wifi-densepose/vendor/ruvector/docs/research/latent-space/implementation-plans/04-swarm-implementation.md

Swarm Implementation Plan: RuVector Attention Mechanisms

Executive Summary

This document outlines a comprehensive swarm-based implementation strategy for the ruvector-attention crate, leveraging hierarchical agent coordination for parallel development across 11 attention mechanism categories with platform bindings and comprehensive testing.

Timeline: 18 weeks Team Size: 22 concurrent agents (peak) Estimated Deliverables: 50+ attention variants, 4 platform bindings, 500+ tests

---

1. Swarm Architecture

1.1 Hierarchical Topology

swarm:
  name: "ruvector-attention-builder"
  topology: "hierarchical"
  coordination: "claude-flow + memory-based"

  queen:
    role: "hierarchical-coordinator"
    agent_id: "attention-queen-001"
    responsibilities:
      - Strategic planning and task decomposition
      - Cross-team dependency management
      - Progress monitoring and bottleneck detection
      - Quality assurance and integration coordination
      - Performance optimization oversight
      - Stakeholder communication

    tools:
      - mcp__claude-flow__swarm_init
      - mcp__claude-flow__task_orchestrate
      - mcp__claude-flow__swarm_monitor
      - mcp__claude-flow__bottleneck_analyze
      - mcp__claude-flow__performance_report

    coordination_protocol:
      check_in_frequency: "every 4 hours"
      status_aggregation: "real-time via memory"
      escalation_threshold: "20% delay on critical path"

  workers:
    - team: "core-attention-team"
      size: 3
      agent_types: ["coder", "coder", "tester"]
      capabilities:
        - Rust implementation
        - Trait design
        - Unit testing
      tasks:
        - Scaled dot-product attention
        - Multi-head attention
        - Base traits and interfaces
        - Attention masks
      estimated_duration: "2 weeks"
      dependencies: []

    - team: "geometric-team"
      size: 2
      agent_types: ["ml-developer", "coder"]
      capabilities:
        - Non-Euclidean geometry
        - Mathematical optimization
        - Numerical stability
      tasks:
        - Hyperbolic attention (Poincaré, Lorentz)
        - Spherical attention
        - Mixed curvature attention
        - Product space attention
      estimated_duration: "3 weeks"
      dependencies: ["core-attention-team"]

    - team: "sparse-team"
      size: 3
      agent_types: ["coder", "performance-benchmarker", "tester"]
      capabilities:
        - Memory optimization
        - CUDA/parallel processing
        - Performance profiling
      tasks:
        - Local+Global attention
        - Linear attention (Performer)
        - Flash attention
        - Block-sparse attention
      estimated_duration: "4 weeks"
      dependencies: ["core-attention-team"]

    - team: "graph-team"
      size: 3
      agent_types: ["ml-developer", "coder", "tester"]
      capabilities:
        - Graph neural networks
        - Topology-aware processing
        - Edge feature encoding
      tasks:
        - Edge-featured attention
        - RoPE for graphs
        - Cross-space attention
        - Heterogeneous graph attention
      estimated_duration: "3 weeks"
      dependencies: ["core-attention-team", "geometric-team"]

    - team: "adaptive-team"
      size: 2
      agent_types: ["ml-developer", "coder"]
      capabilities:
        - Reinforcement learning
        - Meta-learning
        - Dynamic routing
      tasks:
        - Mixture-of-Experts attention
        - RL-based latent navigation
        - Adaptive parameter sharing
      estimated_duration: "4 weeks"
      dependencies: ["core-attention-team"]

    - team: "training-team"
      size: 2
      agent_types: ["ml-developer", "coder"]
      capabilities:
        - Loss function design
        - Optimization algorithms
        - Training utilities
      tasks:
        - Triplet loss variants
        - Contrastive losses
        - Custom optimizers
        - Regularization techniques
      estimated_duration: "2 weeks"
      dependencies: ["core-attention-team"]

    - team: "platform-team"
      size: 4
      agent_types: ["coder", "coder", "backend-dev", "tester"]
      capabilities:
        - WASM compilation
        - Node.js bindings (NAPI-RS)
        - CLI development
        - Cross-platform testing
      tasks:
        - WASM bindings and optimization
        - NAPI-RS bindings (Node 18, 20, 22)
        - CLI interface and commands
        - SDK and examples
      estimated_duration: "4 weeks"
      dependencies: ["core-attention-team", "geometric-team", "sparse-team"]

    - team: "quality-team"
      size: 3
      agent_types: ["tester", "tester", "reviewer"]
      capabilities:
        - Test-driven development
        - Integration testing
        - Performance benchmarking
        - Code review
      tasks:
        - Unit test suite (90% coverage)
        - Integration tests
        - Benchmark suite
        - Documentation review
      estimated_duration: "ongoing (all phases)"
      dependencies: ["all teams"]

  total_agents: 22
  peak_concurrency: 15
  coordination_overhead: "~10% of total effort"

1.2 Communication Matrix

communication_patterns:
  synchronous:
    - Daily standup (async via memory)
    - Code review sessions
    - Integration checkpoints

  asynchronous:
    - Memory-based state sharing
    - Hook-driven notifications
    - Progress updates

  escalation_paths:
    level_1: "Team lead handles within team"
    level_2: "Queen coordinator resolves cross-team"
    level_3: "Architecture review board"

  shared_artifacts:
    - "attention/traits/base.rs" (all teams)
    - "attention/utils/math.rs" (geometric, sparse)
    - "bindings/api_schema.json" (platform team)
    - "benchmarks/suite.rs" (quality team)

---

2. Phase-by-Phase Execution

Phase 1: Foundation (Week 1-2)

Objective: Establish project structure, CI/CD, and core traits

phase_1:
  duration: "2 weeks"
  agents_involved: 5

  initialization:
    swarm_command: |
      npx claude-flow sparc batch "spec-pseudocode,architect" "ruvector-attention foundation"

  parallel_agents:
    - agent: "specification-writer"
      type: "researcher"
      task: "Write comprehensive specification for all attention mechanisms"
      deliverables:
        - "docs/specs/attention-mechanisms.md"
        - "docs/specs/api-design.md"
      hooks:
        - "pre-task: Load existing research docs"
        - "post-task: Store spec in memory at 'attention/specs/v1'"

    - agent: "trait-architect"
      type: "system-architect"
      task: "Design core traits and interfaces for attention mechanisms"
      deliverables:
        - "src/traits/attention.rs"
        - "src/traits/geometric.rs"
        - "src/traits/adaptive.rs"
      dependencies:
        - "specification-writer (50% complete)"

    - agent: "project-scaffolder"
      type: "backend-dev"
      task: "Create project structure, Cargo.toml, module organization"
      deliverables:
        - "Cargo.toml"
        - "src/lib.rs"
        - "src/core/mod.rs"
        - "src/geometric/mod.rs"
        - "src/sparse/mod.rs"
        - "src/graph/mod.rs"
        - "src/adaptive/mod.rs"
        - "src/training/mod.rs"

    - agent: "ci-engineer"
      type: "cicd-engineer"
      task: "Set up CI/CD pipelines for Rust, WASM, NAPI-RS"
      deliverables:
        - ".github/workflows/rust.yml"
        - ".github/workflows/wasm.yml"
        - ".github/workflows/napi.yml"
        - ".github/workflows/benchmarks.yml"

    - agent: "docs-initializer"
      type: "coder"
      task: "Create initial documentation structure and README"
      deliverables:
        - "README.md"
        - "docs/architecture.md"
        - "docs/getting-started.md"
        - "CONTRIBUTING.md"

  quality_gates:
    - "Cargo build successful"
    - "CI/CD pipelines green"
    - "Architecture review approved"
    - "All traits have documentation"

  memory_coordination:
    keys:
      - "attention/phase1/traits" (trait definitions)
      - "attention/phase1/structure" (module layout)
      - "attention/phase1/specs" (specifications)

  success_criteria:
    - Clean cargo build
    - All CI checks pass
    - Trait design reviewed and approved
    - Documentation structure complete

Phase 2: Core Implementation (Week 3-6)

