ADR-028: Neural Attention Mechanisms for Claude-Flow V3

Status: Proposed Date: 2026-01-16 Author: System Architecture Designer Version: 1.0.0

Context

Claude-Flow v3 requires advanced attention mechanisms for several critical operations:

Agent Memory Retrieval: Current memory lookups use basic vector similarity (cosine/dot product). More sophisticated attention mechanisms can improve retrieval quality by weighing relevance, recency, and contextual importance.
Context Window Optimization: With growing context sizes, efficient attention computation becomes critical. Sparse and linear attention variants can reduce O(n^2) complexity to O(n) or O(n log n).
Pattern Matching in Code: Code analysis benefits from structural attention that respects AST hierarchies, function boundaries, and import relationships.
Swarm Coordination Signals: Multi-agent coordination requires cross-attention mechanisms to share relevant context between agents while filtering noise.

The RuVector intelligence system provides 39 attention mechanism implementations optimized for AI agent workloads. Integrating these into Claude-Flow v3 will significantly improve memory retrieval, context management, and agent coordination.

Decision

Integrate RuVector's 39 attention mechanism types into Claude-Flow v3 via a unified AttentionService that provides:

Pluggable attention backends - Select mechanisms based on use case
Automatic fallback - Graceful degradation when GPU unavailable
Integration with existing services - Memory, SONA, and plugin hooks
Performance optimization - Quantization, caching, and batching

Attention Mechanism Types (39 Total)

Category 1: Multi-Head Attention (7 types)

Type	Description	Use Case	Complexity
`standard-mha`	Standard multi-head attention (Vaswani et al.)	General purpose	O(n^2)
`rotary-mha`	Multi-head with rotary position embeddings (RoPE)	Long sequences	O(n^2)
`alibi-mha`	Multi-head with ALiBi position bias	Extrapolation	O(n^2)
`grouped-query-attention`	GQA (fewer KV heads)	Memory efficient	O(n^2)
`multi-query-attention`	Single KV head shared	Fast inference	O(n^2)
`differential-attention`	Attention with derivative signals	Change detection	O(n^2)
`mixture-attention`	Multiple attention patterns combined	Hybrid tasks	O(n^2)

typescript

// Standard multi-head attention configuration
interface MultiHeadAttentionConfig {
  numHeads: number;           // Number of attention heads (default: 8)
  headDim: number;            // Dimension per head (default: 64)
  dropout: number;            // Dropout rate (default: 0.1)
  qkvBias: boolean;           // Include bias in QKV projections
  outputBias: boolean;        // Include bias in output projection
  scaleFactor?: number;       // Custom scaling (default: 1/sqrt(headDim))
}

Category 2: Self-Attention Variants (6 types)

Type	Description	Use Case	Complexity
`causal-self-attention`	Masked for autoregressive generation	Code completion	O(n^2)
`bidirectional-self-attention`	Full context both directions	Understanding	O(n^2)
`relative-position-attention`	Relative position encodings	Sequence modeling	O(n^2)
`disentangled-attention`	Separate content/position (DeBERTa)	NLU tasks	O(n^2)
`talking-heads-attention`	Inter-head communication	Complex reasoning	O(n^2)
`synthesizer-attention`	Learned attention patterns	Fast inference	O(n)

typescript

// Causal self-attention for autoregressive tasks
interface CausalSelfAttentionConfig {
  blockSize: number;          // Maximum sequence length
  numHeads: number;           // Attention heads
  embedDim: number;           // Embedding dimension
  maskFuture: boolean;        // Mask future tokens (default: true)
  useSlidingWindow?: number;  // Optional sliding window size
}

