-24