Objective: Implement foundational attention mechanisms

phase_2:
  duration: "4 weeks"
  agents_involved: 8

  initialization:
    swarm_command: |
      npx claude-flow swarm init --topology mesh --agents 8 --namespace "core-attention"

  parallel_streams:
    stream_a:
      name: "core-attention-stream"
      agents: 3
      coordination: "mesh (all can communicate)"

      tasks:
        - task_id: "core-001"
          agent: "scaled-dot-product-dev"
          type: "coder"
          description: "Implement scaled dot-product attention"
          file: "src/core/scaled_dot_product.rs"
          tests: "tests/core/test_scaled_dot_product.rs"
          estimated_hours: 16
          dependencies: ["phase_1"]

          subtasks:
            - "Forward pass implementation"
            - "Backward pass (gradients)"
            - "Attention mask support"
            - "Numerical stability fixes"
            - "Unit tests (90% coverage)"

          hooks:
            pre_task: |
              npx claude-flow hooks pre-task --description "scaled dot-product attention"
              npx claude-flow hooks session-restore --session-id "core-attention"

            post_edit: |
              npx claude-flow hooks post-edit --file "src/core/scaled_dot_product.rs" \
                --memory-key "attention/core/scaled-dot-product"

            post_task: |
              npx claude-flow hooks post-task --task-id "core-001"
              npx claude-flow hooks notify --message "Scaled dot-product complete"

        - task_id: "core-002"
          agent: "multi-head-dev"
          type: "coder"
          description: "Implement multi-head attention"
          file: "src/core/multi_head.rs"
          tests: "tests/core/test_multi_head.rs"
          estimated_hours: 24
          dependencies: ["core-001"]

          subtasks:
            - "Head projection layers"
            - "Parallel attention computation"
            - "Output concatenation and projection"
            - "Dropout and layer norm"
            - "KV-cache support"
            - "Integration tests"

        - task_id: "core-003"
          agent: "attention-tester"
          type: "tester"
          description: "Comprehensive testing for core attention"
          estimated_hours: 16
          dependencies: ["core-001", "core-002"]

          deliverables:
            - "tests/core/integration_tests.rs"
            - "tests/core/property_tests.rs"
            - "benches/core_benchmarks.rs"

    stream_b:
      name: "geometric-attention-stream"
      agents: 2
      coordination: "leader-follower"
      dependencies: ["stream_a.core-001"]

      tasks:
        - task_id: "geo-001"
          agent: "hyperbolic-expert"
          type: "ml-developer"
          description: "Implement hyperbolic attention mechanisms"
          files:
            - "src/geometric/hyperbolic/poincare.rs"
            - "src/geometric/hyperbolic/lorentz.rs"
            - "src/geometric/hyperbolic/mobius.rs"
          estimated_hours: 40

          subtasks:
            - id: "geo-001-1"
              name: "Poincaré distance and operations"
              hours: 8
              tests_required: true

            - id: "geo-001-2"
              name: "Lorentz model implementation"
              hours: 8
              tests_required: true

            - id: "geo-001-3"
              name: "Möbius transformations"
              hours: 6
              tests_required: true

            - id: "geo-001-4"
              name: "Hyperbolic attention forward pass"
              hours: 10
              tests_required: true

            - id: "geo-001-5"
              name: "Gradient computation (Riemannian)"
              hours: 12
              tests_required: true

            - id: "geo-001-6"
              name: "Numerical stability and edge cases"
              hours: 6
              tests_required: true

          memory_keys:
            - "attention/geometric/hyperbolic/poincare"
            - "attention/geometric/hyperbolic/lorentz"

        - task_id: "geo-002"
          agent: "spherical-dev"
          type: "coder"
          description: "Implement spherical and mixed curvature attention"
          files:
            - "src/geometric/spherical.rs"
            - "src/geometric/mixed_curvature.rs"
          estimated_hours: 32
          dependencies: ["geo-001"]

    stream_c:
      name: "sparse-attention-stream"
      agents: 3
      coordination: "pipeline"
      dependencies: ["stream_a.core-002"]

      tasks:
        - task_id: "sparse-001"
          agent: "local-global-dev"
          type: "coder"
          description: "Implement local+global sparse attention"
          file: "src/sparse/local_global.rs"
          estimated_hours: 20

        - task_id: "sparse-002"
          agent: "linear-attention-dev"
          type: "ml-developer"
          description: "Implement linear attention (Performer-style)"
          file: "src/sparse/linear.rs"
          estimated_hours: 24

        - task_id: "sparse-003"
          agent: "flash-attention-dev"
          type: "performance-benchmarker"
          description: "Implement Flash Attention optimizations"
          file: "src/sparse/flash.rs"
          estimated_hours: 32

          optimization_targets:
            - "Memory-efficient attention computation"
            - "Kernel fusion where possible"
            - "Tiling strategies"
            - "Benchmark vs baseline (>2x speedup target)"

  dependency_graph:
    visualization: |
      core-001 (scaled dot-product)
          ├─> core-002 (multi-head)
          │       ├─> sparse-001 (local+global)
          │       ├─> sparse-002 (linear)
          │       └─> sparse-003 (flash)
          └─> geo-001 (hyperbolic)
                  └─> geo-002 (spherical/mixed)

  coordination_checkpoints:
    - checkpoint: "Week 3 End"
      required_complete: ["core-001", "core-002"]
      review_focus: "API stability, test coverage"

    - checkpoint: "Week 4 End"
      required_complete: ["geo-001", "sparse-001"]
      review_focus: "Numerical accuracy, performance"

    - checkpoint: "Week 6 End"
      required_complete: ["all stream tasks"]
      review_focus: "Integration, documentation"

  quality_gates:
    code_coverage:
      minimum: 85%
      per_module:
        core: 90%
        geometric: 85%
        sparse: 85%

    performance_benchmarks:
      - "Scaled dot-product < 10ms for 1024x1024"
      - "Multi-head (8 heads) < 50ms for 512x512"
      - "Flash attention 2x faster than baseline"

    code_quality:
      - "cargo clippy -- -D warnings"
      - "cargo fmt --check"
      - "No unsafe code without documentation"

Phase 3: Advanced Features (Week 7-10)

Objective: Implement graph and adaptive attention mechanisms

phase_3:
  duration: "4 weeks"
  agents_involved: 7

  initialization:
    swarm_command: |
      npx claude-flow sparc concurrent "coder" "./tasks/advanced-attention.json"

  task_file_format:
    file: "./tasks/advanced-attention.json"
    content: |
      {
        "tasks": [
          {
            "id": "graph-001",
            "type": "graph-attention",
            "description": "Edge-featured attention for graphs",
            "agent_type": "ml-developer",
            "files": ["src/graph/edge_featured.rs"],
            "estimated_hours": 28
          },
          {
            "id": "graph-002",
            "type": "graph-attention",
            "description": "RoPE for graph structures",
            "agent_type": "coder",
            "files": ["src/graph/rope_graph.rs"],
            "estimated_hours": 24
          },
          ...
        ]
      }

  parallel_teams:
    graph_team:
      agents: 3
      tasks:
        - task: "edge-featured-attention"
          description: "Attention with edge features in graph neural networks"
          implementation:
            - "Edge feature encoding"
            - "Attention weight computation with edges"
            - "Message passing integration"
          file: "src/graph/edge_featured.rs"
          tests: "tests/graph/test_edge_featured.rs"

        - task: "rope-graph-attention"
          description: "Rotary positional embeddings for graph structures"
          implementation:
            - "Graph topology encoding"
            - "Distance-based rotations"
            - "Multi-hop attention"
          file: "src/graph/rope_graph.rs"

        - task: "cross-space-attention"
          description: "Attention across different geometric spaces"
          implementation:
            - "Space bridging mechanisms"
            - "Coordinate transformation"
            - "Multi-space aggregation"
          file: "src/graph/cross_space.rs"

      coordination:
        pattern: "mesh"
        shared_memory:
          - "attention/graph/edge-encoding"
          - "attention/graph/topology-utils"

    adaptive_team:
      agents: 2
      tasks:
        - task: "moe-attention"
          description: "Mixture-of-Experts attention routing"
          implementation:
            - "Expert networks (multiple attention heads)"
            - "Gating network (soft routing)"
            - "Load balancing loss"
            - "Sparse expert selection"
          file: "src/adaptive/moe.rs"
          estimated_hours: 36