Type	Description	Use Case	Complexity
`cross-attention`	Attend to different sequence	Agent coordination	O(n*m)
`perceiver-attention`	Latent bottleneck	Large inputs	O(n*l)
`gated-cross-attention`	Gated fusion	Multi-modal	O(n*m)
`memory-attention`	Attend to memory bank	RAG retrieval	O(n*k)
`hierarchical-cross-attention`	Multi-level attention	Document analysis	O(n*m)

typescript

// Cross-attention for agent coordination
interface CrossAttentionConfig {
  queryDim: number;           // Query dimension (agent A)
  keyValueDim: number;        // Key/Value dimension (agent B)
  numHeads: number;           // Attention heads
  outputDim: number;          // Output dimension
  useGating?: boolean;        // Gated fusion (default: false)
  maxMemorySlots?: number;    // Memory bank size (for memory-attention)
}

Category 4: Sparse Attention Patterns (8 types)

Type	Description	Use Case	Complexity
`bigbird-attention`	Block sparse + global + random	Long documents	O(n)
`longformer-attention`	Sliding window + global	Long sequences	O(n)
`local-attention`	Fixed window only	Local context	O(n*w)
`strided-attention`	Strided patterns	Structured data	O(n*s)
`sparse-transformer-attention`	Factorized patterns	Images/sequences	O(n*sqrt(n))
`star-attention`	Hub-and-spoke topology	Swarm coordination	O(n*h)
`blockwise-attention`	Chunked computation	Memory efficient	O(n^2) chunked
`random-attention`	Random sparse sampling	Approximation	O(n*k)

typescript

// Longformer-style attention for long contexts
interface LongformerAttentionConfig {
  windowSize: number;         // Local attention window (default: 512)
  globalTokens: number[];     // Token indices with global attention
  dilationRates?: number[];   // Dilation for each layer
  numHeads: number;
  embedDim: number;
}

// BigBird attention combining multiple sparse patterns
interface BigBirdAttentionConfig {
  blockSize: number;          // Block size for sparse attention
  numGlobalTokens: number;    // Number of global attention tokens
  numRandomBlocks: number;    // Random blocks per row
  windowSize: number;         // Sliding window size
  numHeads: number;
  embedDim: number;
}

Category 5: Linear Attention Approximations (6 types)

Type	Description	Use Case	Complexity
`linear-attention`	Kernel feature maps	Real-time inference	O(n)
`performer-attention`	FAVOR+ random features	Large scale	O(n)
`cosformer-attention`	Cos-based re-weighting	Efficient softmax	O(n)
`rfa-attention`	Random feature attention	Memory efficient	O(n)
`nystromer-attention`	Nystrom approximation	Matrix approximation	O(n)
`linformer-attention`	Low-rank projection	Compression	O(n)

typescript

// Linear attention with kernel approximation
interface LinearAttentionConfig {
  featureDim: number;         // Random feature dimension
  kernelType: 'elu' | 'relu' | 'softmax' | 'exp';
  numHeads: number;
  embedDim: number;
  useNormalization?: boolean; // Normalize outputs
  epsNumericalStability?: number;
}

// Performer FAVOR+ attention
interface PerformerAttentionConfig {
  numRandomFeatures: number;  // Number of random features (default: 256)
  orthogonalFeatures: boolean; // Use orthogonal random features
  redraw: boolean;            // Redraw features each forward pass
  numHeads: number;
  embedDim: number;
}

Category 6: Flash Attention Optimization (3 types)

Type	Description	Use Case	Complexity
`flash-attention-v2`	IO-aware exact attention	GPU optimization	O(n^2) IO-optimized
`flash-attention-v3`	Hopper architecture optimized	H100/H200	O(n^2) IO-optimized
`flash-decoding`	Optimized for inference	Fast generation	O(n^2) cached

typescript

// Flash Attention v2 configuration
interface FlashAttentionConfig {
  causal: boolean;            // Causal masking
  softmaxScale?: number;      // Custom softmax scaling
  windowSize?: [number, number]; // Sliding window (left, right)
  returnSoftmax: boolean;     // Return attention weights

  // Hardware optimization
  blockSizeQ: number;         // Block size for queries (default: 128)
  blockSizeKV: number;        // Block size for keys/values (default: 64)
  numWarps: number;           // CUDA warps per block
  numStages: number;          // Pipeline stages
}