        - task: "rl-navigation"
          description: "RL-based latent space navigation"
          implementation:
            - "Policy network for attention routing"
            - "Reward shaping (task-specific)"
            - "PPO/A2C training loop"
            - "Experience replay integration"
          file: "src/adaptive/rl_navigation.rs"
          estimated_hours: 40

      dependencies:
        - "phase_2.core-002" (multi-head base)
        - "training-team.optimizer-001" (RL optimizers)

    training_team:
      agents: 2
      tasks:
        - task: "loss-functions"
          description: "Implement training losses for attention learning"
          implementations:
            - "Triplet loss (margin, soft-margin, semi-hard)"
            - "Contrastive loss (InfoNCE, SimCLR)"
            - "Alignment loss (cross-space)"
            - "Distillation loss"
          file: "src/training/losses.rs"

        - task: "optimizers"
          description: "Custom optimizers for attention mechanisms"
          implementations:
            - "Riemannian Adam (for geometric spaces)"
            - "Sparse Adam (for sparse attention)"
            - "Lookahead optimizer wrapper"
          file: "src/training/optimizers.rs"

  integration_points:
    week_7:
      - "Graph attention base traits"
      - "Adaptive attention interfaces"

    week_8:
      - "Edge-featured + RoPE graph complete"
      - "MoE attention routing functional"

    week_9:
      - "RL navigation initial implementation"
      - "Loss functions integrated"

    week_10:
      - "All advanced features complete"
      - "Cross-module integration tests"
      - "Performance benchmarks"

  memory_coordination:
    shared_state:
      - key: "attention/graph/traits"
        updated_by: ["graph-agent-1", "graph-agent-2", "graph-agent-3"]
        read_by: ["all agents"]

      - key: "attention/adaptive/moe-config"
        updated_by: ["adaptive-agent-1"]
        read_by: ["platform-team"]

      - key: "attention/training/losses"
        updated_by: ["training-agent-1"]
        read_by: ["quality-team", "docs-team"]

  quality_gates:
    functionality:
      - "Graph attention handles 10K+ nodes"
      - "MoE routing balances load within 10%"
      - "RL navigation converges in <100 episodes"

    performance:
      - "Edge-featured attention scales O(E) not O(V^2)"
      - "MoE overhead <15% vs single expert"

    testing:
      - "Property-based tests for graph invariants"
      - "RL policy convergence tests"
      - "Loss function gradient checks"

Phase 4: Platform Bindings (Week 11-14)

Objective: Create WASM and NAPI-RS bindings, CLI, SDK

phase_4:
  duration: "4 weeks"
  agents_involved: 4

  initialization:
    swarm_command: |
      npx claude-flow sparc batch "coder,tester" "platform bindings and interfaces"

  parallel_tracks:
    wasm_track:
      agent: "wasm-specialist"
      type: "coder"
      duration: "3 weeks"

      tasks:
        - "Set up wasm-pack configuration"
        - "Create WASM bindings for core attention"
        - "Optimize binary size (<500KB target)"
        - "TypeScript type definitions"
        - "Browser compatibility testing"
        - "NPM package publishing"

      deliverables:
        - "wasm/Cargo.toml"
        - "wasm/src/lib.rs"
        - "wasm/pkg/ (generated)"
        - "wasm/examples/browser-demo.html"
        - "wasm/README.md"

      optimization_targets:
        binary_size: "<500KB gzipped"
        startup_time: "<100ms in browser"
        memory_usage: "<50MB for typical workload"

      build_commands: |
        wasm-pack build --target web --release
        wasm-pack build --target nodejs --release
        wasm-pack test --headless --chrome

    napi_track:
      agents: 2
      type: "backend-dev"
      duration: "3 weeks"

      node_versions: [18, 20, 22]
      platforms: ["linux-x64", "darwin-x64", "darwin-arm64", "win32-x64"]

      tasks:
        - agent: "napi-core-dev"
          focus: "Core NAPI bindings implementation"
          files:
            - "napi/Cargo.toml"
            - "napi/src/lib.rs"
            - "napi/src/attention.rs"
            - "napi/src/geometric.rs"
            - "napi/src/sparse.rs"

          exposed_apis:
            - "ScaledDotProductAttention"
            - "MultiHeadAttention"
            - "HyperbolicAttention"
            - "FlashAttention"
            - "MoEAttention"

        - agent: "napi-test-dev"
          focus: "Cross-platform testing and CI"
          deliverables:
            - "napi/tests/attention.test.js"
            - "napi/tests/benchmark.js"
            - ".github/workflows/napi-build.yml"

          test_matrix: |
            Node 18: linux, macos, windows
            Node 20: linux, macos, windows
            Node 22: linux, macos, windows

      build_configuration: |
        # napi/package.json
        {
          "napi": {
            "name": "ruvector-attention",
            "triples": {
              "defaults": true,
              "additional": [
                "x86_64-unknown-linux-musl",
                "aarch64-unknown-linux-gnu"
              ]
            }
          }
        }

    cli_track:
      agent: "cli-developer"
      type: "backend-dev"
      duration: "2 weeks"

      commands:
        - name: "attention compute"
          description: "Compute attention for given inputs"
          args: ["--type", "--input", "--output", "--config"]

        - name: "attention benchmark"
          description: "Run benchmark suite"
          args: ["--type", "--size", "--iterations"]

        - name: "attention train"
          description: "Train attention parameters"
          args: ["--data", "--epochs", "--lr"]

        - name: "attention convert"
          description: "Convert between formats (ONNX, PyTorch, etc.)"
          args: ["--from", "--to", "--model"]

      implementation:
        framework: "clap (v4)"
        config_format: "TOML"
        output_format: "JSON, YAML, or pretty-print"

      deliverables:
        - "cli/src/main.rs"
        - "cli/src/commands/compute.rs"
        - "cli/src/commands/benchmark.rs"
        - "cli/src/commands/train.rs"
        - "cli/README.md"
        - "cli/examples/*.toml"

    sdk_track:
      agent: "sdk-architect"
      type: "coder"
      duration: "2 weeks"

      languages:
        - rust: "Native (main crate)"
        - javascript: "Via WASM/NAPI"
        - python: "Via PyO3 (future)"

      sdk_components:
        - "High-level attention builder API"
        - "Configuration management"
        - "Model serialization (serde)"
        - "Integration examples"

      examples:
        - "examples/quick-start.rs"
        - "examples/custom-attention.rs"
        - "examples/training-loop.rs"
        - "examples/onnx-export.rs"
        - "examples/wasm-integration/"
        - "examples/node-integration/"

      documentation:
        - "SDK reference documentation"
        - "Tutorial series (5+ tutorials)"
        - "API migration guide"
        - "Performance tuning guide"

  integration_phase:
    week_14:
      tasks:
        - "Cross-platform testing (all bindings)"
        - "End-to-end examples"
        - "Documentation review"
        - "NPM/crates.io publishing dry-run"

      validation:
        - "WASM works in Chrome, Firefox, Safari"
        - "NAPI works on all target platforms"
        - "CLI help is comprehensive"
        - "SDK examples all compile and run"

  quality_gates:
    wasm:
      - "Binary size <500KB"
      - "TypeScript types generated correctly"
      - "Browser tests pass in all major browsers"

    napi:
      - "All platforms build successfully"
      - "Node 18, 20, 22 compatibility"
      - "Zero-copy optimizations where possible"

    cli:
      - "All commands have --help"
      - "Error messages are actionable"
      - "Performance acceptable (<1s startup)"

    sdk:
      - "All examples compile without warnings"
      - "Documentation coverage >90%"
      - "API surface is ergonomic"

Phase 5: Testing & Optimization (Week 15-18)