Category 7: Mixture-of-Experts Attention (4 types)

Type	Description	Use Case	Complexity
`moe-attention`	Expert-routed attention	Task specialization	O(n^2/E)
`soft-moe-attention`	Soft expert mixing	Smooth routing	O(n^2)
`switch-attention`	Single expert selection	Sparse activation	O(n^2/E)
`expert-choice-attention`	Expert selects tokens	Load balanced	O(n^2/E)

typescript

// Mixture-of-Experts attention configuration
interface MoEAttentionConfig {
  numExperts: number;         // Number of expert attention modules
  topK: number;               // Experts selected per token (default: 2)
  capacityFactor: number;     // Expert capacity (default: 1.25)
  routerType: 'softmax' | 'sigmoid' | 'top-k';
  loadBalancingLoss: boolean; // Add load balancing auxiliary loss
  expertDim: number;          // Dimension per expert
  numHeads: number;
  embedDim: number;
}

Use Cases in Claude-Flow V3

1. Agent Memory Retrieval

Problem: Current memory retrieval uses simple cosine similarity which misses contextual relevance.

Solution: Use memory-attention with learned relevance weighting.

typescript

// Memory retrieval with attention
class AttentionEnhancedMemory {
  private memoryAttention: MemoryAttention;
  private memoryBank: Float32Array[];

  async retrieve(query: Float32Array, k: number): Promise<MemoryEntry[]> {
    // Step 1: Get top-k candidates via HNSW (fast)
    const candidates = await this.hnsw.search(query, k * 3);

    // Step 2: Rerank with attention (accurate)
    const attentionScores = await this.memoryAttention.attend(
      query,                    // Query vector
      candidates.map(c => c.embedding), // Key vectors
      candidates.map(c => c.content)    // Value vectors
    );

    // Step 3: Return top-k by attention score
    return attentionScores
      .sort((a, b) => b.score - a.score)
      .slice(0, k);
  }
}

Configuration:

typescript

{
  attention: {
    memoryRetrieval: {
      type: 'memory-attention',
      numHeads: 4,
      useGating: true,
      maxMemorySlots: 1000
    }
  }
}

2. Context Window Optimization

Problem: Large context windows cause quadratic memory/compute growth.

Solution: Use longformer-attention or bigbird-attention for O(n) scaling.

typescript

// Context window manager with sparse attention
class OptimizedContextManager {
  private attention: LongformerAttention;

  constructor(config: LongformerAttentionConfig) {
    this.attention = new LongformerAttention({
      windowSize: 512,          // Local window
      globalTokens: [0],        // CLS token global
      numHeads: 8,
      embedDim: 768
    });
  }

  async processLongContext(
    tokens: Float32Array,
    maxLength: number = 32768
  ): Promise<Float32Array> {
    // O(n) attention instead of O(n^2)
    return this.attention.forward(tokens, {
      slidingWindowMask: true,
      globalMask: this.buildGlobalMask(tokens)
    });
  }
}

Performance Comparison:

Context Length	Standard MHA	Longformer	Speedup
4K tokens	16ms	4ms	4x
16K tokens	256ms	16ms	16x
64K tokens	4096ms	64ms	64x
128K tokens	OOM	128ms	-

3. Pattern Matching in Code

Problem: Code patterns have hierarchical structure (files > classes > functions > statements).

Solution: Use hierarchical-cross-attention with AST-aware masking.

typescript

// AST-aware code attention
class CodePatternAttention {
  private hierarchicalAttention: HierarchicalCrossAttention;

  async matchPatterns(
    codeEmbedding: Float32Array,
    patternBank: CodePattern[]
  ): Promise<PatternMatch[]> {
    // Build hierarchical representation
    const levels = [
      this.getFileLevel(codeEmbedding),
      this.getClassLevel(codeEmbedding),
      this.getFunctionLevel(codeEmbedding),
      this.getStatementLevel(codeEmbedding)
    ];

    // Hierarchical attention across all patterns
    const matches = await this.hierarchicalAttention.forward({
      queries: levels,
      keys: patternBank.map(p => p.embedding),
      values: patternBank.map(p => p.metadata),
      levelMasks: this.buildASTMasks(codeEmbedding)
    });

    return matches.filter(m => m.confidence > 0.7);
  }
}

4. Swarm Coordination Signals

Problem: Multi-agent communication generates noise; agents need selective attention.