Objective: Comprehensive testing, benchmarking, optimization, and release preparation

phase_5:
  duration: "4 weeks"
  agents_involved: 6

  initialization:
    swarm_command: |
      npx claude-flow sparc pipeline "comprehensive testing and optimization"

  sequential_stages:
    stage_1_unit_tests:
      duration: "1 week"
      agents: 2

      coverage_targets:
        overall: 85%
        core: 90%
        geometric: 85%
        sparse: 85%
        graph: 85%
        adaptive: 80%
        training: 85%
        bindings: 80%

      test_types:
        unit_tests:
          - "Function-level correctness"
          - "Edge case handling"
          - "Error propagation"

        property_tests:
          - "Attention weights sum to 1"
          - "Gradient numerical stability"
          - "Geometric manifold constraints"

        doc_tests:
          - "All public APIs have runnable examples"
          - "Documentation examples are tested"

      tasks:
        - agent: "unit-test-writer-1"
          modules: ["core", "geometric", "sparse"]
          estimated_hours: 40

        - agent: "unit-test-writer-2"
          modules: ["graph", "adaptive", "training"]
          estimated_hours: 40

      tools:
        - "cargo test --all-features"
        - "cargo tarpaulin (coverage)"
        - "cargo-mutants (mutation testing)"

    stage_2_integration_tests:
      duration: "1 week"
      agents: 2
      dependencies: ["stage_1_unit_tests"]

      test_scenarios:
        - scenario: "End-to-end attention pipeline"
          description: "Input -> Attention -> Output with real data"
          modules: ["core", "sparse"]

        - scenario: "Cross-space attention flow"
          description: "Euclidean -> Hyperbolic -> Spherical"
          modules: ["geometric", "graph"]

        - scenario: "MoE routing and expert selection"
          description: "Multi-expert attention with load balancing"
          modules: ["adaptive", "training"]

        - scenario: "WASM/NAPI integration"
          description: "Rust <-> JS data marshalling"
          modules: ["bindings"]

      tasks:
        - agent: "integration-tester-1"
          focus: "Core integration tests"
          files: ["tests/integration/attention_pipeline.rs"]

        - agent: "integration-tester-2"
          focus: "Platform integration tests"
          files: ["tests/integration/bindings_test.rs"]

      validation:
        - "All integration tests pass"
        - "No memory leaks detected (valgrind)"
        - "Thread safety verified (loom)"

    stage_3_benchmarks:
      duration: "1 week"
      agents: 1
      dependencies: ["stage_2_integration_tests"]

      agent: "performance-engineer"
      type: "performance-benchmarker"

      benchmark_suite:
        attention_mechanisms:
          - name: "scaled_dot_product_latency"
            sizes: [128, 256, 512, 1024, 2048]
            metrics: ["latency_p50", "latency_p95", "latency_p99"]

          - name: "multi_head_throughput"
            heads: [4, 8, 16, 32]
            sequence_lengths: [128, 512, 1024]
            metrics: ["throughput_qps", "memory_usage"]

          - name: "hyperbolic_accuracy"
            curvatures: [-1.0, -0.5, -0.1]
            dimensions: [64, 128, 256]
            metrics: ["numerical_error", "gradient_stability"]

          - name: "flash_memory_efficiency"
            batch_sizes: [1, 4, 16, 64]
            sequence_lengths: [1024, 2048, 4096]
            metrics: ["peak_memory_mb", "memory_vs_baseline_ratio"]

          - name: "moe_routing_overhead"
            num_experts: [4, 8, 16]
            top_k: [1, 2, 4]
            metrics: ["overhead_percent", "load_balance_variance"]

        cross_platform:
          - name: "wasm_vs_native_performance"
            attention_types: ["scaled_dot_product", "multi_head"]
            metrics: ["slowdown_ratio", "startup_overhead"]

          - name: "napi_marshalling_overhead"
            data_sizes: ["1KB", "10KB", "100KB", "1MB"]
            metrics: ["serialization_us", "deserialization_us"]

      regression_detection:
        threshold: "5% slowdown vs baseline"
        baseline_branch: "main"
        alert_on_regression: true

      optimization_targets:
        - "Identify top 3 bottlenecks per module"
        - "Profile with perf/flamegraph"
        - "Suggest SIMD/parallelization opportunities"

      deliverables:
        - "benches/attention_suite.rs"
        - "docs/performance-report.md"
        - "charts/latency-comparison.svg"
        - "charts/memory-usage.svg"

    stage_4_optimization:
      duration: "1 week"
      agents: 1
      dependencies: ["stage_3_benchmarks"]

      agent: "optimizer"
      type: "coder"

      optimization_areas:
        algorithmic:
          - "Replace naive implementations with optimized variants"
          - "Cache frequently computed values"
          - "Reduce allocation overhead"

        simd:
          - "Vectorize attention score computation"
          - "Use packed_simd for small matrices"
          - "Platform-specific intrinsics where beneficial"

        memory:
          - "Pool allocations for repeated use"
          - "In-place operations where possible"
          - "Reduce copying via smart borrowing"

        parallelism:
          - "Rayon for embarrassingly parallel operations"
          - "Multi-head attention parallelization"
          - "Batch processing optimizations"

      validation:
        - "Benchmarks show >=10% improvement on >=3 workloads"
        - "No regression on any existing benchmark"
        - "All tests still pass after optimization"

  final_validation:
    week_18:
      checklist:
        code_quality:
          - "cargo clippy --all-targets --all-features -- -D warnings ✓"
          - "cargo fmt --all -- --check ✓"
          - "cargo deny check licenses ✓"
          - "cargo audit ✓"

        testing:
          - "Unit test coverage >=85% ✓"
          - "Integration tests pass ✓"
          - "Property tests pass ✓"
          - "Doc tests pass ✓"
          - "WASM tests pass (all browsers) ✓"
          - "NAPI tests pass (all platforms) ✓"

        performance:
          - "No >5% regression vs baseline ✓"
          - "Memory usage within targets ✓"
          - "Binary sizes within targets ✓"

        documentation:
          - "API docs complete (>90% coverage) ✓"
          - "Examples all compile and run ✓"
          - "README is comprehensive ✓"
          - "Architecture docs updated ✓"
          - "Migration guide written ✓"

        release_prep:
          - "CHANGELOG.md updated ✓"
          - "Version bumped (SemVer) ✓"
          - "Git tags created ✓"
          - "Release notes drafted ✓"
          - "NPM/crates.io publish dry-run successful ✓"

  deliverables:
    - "Comprehensive test suite (500+ tests)"
    - "Benchmark suite with historical tracking"
    - "Performance report with charts"
    - "Optimization documentation"
    - "Release checklist (completed)"

---

3. Task Decomposition Template

Standard Task Breakdown

task_template:
  attention_type: "{mechanism_name}"
  module: "src/{category}/{mechanism}.rs"

  subtasks:
    - id: "{mechanism}-001"
      name: "Mathematical foundation implementation"
      description: "Core mathematical operations (distances, transformations)"
      estimated_hours: 6
      dependencies: []
      tests_required: true
      test_file: "tests/{category}/test_{mechanism}_math.rs"

      acceptance_criteria:
        - "All mathematical operations numerically stable"
        - "Unit tests cover edge cases (zero, infinity, NaN)"
        - "Property tests validate mathematical properties"

      memory_keys:
        write: ["attention/{category}/{mechanism}/math"]
        read: ["attention/core/traits"]

    - id: "{mechanism}-002"
      name: "Forward pass implementation"
      description: "Attention computation in forward direction"
      estimated_hours: 8
      dependencies: ["{mechanism}-001"]
      tests_required: true

      implementation_checklist:
        - "Query, Key, Value transformations"
        - "Attention score computation"
        - "Softmax/normalization"
        - "Output aggregation"
        - "Attention mask support"

      performance_targets:
        - "Latency <{target}ms for {size}x{size}"
        - "Memory usage <{target}MB"

    - id: "{mechanism}-003"
      name: "Backward pass (gradient computation)"
      description: "Compute gradients for backpropagation"
      estimated_hours: 12
      dependencies: ["{mechanism}-002"]
      tests_required: true

      validation:
        - "Gradient check (numerical vs analytical)"
        - "Gradient flow (no vanishing/exploding)"
        - "Memory efficiency (gradient checkpointing if needed)"