Solution: Use star-attention for hub-and-spoke coordination or cross-attention for peer-to-peer.

typescript

// Swarm coordinator with star attention topology
class SwarmAttentionCoordinator {
  private starAttention: StarAttention;

  constructor() {
    this.starAttention = new StarAttention({
      numSpokes: 8,           // Max worker agents
      hubDim: 768,            // Coordinator embedding dim
      spokeDim: 512,          // Worker embedding dim
      numHeads: 4
    });
  }

  async coordinateAgents(
    coordinatorState: Float32Array,
    agentStates: Map<string, Float32Array>
  ): Promise<CoordinationSignals> {
    // Hub attends to all spokes
    const signals = await this.starAttention.forward({
      hub: coordinatorState,
      spokes: Array.from(agentStates.values())
    });

    // Return selective signals per agent
    return {
      broadcast: signals.hubOutput,
      targeted: signals.spokeOutputs.map((s, i) => ({
        agentId: Array.from(agentStates.keys())[i],
        signal: s
      }))
    };
  }
}

Topology Options:

Topology	Attention Type	Best For
Hierarchical	`star-attention`	Coordinator + workers
Mesh	`cross-attention`	Peer-to-peer
Hierarchical-Mesh	`moe-attention`	Mixed coordination
Adaptive	`switch-attention`	Dynamic topology

Integration Points

1. Memory Service (ADR-006)

typescript

// v3/@claude-flow/memory/src/attention-enhanced-memory.ts

import { AttentionService } from '@claude-flow/attention';
import { UnifiedMemoryService } from './unified-memory-service.js';

export class AttentionEnhancedMemoryService extends UnifiedMemoryService {
  private attentionService: AttentionService;

  constructor(config: MemoryConfig & AttentionConfig) {
    super(config);
    this.attentionService = new AttentionService({
      defaultMechanism: config.attention?.type || 'memory-attention',
      fallbackMechanism: 'linear-attention',
      gpuAcceleration: config.attention?.useGPU ?? true
    });
  }

  async searchSemantic(
    query: string,
    k: number,
    options?: { useAttention?: boolean }
  ): Promise<MemoryEntry[]> {
    // Standard HNSW retrieval
    const candidates = await super.searchSemantic(query, k * 3);

    if (!options?.useAttention) {
      return candidates.slice(0, k);
    }

    // Rerank with attention
    const queryEmbedding = await this.embed(query);
    const reranked = await this.attentionService.rerank(
      queryEmbedding,
      candidates.map(c => c.embedding!),
      candidates
    );

    return reranked.slice(0, k);
  }
}

2. SONA Neural Architecture

typescript

// v3/@claude-flow/intelligence/src/sona-attention.ts

import { AttentionService, FlashAttentionConfig } from '@claude-flow/attention';

export class SONAWithAttention {
  private flashAttention: AttentionService;
  private moeAttention: AttentionService;

  constructor() {
    // Use Flash Attention for SONA's self-attention layers
    this.flashAttention = new AttentionService({
      mechanism: 'flash-attention-v2',
      config: {
        causal: false,
        blockSizeQ: 128,
        blockSizeKV: 64
      } as FlashAttentionConfig
    });

    // Use MoE attention for expert routing
    this.moeAttention = new AttentionService({
      mechanism: 'moe-attention',
      config: {
        numExperts: 8,
        topK: 2,
        loadBalancingLoss: true
      }
    });
  }

  async adapt(pattern: Pattern): Promise<AdaptationResult> {
    // Fast adaptation with Flash Attention (2.49x-7.47x speedup)
    const attended = await this.flashAttention.forward(
      pattern.embedding,
      this.expertWeights
    );

    // Route to specialized expert
    const expertOutput = await this.moeAttention.forward(attended);

    return {
      adaptedPattern: expertOutput,
      expertUsage: this.moeAttention.getExpertUsage()
    };
  }
}

3. Plugin Hook System

typescript

// v3/@claude-flow/cli/src/hooks/attention-hooks.ts

import { AttentionService } from '@claude-flow/attention';

export const attentionHooks = {
  /**
   * Pre-retrieval hook: configure attention for query
   */
  'pre-retrieval': async (ctx: HookContext) => {
    const queryComplexity = await analyzeQueryComplexity(ctx.query);

    // Select attention mechanism based on complexity
    ctx.attentionConfig = {
      mechanism: queryComplexity > 0.7
        ? 'memory-attention'      // Complex queries need full attention
        : 'linear-attention',     // Simple queries use O(n) attention
      numHeads: Math.ceil(queryComplexity * 8),
      useGating: queryComplexity > 0.5
    };

    return ctx;
  },

  /**
   * Post-retrieval hook: log attention patterns
   */
  'post-retrieval': async (ctx: HookContext) => {
    if (ctx.attentionWeights) {
      await storeAttentionPattern({
        query: ctx.query,
        weights: ctx.attentionWeights,
        mechanism: ctx.attentionConfig.mechanism,
        timestamp: Date.now()
      });
    }
    return ctx;
  },

  /**
   * Intelligence hook: route to attention mechanism
   */
  'intelligence': async (ctx: HookContext) => {
    const taskType = detectTaskType(ctx.task);

    const mechanisms: Record<string, string> = {
      'code-analysis': 'hierarchical-cross-attention',
      'memory-retrieval': 'memory-attention',
      'long-context': 'longformer-attention',
      'swarm-coordination': 'star-attention',
      'default': 'standard-mha'
    };

    ctx.recommendedAttention = mechanisms[taskType] || mechanisms.default;
    return ctx;
  }
};

Performance Considerations

1. GPU Acceleration

typescript

// Automatic GPU detection and fallback
interface AttentionAccelerationConfig {
  // GPU settings
  preferGPU: boolean;           // Prefer GPU when available
  gpuMemoryLimit?: number;      // Max GPU memory (MB)
  gpuDeviceId?: number;         // Specific GPU device

  // Fallback settings
  fallbackToLinear: boolean;    // Use linear attention on CPU
  fallbackThreshold: number;    // Sequence length to trigger fallback

  // Mixed precision
  useFP16: boolean;             // Use half precision
  useBF16: boolean;             // Use bfloat16 (Ampere+)
}

// Runtime acceleration selection
class AttentionAccelerator {
  async selectBackend(
    mechanism: string,
    sequenceLength: number,
    config: AttentionAccelerationConfig
  ): Promise<AttentionBackend> {
    // Check GPU availability
    const gpuAvailable = await this.checkGPU();

    if (gpuAvailable && config.preferGPU) {
      // Use Flash Attention on GPU
      if (mechanism.includes('flash') || sequenceLength > 1024) {
        return new FlashAttentionGPU(config);
      }
      return new CUDAAttention(mechanism, config);
    }

    // CPU fallback
    if (sequenceLength > config.fallbackThreshold) {
      // Switch to linear attention for long sequences on CPU
      return new LinearAttentionCPU(config);
    }

    return new StandardAttentionCPU(mechanism, config);
  }
}

2. Quantization Support

typescript

// Attention weight quantization for memory efficiency
interface QuantizationConfig {
  weightsQuantization: 'none' | 'int8' | 'int4' | 'fp16';
  activationsQuantization: 'none' | 'int8' | 'fp16';
  dynamicQuantization: boolean;  // Quantize per-batch
  calibrationSamples?: number;   // Samples for static quantization
}