    - id: "{mechanism}-004"
      name: "Numerical stability and edge cases"
      description: "Handle numerical issues and corner cases"
      estimated_hours: 4
      dependencies: ["{mechanism}-002", "{mechanism}-003"]
      tests_required: true

      edge_cases:
        - "Very large/small values"
        - "Zero attention weights"
        - "Single-element sequences"
        - "Batch size = 1"

    - id: "{mechanism}-005"
      name: "Integration and documentation"
      description: "Integrate with rest of codebase and document"
      estimated_hours: 4
      dependencies: ["{mechanism}-004"]
      tests_required: true

      deliverables:
        - "Module exports added to lib.rs"
        - "Public API documented (rustdoc)"
        - "Example usage code"
        - "Integration test"

  total_estimated_hours: 34

  coordination:
    hooks:
      pre_task: |
        npx claude-flow hooks pre-task --description "{mechanism} attention"
        npx claude-flow hooks session-restore --session-id "attention-{category}"

      post_each_subtask: |
        npx claude-flow hooks post-edit --file "src/{category}/{mechanism}.rs" \
          --memory-key "attention/{category}/{mechanism}/{subtask-id}"

      post_task: |
        npx claude-flow hooks post-task --task-id "{mechanism}"
        npx claude-flow hooks notify --message "{mechanism} attention complete"

  quality_gates:
    code:
      - "cargo clippy passes"
      - "cargo fmt applied"
      - "No unsafe code without justification"

    tests:
      - "Coverage >=85%"
      - "All tests pass"
      - "Property tests included"

    performance:
      - "Meets latency target"
      - "Meets memory target"
      - "No unexpected allocations in hot path"

Example: Hyperbolic Attention Decomposition

task: "hyperbolic_attention"
module: "src/geometric/hyperbolic.rs"

subtasks:
  - id: "hyp-001"
    name: "Poincaré ball distance implementation"
    estimated_hours: 4
    dependencies: []
    tests_required: true

    implementation:
      - "Poincaré distance formula"
      - "Möbius addition"
      - "Möbius scalar multiplication"
      - "Exponential map (exp_map)"
      - "Logarithmic map (log_map)"

    tests:
      - "Distance symmetry: d(x, y) == d(y, x)"
      - "Triangle inequality"
      - "Boundary behavior (norm -> 1)"
      - "Numerical stability near origin"

  - id: "hyp-002"
    name: "Lorentz model operations"
    estimated_hours: 6
    dependencies: []
    tests_required: true

    implementation:
      - "Lorentzian distance"
      - "Lorentz addition (gyrovector)"
      - "Parallel transport"
      - "Model conversion (Poincaré <-> Lorentz)"

    validation:
      - "Metric signature (-1, +1, +1, ...)"
      - "Hyperboloid constraint"

  - id: "hyp-003"
    name: "Hyperbolic attention forward pass"
    estimated_hours: 8
    dependencies: ["hyp-001", "hyp-002"]
    tests_required: true

    algorithm: |
      1. Map Q, K to Poincaré ball (if not already)
      2. Compute pairwise Poincaré distances
      3. Convert distances to attention scores (negative distance)
      4. Apply softmax in tangent space
      5. Aggregate V using Möbius weighted average
      6. Map output back to appropriate space

    performance_target:
      - "512x512 attention in <50ms"
      - "Numerical error <1e-6"

  - id: "hyp-004"
    name: "Riemannian gradient computation"
    estimated_hours: 12
    dependencies: ["hyp-003"]
    tests_required: true

    components:
      - "Riemannian gradient (projection to tangent space)"
      - "Parallel transport for gradient propagation"
      - "Geodesic retraction"

    gradient_check:
      - "Numerical gradient vs analytical (tolerance 1e-5)"
      - "Gradient norm stability"

  - id: "hyp-005"
    name: "Numerical stability fixes"
    estimated_hours: 4
    dependencies: ["hyp-003", "hyp-004"]
    tests_required: true

    issues_to_address:
      - "Overflow/underflow in exponentials"
      - "Division by zero near boundary"
      - "Loss of precision in log operations"

    solutions:
      - "Log-sum-exp trick for softmax"
      - "Clamping near boundary (norm < 1 - ε)"
      - "Stable division via artanh"

total_hours: 34
completion_criteria:
  - "All subtasks tested individually"
  - "Integration test with multi-head attention"
  - "Benchmark vs Euclidean baseline"
  - "Documentation with mathematical background"

---

4. Agent Communication Protocol

4.1 Memory Coordination Namespace

memory_structure:
  namespace: "coordination"

  key_hierarchy:
    # Swarm-level coordination
    swarm:
      status: "swarm/hierarchical/status"
      progress: "swarm/hierarchical/progress"
      metrics: "swarm/hierarchical/metrics"
      hierarchy: "swarm/shared/hierarchy"

    # Phase-level state
    phases:
      phase_1: "swarm/phase/1/status"
      phase_2: "swarm/phase/2/status"
      phase_3: "swarm/phase/3/status"
      phase_4: "swarm/phase/4/status"
      phase_5: "swarm/phase/5/status"

    # Team-level coordination
    teams:
      core: "swarm/team/core-attention/status"
      geometric: "swarm/team/geometric/status"
      sparse: "swarm/team/sparse/status"
      graph: "swarm/team/graph/status"
      adaptive: "swarm/team/adaptive/status"
      training: "swarm/team/training/status"
      platform: "swarm/team/platform/status"
      quality: "swarm/team/quality/status"

    # Worker-level state
    workers:
      pattern: "swarm/worker-{id}/status"
      examples:
        - "swarm/worker-1/status"  # core-attention-dev-1
        - "swarm/worker-2/status"  # core-attention-dev-2
        - "swarm/worker-3/status"  # core-attention-tester

    # Artifact-level sharing
    artifacts:
      traits: "attention/core/traits"
      scaled_dot_product: "attention/core/scaled-dot-product"
      multi_head: "attention/core/multi-head"
      hyperbolic: "attention/geometric/hyperbolic"
      flash: "attention/sparse/flash"
      moe: "attention/adaptive/moe"
      wasm_api: "bindings/wasm/api"
      napi_api: "bindings/napi/api"
      benchmarks: "tests/benchmarks/results"

4.2 Hook Integration

hooks_protocol:
  pre_task:
    command: |
      npx claude-flow hooks pre-task --description "{task_description}"

    actions:
      - "Restore previous session state"
      - "Load relevant memory keys"
      - "Check dependencies completed"
      - "Allocate resources"

    memory_reads:
      - "swarm/hierarchical/status"
      - "swarm/team/{team}/status"
      - "attention/core/traits" (if needed)

    example: |
      npx claude-flow hooks pre-task \
        --description "Implement hyperbolic attention" \
        --session-id "attention-swarm" \
        --team "geometric"

  session_restore:
    command: |
      npx claude-flow hooks session-restore --session-id "{session_id}"

    restores:
      - "Previous task context"
      - "File edit history"
      - "Memory snapshots"
      - "Coordination state"

    example: |
      npx claude-flow hooks session-restore --session-id "attention-swarm"

  post_edit:
    command: |
      npx claude-flow hooks post-edit --file "{file_path}" --memory-key "{key}"

    actions:
      - "Auto-format code (rustfmt)"
      - "Store file state in memory"
      - "Train neural patterns"
      - "Notify dependent agents"

    memory_writes:
      - "{memory_key}/content"
      - "{memory_key}/timestamp"
      - "{memory_key}/agent"

    example: |
      npx claude-flow hooks post-edit \
        --file "src/geometric/hyperbolic.rs" \
        --memory-key "attention/geometric/hyperbolic"

  notify:
    command: |
      npx claude-flow hooks notify --message "{message}" --recipients "{agents}"

    use_cases:
      - "Notify dependent agents of completion"
      - "Alert queen coordinator of issues"
      - "Broadcast API changes"

    example: |
      npx claude-flow hooks notify \
        --message "Hyperbolic attention complete, API available" \
        --recipients "adaptive-team,graph-team"

  post_task:
    command: |
      npx claude-flow hooks post-task --task-id "{task_id}"

    actions:
      - "Mark task complete in memory"
      - "Update team progress"
      - "Trigger dependent tasks"
      - "Generate completion metrics"

    memory_writes:
      - "swarm/worker-{id}/tasks/{task_id}/complete"
      - "swarm/team/{team}/progress"
      - "swarm/hierarchical/progress"

    example: |
      npx claude-flow hooks post-task --task-id "hyp-001"

  session_end:
    command: |
      npx claude-flow hooks session-end --export-metrics true

    actions:
      - "Export session summary"
      - "Persist all memory"
      - "Generate performance report"
      - "Archive session data"

    example: |
      npx claude-flow hooks session-end \
        --export-metrics true \
        --output "session-reports/phase-2-completion.json"