// Memory savings with quantization
// | Precision | Memory | Speedup | Accuracy Loss |
// |-----------|--------|---------|---------------|
// | FP32      | 100%   | 1x      | 0%            |
// | FP16      | 50%    | 1.5x    | <0.1%         |
// | INT8      | 25%    | 2x      | <0.5%         |
// | INT4      | 12.5%  | 3x      | <2%           |

3. Caching Strategies

typescript

// KV-cache for autoregressive generation
interface KVCacheConfig {
  maxCacheSize: number;         // Maximum cached tokens
  slidingWindow?: number;       // Sliding window for bounded cache
  compressionRatio?: number;    // Compress old cache entries
  evictionPolicy: 'lru' | 'fifo' | 'attention-score';
}

class AttentionKVCache {
  private keyCache: Map<number, Float32Array>;
  private valueCache: Map<number, Float32Array>;

  async getCachedAttention(
    query: Float32Array,
    newKeys: Float32Array,
    newValues: Float32Array,
    position: number
  ): Promise<Float32Array> {
    // Append new KV to cache
    this.keyCache.set(position, newKeys);
    this.valueCache.set(position, newValues);

    // Compute attention with full cached history
    const allKeys = this.getAllKeys();
    const allValues = this.getAllValues();

    return this.attention.forward(query, allKeys, allValues);
  }

  async evict(): Promise<void> {
    if (this.keyCache.size <= this.config.maxCacheSize) return;

    switch (this.config.evictionPolicy) {
      case 'lru':
        this.evictLRU();
        break;
      case 'attention-score':
        await this.evictByAttentionScore();
        break;
      default:
        this.evictFIFO();
    }
  }
}

Implementation Plan

Phase 1: Core Attention Service (Week 1-2)

Create @claude-flow/attention package
Implement base AttentionService interface
Add 7 multi-head attention types
Add 6 self-attention variants
Unit tests with 90%+ coverage

Phase 2: Sparse & Linear Attention (Week 2-3)

Implement 8 sparse attention patterns
Implement 6 linear attention approximations
Add GPU acceleration with Flash Attention
Benchmark against standard attention

Phase 3: Integration (Week 3-4)

Integrate with Memory Service (ADR-006)
Integrate with SONA neural architecture
Add plugin hooks for attention selection
Create CLI commands for attention management

Phase 4: MoE & Advanced (Week 4-5)

Implement 4 MoE attention types
Add cross-attention for multi-modal
Create swarm coordination attention
Performance optimization and profiling

File Structure

v3/@claude-flow/attention/
├── package.json
├── src/
│   ├── index.ts                    # Public API exports
│   ├── attention-service.ts        # Main service class
│   ├── types.ts                    # TypeScript interfaces
│   │
│   ├── mechanisms/
│   │   ├── multi-head/
│   │   │   ├── standard-mha.ts
│   │   │   ├── rotary-mha.ts
│   │   │   ├── alibi-mha.ts
│   │   │   ├── grouped-query.ts
│   │   │   ├── multi-query.ts
│   │   │   ├── differential.ts
│   │   │   └── mixture.ts
│   │   │
│   │   ├── self-attention/
│   │   │   ├── causal.ts
│   │   │   ├── bidirectional.ts
│   │   │   ├── relative-position.ts
│   │   │   ├── disentangled.ts
│   │   │   ├── talking-heads.ts
│   │   │   └── synthesizer.ts
│   │   │
│   │   ├── cross-attention/
│   │   │   ├── standard-cross.ts
│   │   │   ├── perceiver.ts
│   │   │   ├── gated-cross.ts
│   │   │   ├── memory.ts
│   │   │   └── hierarchical.ts
│   │   │
│   │   ├── sparse/
│   │   │   ├── bigbird.ts
│   │   │   ├── longformer.ts
│   │   │   ├── local.ts
│   │   │   ├── strided.ts
│   │   │   ├── sparse-transformer.ts
│   │   │   ├── star.ts
│   │   │   ├── blockwise.ts
│   │   │   └── random.ts
│   │   │
│   │   ├── linear/
│   │   │   ├── linear.ts
│   │   │   ├── performer.ts
│   │   │   ├── cosformer.ts
│   │   │   ├── rfa.ts
│   │   │   ├── nystrom.ts
│   │   │   └── linformer.ts
│   │   │
│   │   ├── flash/
│   │   │   ├── flash-v2.ts
│   │   │   ├── flash-v3.ts
│   │   │   └── flash-decoding.ts
│   │   │
│   │   └── moe/
│   │       ├── moe.ts
│   │       ├── soft-moe.ts
│   │       ├── switch.ts
│   │       └── expert-choice.ts
│   │
│   ├── acceleration/
│   │   ├── gpu-backend.ts
│   │   ├── cpu-backend.ts
│   │   ├── quantization.ts
│   │   └── kv-cache.ts
│   │
│   └── utils/
│       ├── masking.ts
│       ├── position-encoding.ts
│       └── scaling.ts
│
└── tests/
    ├── multi-head.test.ts
    ├── sparse.test.ts
    ├── linear.test.ts
    ├── flash.test.ts
    ├── moe.test.ts
    └── integration.test.ts