4.3 Agent Communication Patterns

communication_patterns:
  broadcast:
    description: "One agent sends to all in team"
    use_case: "API change notification"
    mechanism: "Memory write to shared key + notify hook"
    example: |
      # Agent writes to shared memory
      mcp__claude-flow__memory_usage {
        action: "store",
        key: "attention/core/traits-v2",
        namespace: "coordination",
        value: JSON.stringify({ updated_api: {...} })
      }

      # Notify all team members
      npx claude-flow hooks notify \
        --message "Core traits updated to v2" \
        --recipients "all"

  request_response:
    description: "Agent requests info from another"
    use_case: "Check if dependency complete"
    mechanism: "Memory read + conditional logic"
    example: |
      # Check if dependency complete
      const status = mcp__claude-flow__memory_usage {
        action: "retrieve",
        key: "swarm/worker-1/tasks/core-001/complete",
        namespace: "coordination"
      }

      if (status.value === "true") {
        // Proceed with dependent task
      }

  pipeline:
    description: "Sequential handoff between agents"
    use_case: "Core -> Geometric -> Graph pipeline"
    mechanism: "Post-task hook triggers next agent"
    example: |
      # Agent 1 completes
      npx claude-flow hooks post-task --task-id "core-001"

      # Hook automatically triggers Agent 2
      # (configured in swarm initialization)

  peer_to_peer:
    description: "Direct collaboration between agents"
    use_case: "Joint debugging session"
    mechanism: "Shared memory workspace"
    example: |
      # Both agents read/write to shared debug space
      mcp__claude-flow__memory_usage {
        action: "store",
        key: "swarm/debug/hyperbolic-issue",
        namespace: "coordination",
        value: JSON.stringify({
          issue: "Gradient overflow",
          findings: [...],
          proposed_fix: "..."
        })
      }

4.4 Conflict Resolution

conflict_resolution:
  file_edit_conflicts:
    prevention:
      - "Assign non-overlapping file ownership"
      - "Use feature branches per agent"
      - "Lock mechanism via memory flags"

    detection:
      - "Git merge conflicts"
      - "Memory timestamp comparison"

    resolution:
      - "Queen coordinator arbitrates"
      - "Latest timestamp wins (with review)"
      - "Manual merge by senior agent"

  api_breaking_changes:
    protocol:
      1. "Agent proposes change via memory"
      2. "Queen coordinator reviews impact"
      3. "Affected agents notified"
      4. "Consensus or decision by queen"
      5. "Migration plan created"
      6. "Coordinated update"

  resource_contention:
    scenarios:
      - "Multiple agents need CI runner"
      - "Memory/CPU limits reached"

    solution:
      - "Queen coordinator queues tasks"
      - "Priority-based scheduling"
      - "Resource reservation system"

---

5. Quality Gates

5.1 Code Review Requirements

code_review:
  required_approvals: 2
  reviewer_types:
    - "Peer reviewer (same team)"
    - "Cross-team reviewer OR queen coordinator"

  review_checklist:
    correctness:
      - "Algorithm implements spec correctly"
      - "Edge cases handled"
      - "Error handling appropriate"

    performance:
      - "No obvious performance issues"
      - "Allocations minimized in hot paths"
      - "Complexity analysis reasonable"

    testing:
      - "Unit tests cover >85% of code"
      - "Integration tests exist"
      - "Benchmarks added for new features"

    documentation:
      - "Public APIs documented"
      - "Complex algorithms explained"
      - "Examples provided"

    style:
      - "cargo fmt applied"
      - "cargo clippy passes"
      - "Naming conventions followed"

  automated_checks:
    ci_pipeline:
      - name: "Rust CI"
        steps:
          - "cargo build --all-features"
          - "cargo test --all-features"
          - "cargo clippy -- -D warnings"
          - "cargo fmt --check"

      - name: "WASM CI"
        steps:
          - "wasm-pack build --target web"
          - "wasm-pack test --headless --chrome"

      - name: "NAPI CI"
        steps:
          - "npm run build"
          - "npm test"
        matrix:
          node: [18, 20, 22]
          os: [ubuntu-latest, macos-latest, windows-latest]

      - name: "Benchmarks"
        steps:
          - "cargo bench --no-fail-fast"
          - "compare with baseline"
          - "alert on >5% regression"

  approval_workflow:
    1: "Agent submits PR"
    2: "Automated checks run"
    3: "Peer review (24h SLA)"
    4: "Address feedback"
    5: "Cross-team review (24h SLA)"
    6: "Final approval by queen (if needed)"
    7: "Merge to main"

5.2 Coverage Requirements

coverage:
  minimum_overall: 85%

  per_module:
    core:
      target: 90%
      rationale: "Foundation for all other modules"
      critical_paths:
        - "Attention score computation"
        - "Softmax stability"
        - "Gradient flow"

    geometric:
      target: 85%
      rationale: "Complex math requires thorough testing"
      focus_areas:
        - "Distance computations"
        - "Manifold constraints"
        - "Numerical stability"

    sparse:
      target: 85%
      rationale: "Performance-critical code"
      focus_areas:
        - "Memory access patterns"
        - "Kernel optimizations"
        - "Correctness vs dense baseline"

    graph:
      target: 85%
      rationale: "Topology-aware operations"
      focus_areas:
        - "Edge feature handling"
        - "Graph invariants"
        - "Heterogeneous graphs"

    adaptive:
      target: 80%
      rationale: "RL components have stochastic behavior"
      focus_areas:
        - "Routing logic"
        - "Load balancing"
        - "Expert selection"

    training:
      target: 85%
      rationale: "Loss/optimizer correctness critical"
      focus_areas:
        - "Gradient computation"
        - "Loss function properties"
        - "Optimizer updates"

    bindings:
      target: 80%
      rationale: "Type marshalling and FFI"
      focus_areas:
        - "Data conversion"
        - "Error handling across FFI"
        - "Memory management"

  measurement:
    tool: "cargo-tarpaulin"
    command: |
      cargo tarpaulin --all-features --workspace \
        --out Xml --out Html --output-dir coverage/ \
        --exclude-files 'target/*' 'examples/*'

    reporting:
      - "Coverage badge in README"
      - "HTML report in coverage/"
      - "CI fails if below threshold"

  exceptions:
    - file: "src/platform_specific/*.rs"
      reason: "Platform-specific code tested on respective platforms"

    - file: "src/experimental/*.rs"
      reason: "Experimental features, lower threshold (70%)"

5.3 Performance Benchmarks

benchmarks:
  regression_threshold: 5%
  baseline_branch: "main"

  required_benchmarks:
    core_attention:
      - name: "scaled_dot_product_latency"
        metric: "latency (ms)"
        sizes: [128, 256, 512, 1024, 2048]
        target: "<10ms for 1024x1024"

        acceptance:
          p50: "<10ms"
          p95: "<15ms"
          p99: "<20ms"

      - name: "multi_head_throughput"
        metric: "queries per second"
        configurations:
          - heads: 8, seq_len: 512
          - heads: 16, seq_len: 512
          - heads: 8, seq_len: 1024
        target: ">1000 QPS for 8 heads, 512 seq_len"

    geometric:
      - name: "hyperbolic_accuracy"
        metric: "numerical error"
        test_cases:
          - "Poincaré distance"
          - "Möbius operations"
          - "Gradient computation"
        target: "<1e-6 error"