Configuration Schema

typescript

// claude-flow.config.json
{
  "attention": {
    // Default mechanism for general use
    "defaultMechanism": "standard-mha",

    // Task-specific overrides
    "taskOverrides": {
      "memoryRetrieval": "memory-attention",
      "longContext": "longformer-attention",
      "codeAnalysis": "hierarchical-cross-attention",
      "swarmCoordination": "star-attention"
    },

    // Performance settings
    "acceleration": {
      "preferGPU": true,
      "fallbackToLinear": true,
      "fallbackThreshold": 4096
    },

    // Quantization
    "quantization": {
      "weights": "fp16",
      "activations": "fp16",
      "dynamicQuantization": false
    },

    // Caching
    "cache": {
      "enabled": true,
      "maxSize": 8192,
      "evictionPolicy": "attention-score"
    }
  }
}

CLI Commands

bash

# List available attention mechanisms
npx @claude-flow/cli@latest attention list

# Benchmark attention mechanism
npx @claude-flow/cli@latest attention benchmark --mechanism longformer --sequence-length 16384

# Set default attention mechanism
npx @claude-flow/cli@latest attention set-default --mechanism flash-attention-v2

# Show attention statistics
npx @claude-flow/cli@latest attention stats

# Clear attention cache
npx @claude-flow/cli@latest attention cache clear

Consequences

Positive

2.49x-7.47x speedup with Flash Attention on GPU
O(n) complexity for long sequences with sparse/linear attention
Improved retrieval quality with attention-based reranking
Better swarm coordination with topology-aware attention
Flexible architecture with 39 mechanism options

Negative

Increased complexity - more code to maintain
GPU dependency for optimal performance (Flash Attention)
Memory overhead for KV caching
Learning curve for mechanism selection

Neutral

Optional feature - fallback to standard cosine similarity
Configurable - can disable for resource-constrained environments

Performance Targets

Metric	Target	Mechanism
Memory retrieval latency	<10ms	memory-attention
Long context (32K tokens)	<100ms	longformer-attention
Swarm coordination	<5ms	star-attention
Flash Attention speedup	2.49x-7.47x	flash-attention-v2/v3
Linear attention throughput	10x standard	performer-attention
Memory overhead (KV cache)	<500MB	configurable

Success Metrics

All 39 attention mechanisms implemented
90%+ test coverage
Flash Attention GPU acceleration working
Memory service integration complete
SONA integration complete
CLI commands functional
Performance targets achieved
Documentation complete

References

ADR-006: Unified Memory Service
ADR-017: RuVector Integration Architecture
ADR-026: Agent Booster Model Routing
Flash Attention Paper: https://arxiv.org/abs/2205.14135
Longformer Paper: https://arxiv.org/abs/2004.05150
BigBird Paper: https://arxiv.org/abs/2007.14062
Performer Paper: https://arxiv.org/abs/2009.14794
RuVector Documentation: https://github.com/ruvnet/ruvector

WASM-Native Implementations (2026-03-17 Update)

The @ruvector/[email protected] package now provides native WASM implementations of several attention and intelligence components described in this ADR. These run at near-native speed without GPU requirements.

Available WASM Components

This ADR Concept	ruvllm-wasm Class	Status
HNSW Search (150x-12,500x)	`HnswRouterWasm`	Published, working (v2.0.1)
SONA Adaptation (<0.05ms)	`SonaInstantWasm`	Published, working
KV Cache Management	`KvCacheWasm`	Published, working
LoRA Adaptation	`MicroLoraWasm`	Published, working (ranks 1-4, <10KB)
Memory Pool / Buffer	`BufferPoolWasm`	Published, working
Inference Arena	`InferenceArenaWasm`	Published, working

WASM vs JavaScript Performance

Component	JS Implementation	WASM Implementation	Expected Speedup
HNSW Search	`FlashAttention` (JS)	`HnswRouterWasm`	5-50x
SONA Adapt	`SemanticRouter` (JS)	`SonaInstantWasm`	10-100x
LoRA Train	`LoRAAdapter` (JS)	`MicroLoraWasm`	3-10x
KV Cache	Manual `Map<>`	`KvCacheWasm`	2-5x

Integration Pattern

typescript

// Detection and fallback
import { isRuvllmWasmAvailable, initRuvllmWasm } from '../ruvector/ruvllm-wasm.js';

if (await isRuvllmWasmAvailable()) {
  await initRuvllmWasm();
  // Use WASM-native HNSW, SONA, LoRA
} else {
  // Fallback to existing JS implementations
}

Known Issues (v2.0.0)

~~HnswRouterWasm.addPattern() bug~~ — Fixed in v2.0.1 (integer-based geometric distribution replaces wasm_random()).
Stats objects return WASM pointer objects ({__wbg_ptr: number}) — use .toJson() or named accessors.
initSync requires object form: initSync({ module: bytes }) (not raw bytes).

References

ADR-017: RuVector Integration Architecture (updated 2026-03-17 with WASM packages)
ADR-059: @ruvector/rvagent-wasm Integration
Package: @ruvector/[email protected] on npm

Status: Proposed (WASM implementations partially available) Priority: High Estimated Effort: 5 weeks (reduced with WASM components) Dependencies: ADR-006 (Memory), ADR-017 (RuVector) Updated: 2026-03-17

ADR-028: Neural Attention Mechanisms for Claude-Flow V3

ADR-028: Neural Attention Mechanisms for Claude-Flow V3

Context

Decision

Attention Mechanism Types (39 Total)

Category 1: Multi-Head Attention (7 types)

Category 2: Self-Attention Variants (6 types)

Category 3: Cross-Attention for Multi-Modal (5 types)

Category 4: Sparse Attention Patterns (8 types)

Category 5: Linear Attention Approximations (6 types)

Category 6: Flash Attention Optimization (3 types)

Category 7: Mixture-of-Experts Attention (4 types)

Use Cases in Claude-Flow V3

1. Agent Memory Retrieval

2. Context Window Optimization

3. Pattern Matching in Code

4. Swarm Coordination Signals

Integration Points

1. Memory Service (ADR-006)

2. SONA Neural Architecture

3. Plugin Hook System

Performance Considerations

1. GPU Acceleration

2. Quantization Support

3. Caching Strategies

Implementation Plan

Phase 1: Core Attention Service (Week 1-2)

Phase 2: Sparse & Linear Attention (Week 2-3)

Phase 3: Integration (Week 3-4)

Phase 4: MoE & Advanced (Week 4-5)

File Structure

Configuration Schema

CLI Commands

Consequences

Positive

Negative

Neutral

Performance Targets

Success Metrics

References

WASM-Native Implementations (2026-03-17 Update)

Available WASM Components

WASM vs JavaScript Performance

Integration Pattern

Known Issues (v2.0.0)

References