      - name: "hyperbolic_performance"
        metric: "latency vs Euclidean"
        target: "<2x slowdown vs Euclidean baseline"

    sparse:
      - name: "flash_memory_usage"
        metric: "peak memory (MB)"
        batch_sizes: [1, 4, 16, 64]
        seq_lengths: [1024, 2048, 4096]
        target: "<50% of dense attention memory"

      - name: "flash_speedup"
        metric: "latency vs dense"
        target: ">2x faster than dense attention"

      - name: "linear_attention_scaling"
        metric: "complexity"
        seq_lengths: [1024, 2048, 4096, 8192]
        target: "O(N) scaling confirmed"

    adaptive:
      - name: "moe_routing_overhead"
        metric: "overhead (%)"
        num_experts: [4, 8, 16]
        target: "<15% overhead vs single expert"

      - name: "moe_load_balance"
        metric: "variance in expert utilization"
        target: "<10% variance"

    bindings:
      - name: "wasm_startup_time"
        metric: "latency (ms)"
        target: "<100ms in browser"

      - name: "wasm_vs_native"
        metric: "slowdown ratio"
        target: "<3x slower than native"

      - name: "napi_marshalling"
        metric: "overhead (us)"
        data_sizes: ["1KB", "10KB", "100KB"]
        target: "<100us for 10KB"

  benchmark_suite:
    framework: "criterion.rs"
    configuration: |
      [profile.bench]
      opt-level = 3
      lto = true
      codegen-units = 1

    execution: |
      cargo bench --all-features -- --save-baseline main
      cargo bench --all-features -- --baseline main

    reporting:
      - "HTML report in target/criterion/"
      - "Comparison vs baseline"
      - "Historical tracking"
      - "Alert on regression >5%"

  continuous_benchmarking:
    schedule: "Every PR + nightly on main"
    storage: "GitHub Pages (historical data)"
    visualization: "Charts showing trends over time"

5.4 Security & Safety

security:
  unsafe_code:
    policy: "Minimize unsafe code, justify all usage"

    justification_required:
      - "Performance-critical section (with benchmarks)"
      - "FFI boundary (WASM/NAPI)"
      - "Platform-specific optimizations"

    review_process:
      - "All unsafe blocks require documentation"
      - "Soundness argument provided"
      - "Alternative safe approaches considered"
      - "Two reviewers required (vs one for safe code)"

  dependencies:
    audit_tool: "cargo-audit"
    schedule: "Every PR + daily on main"

    allowed_licenses:
      - "MIT"
      - "Apache-2.0"
      - "BSD-3-Clause"
      - "ISC"

    license_check:
      tool: "cargo-deny"
      command: |
        cargo deny check licenses

  fuzzing:
    targets:
      - "Attention score computation (prevent NaN/Inf)"
      - "Geometric operations (boundary cases)"
      - "FFI data marshalling (prevent crashes)"

    tool: "cargo-fuzz"
    schedule: "Weekly fuzzing runs (4+ hours each)"

---

6. Rollback Strategy

6.1 Failure Scenarios & Responses

rollback_scenarios:
  scenario_1_task_failure:
    description: "Individual task fails (tests don't pass, bugs found)"

    detection:
      - "CI pipeline fails"
      - "Code review rejects"
      - "Benchmarks show >10% regression"

    response:
      level_1_retry:
        - "Agent debugs and fixes within 4 hours"
        - "If fixed: proceed normally"

      level_2_reassign:
        - "Queen coordinator reassigns to different agent"
        - "Provide additional context/resources"
        - "Timeline: 8-12 hours"

      level_3_revert:
        - "Revert changes (git revert)"
        - "Restore memory state to pre-task"
        - "Schedule task for next phase"

    prevention:
      - "Smaller task sizes (<16 hours)"
      - "Frequent check-ins (every 4-6 hours)"
      - "Incremental commits"

  scenario_2_phase_failure:
    description: "Entire phase cannot complete on time or quality gates fail"

    detection:
      - "50% of tasks delayed >20%"
      - "Quality gate failures across multiple modules"
      - "Critical path blocked"

    response:
      assess:
        - "Queen coordinator analyzes root cause"
        - "Identify: scope creep, underestimation, blockers"

      options:
        option_a_scope_reduction:
          - "Defer non-critical features to later phase"
          - "Example: Defer RL navigation to Phase 6"
          - "Deliver core functionality first"

        option_b_extend_timeline:
          - "Add 1-2 weeks to phase"
          - "Adjust downstream phase schedules"
          - "Communicate to stakeholders"

        option_c_add_resources:
          - "Spawn additional agents"
          - "Parallelize more aggressively"
          - "Risk: coordination overhead"

      rollback:
        - "Revert to last stable phase checkpoint"
        - "Restore memory snapshot"
        - "Restart phase with adjusted plan"

  scenario_3_integration_failure:
    description: "Modules don't integrate properly"

    detection:
      - "Integration tests fail"
      - "API mismatches between modules"
      - "Circular dependencies discovered"

    response:
      immediate:
        - "Halt all affected modules"
        - "Create integration task force (3 agents)"
        - "Queen coordinator leads"

      resolution:
        - "Identify API contract issues"
        - "Design integration layer"
        - "Coordinate simultaneous updates"
        - "Re-run integration tests"

      rollback_if_needed:
        - "Revert all affected modules to last known good state"
        - "Redesign interfaces"
        - "Sequential re-integration"

  scenario_4_performance_regression:
    description: "Benchmarks show unacceptable performance loss"

    detection:
      - "Benchmarks >10% slower than baseline"
      - "Memory usage exceeds targets by >20%"

    response:
      analyze:
        - "Profile with perf/flamegraph"
        - "Identify bottleneck (algorithm, allocation, I/O)"

      fix_options:
        quick_fix:
          - "If simple fix: apply and re-benchmark (2-4 hours)"

        optimization_sprint:
          - "If complex: dedicated optimization agent"
          - "Timeline: 1-2 days"
          - "Benchmark-driven development"

        rollback:
          - "If unfixable in reasonable time"
          - "Revert to previous performant version"
          - "Schedule optimization as separate task"

  scenario_5_dependency_unavailable:
    description: "External dependency breaks or is unavailable"

    examples:
      - "Crate update breaks API"
      - "WASM/NAPI tooling regression"
      - "CI infrastructure down"

    response:
      workaround:
        - "Pin dependency to last known good version"
        - "Use alternative if available"

      fork_if_needed:
        - "Fork dependency and apply fixes"
        - "Contribute upstream"
        - "Track for eventual merge"

      defer:
        - "If non-critical dependency"
        - "Mark as TODO for later"
        - "Proceed without feature"

6.2 Checkpoint & Restore

checkpointing:
  frequency:
    automatic:
      - "End of each phase"
      - "After major milestones"
      - "Before risky operations (major refactors)"

    manual:
      - "Queen coordinator can trigger anytime"
      - "Agent can request checkpoint before complex task"

  checkpoint_contents:
    code:
      - "Git tag: checkpoint-phase-{N}-{timestamp}"
      - "All branches merged to checkpoint branch"
      - "Clean build verified"

    memory:
      - "Memory snapshot: all coordination/* keys"
      - "Export to JSON"
      - "Store in .checkpoints/memory-{timestamp}.json"

    state:
      - "Task status (pending/in_progress/completed)"
      - "Agent assignments"
      - "Dependency graph"
      - "Quality gate results"

    artifacts:
      - "Test results"
      - "Benchmark data"
      - "Coverage reports"

  restore_procedure:
    when_to_restore:
      - "Phase failure requiring restart"
      - "Catastrophic bug discovered"
      - "Need to explore alternative approach"

    steps:
      1. "Identify checkpoint to restore"
      2. "Halt all agents"
      3. "Git reset --hard <checkpoint-tag>"
      4. "Restore memory from JSON"
      5. "Update agent task assignments"
      6. "Resume with adjusted plan"

    command_sequence: |
      # Halt swarm
      npx claude-flow swarm stop

      # Restore code
      git checkout checkpoint-phase-2-20250515

      # Restore memory
      npx claude-flow memory restore .checkpoints/memory-20250515.json

      # Verify
      cargo build --all-features
      cargo test --all-features

      # Resume
      npx claude-flow swarm resume --plan adjusted-phase-2-plan.yaml

6.3 Graceful Degradation

degradation_strategy:
  principle: "Deliver core functionality first, advanced features later"

  feature_priority_tiers:
    tier_1_critical:
      - "Scaled dot-product attention"
      - "Multi-head attention"
      - "Basic WASM bindings"
      - "Basic NAPI bindings"
      must_deliver: true
      rollback_not_allowed: true

    tier_2_important:
      - "Hyperbolic attention (Poincaré only)"
      - "Flash attention"
      - "CLI basic commands"
      - "Unit tests (85% coverage)"
      can_defer: "to next release if needed"

    tier_3_nice_to_have:
      - "Spherical attention"
      - "MoE attention"
      - "RL navigation"
      - "Full CLI suite"
      can_defer: "yes, without major impact"

    tier_4_experimental:
      - "Cross-space attention"
      - "Advanced RL strategies"
      - "Custom optimizers"
      can_defer: "yes, move to v2.0"

  degradation_triggers:
    time_pressure:
      - "If <80% of phase complete at 80% of timeline"
      - "Action: Defer Tier 3+ features"

    quality_issues:
      - "If Tier 1 features don't meet quality gates"
      - "Action: Halt all Tier 2+ work, focus on Tier 1"

    resource_constraints:
      - "If agent capacity insufficient"
      - "Action: Prioritize ruthlessly, defer Tier 3+"

  communication:
    stakeholders:
      - "Transparent about deferrals"
      - "Maintain roadmap with updated dates"
      - "Document rationale for decisions"

6.4 Post-Rollback Analysis

post_rollback:
  required_actions:
    retrospective:
      participants: ["All affected agents", "Queen coordinator"]
      agenda:
        - "What went wrong?"
        - "Why did it go wrong?"
        - "How to prevent in the future?"
        - "What did we learn?"

      output:
        - "Retrospective document in docs/retrospectives/"
        - "Action items assigned"
        - "Process improvements identified"

    update_plan:
      - "Revise task estimates based on learnings"
      - "Adjust dependencies if needed"
      - "Update quality gates if too strict/lax"
      - "Modify coordination protocol if needed"

    memory_cleanup:
      - "Remove stale memory entries"
      - "Update status to reflect rollback"
      - "Archive failed attempt data (for learning)"

    documentation:
      - "Update CHANGELOG with rollback note"
      - "Document what was attempted and why it failed"
      - "Share learnings with team"

  continuous_improvement:
    metrics_to_track:
      - "Rollback frequency (target: <5% of tasks)"
      - "Time lost to rollbacks"
      - "Common failure patterns"

    adaptation:
      - "Update task templates based on failures"
      - "Improve estimation accuracy"
      - "Enhance early warning systems"
      - "Better resource allocation"

---

7. Success Metrics & KPIs

success_metrics:
  delivery:
    on_time_delivery: ">90% of tasks complete within 110% of estimate"
    phase_completion: "All 5 phases complete within 18 weeks"
    feature_completeness: "100% of Tier 1, 90% of Tier 2 delivered"

  quality:
    code_coverage: ">=85% overall, per-module targets met"
    bug_density: "<5 bugs per 1000 lines of code"
    performance: "All benchmarks meet targets, <5% regressions"
    documentation: ">90% of public APIs documented"

  efficiency:
    agent_utilization: "70-85% (not too low, not burned out)"
    coordination_overhead: "<15% of total effort"
    rework_rate: "<10% of code requires rework"
    rollback_rate: "<5% of tasks rolled back"

  collaboration:
    cross_team_communication: "Measured via memory access patterns"
    conflict_resolution_time: "<24 hours average"
    knowledge_sharing: "All learnings documented in memory"

  performance_benchmarks:
    scaled_dot_product: "<10ms for 1024x1024"
    multi_head: ">1000 QPS (8 heads, 512 seq)"
    flash_memory: "<50% of dense attention"
    wasm_startup: "<100ms"
    napi_marshalling: "<100us for 10KB"

---

8. Emergency Protocols

emergency_procedures:
  critical_bug_in_production:
    severity: "S1 (Critical)"
    response_time: "Immediate"

    procedure:
      1. "Queen coordinator halts all work"
      2. "Assemble emergency response team (3 senior agents)"
      3. "Reproduce bug"
      4. "Hotfix branch created"
      5. "Fix implemented and tested"
      6. "Emergency release (skip some process)"
      7. "Post-mortem within 24 hours"

  agent_unavailable:
    scenario: "Agent fails/is unresponsive"

    response:
      1. "Queen coordinator detects via missed check-in"
      2. "Reassign tasks to available agent"
      3. "Restore agent's context from memory"
      4. "New agent continues work"

  queen_coordinator_unavailable:
    scenario: "Coordinator fails"

    response:
      1. "Senior agent (pre-designated) takes over"
      2. "Restore coordination state from memory"
      3. "Continue operations with minimal disruption"
      4. "New coordinator appointed if needed"

  external_dependency_crisis:
    examples: ["CI down", "crates.io unavailable", "GitHub outage"]

    response:
      1. "Switch to fallback infrastructure if available"
      2. "Cache dependencies locally"
      3. "Continue work on non-dependent tasks"
      4. "Reschedule dependent tasks"

---

9. Timeline Visualization

Week 1-2:   [==== Phase 1: Foundation ====]
             - Project structure
             - Core traits
             - CI/CD setup

Week 3-6:   [============ Phase 2: Core Implementation =============]
             - Scaled dot-product (Week 3)
             - Multi-head (Week 3-4)
             - Geometric (Week 4-5)
             - Sparse (Week 4-6)

Week 7-10:  [=========== Phase 3: Advanced Features ============]
             - Graph attention (Week 7-9)
             - Adaptive attention (Week 7-10)
             - Training utilities (Week 8-9)

Week 11-14: [=========== Phase 4: Platform Bindings ===========]
             - WASM (Week 11-13)
             - NAPI (Week 11-13)
             - CLI (Week 12-13)
             - SDK (Week 13-14)

Week 15-18: [======== Phase 5: Testing & Optimization ========]
             - Unit tests (Week 15)
             - Integration tests (Week 16)
             - Benchmarks (Week 17)
             - Optimization (Week 18)

────────────────────────────────────────────────────────────────
        🎯 Release v1.0.0 (End of Week 18)

---

10. Execution Checklist

Pre-Swarm Initialization

• Review this implementation plan
• Set up Git repository with clean main branch
• Configure CI/CD pipelines
• Install required tools (cargo, wasm-pack, node, etc.)
• Initialize memory coordination system
• Prepare task tracking (GitHub Projects or similar)

Phase-by-Phase Execution

For each phase:

• Initialize swarm with appropriate topology
• Spawn all required agents
• Assign tasks with clear acceptance criteria
• Set up memory coordination keys
• Configure hooks for all agents
• Monitor progress (daily check-ins)
• Run quality gates at checkpoints
• Create checkpoint at phase end
• Hold retrospective
• Update plan based on learnings

Post-Swarm Completion

• All tests passing
• All benchmarks meeting targets
• Documentation complete
• Release notes written
• CHANGELOG updated
• Version tagged
• Publish to crates.io
• Publish to NPM (WASM/NAPI)
• Announce release
• Archive swarm session data

---

Conclusion

This swarm implementation plan provides a comprehensive, phase-by-phase approach to building the ruvector-attention crate. By leveraging hierarchical coordination, memory-based state sharing, and rigorous quality gates, the swarm can deliver a high-quality, performant attention mechanism library in 18 weeks.

Key Success Factors:

1. Clear task decomposition with realistic estimates
2. Effective coordination via memory and hooks
3. Rigorous quality gates at every level
4. Graceful degradation strategy for time pressure
5. Robust rollback mechanisms for failure recovery
6. Continuous learning and adaptation

Next Steps:

1. Review and approve this plan
2. Set up infrastructure (repos, CI/CD, tooling)
3. Initialize swarm for Phase 1
4. Execute systematically, phase by phase
5. Adapt and improve based on actual progress

---

Document Version: 1.0 Last Updated: 2025-11-30 Status: Ready for Execution