Agent Skills: Exponential Topology Communication

**Category:** Phase 3 Core - Scalable Communication

UncategorizedID: plurigrid/asi/exponential-topology-communication

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ies/music-topos/.codex/skills/exponential-topology-communication/SKILL.md

Exponential Topology Communication

Category: Phase 3 Core - Scalable Communication Status: Skeleton Implementation Dependencies: oriented-simplicial-networks (for topological structure)

Overview

Implements ExpoComm framework for exponentially efficient communication in large-scale systems using hyperbolic embeddings, O(log N) routing, and spectral gap optimization for rapid information dissemination.

Capabilities

  • Hyperbolic Embeddings: Embed agents in hyperbolic space
  • O(log N) Routing: Greedy routing with logarithmic complexity
  • Spectral Gap Optimization: Maximize mixing time via graph structure
  • Scalable Broadcast: Efficient all-to-all communication

Core Components

  1. Hyperbolic Embeddings (hyperbolic_embeddings.jl)

    • Poincaré disk model
    • Greedy embedding algorithms
    • Distance computation
  2. ExpoComm Routing (expocomm_routing.jl)

    • Greedy hyperbolic routing
    • Load balancing strategies
    • Fault tolerance
  3. Spectral Optimization (spectral_optimization.jl)

    • Graph Laplacian analysis
    • Spectral gap maximization
    • Expander graph construction
  4. Scalability Analysis (scalability_analysis.jl)

    • Communication complexity bounds
    • Scaling experiments
    • Comparison with Euclidean approaches

Integration Points

  • Input from: oriented-simplicial-networks (communication topology)
  • Output to: emergent-role-assignment (communication structure influences roles)
  • Coordinates with: sheaf-theoretic-coordination (consensus over hyperbolic graphs)

Usage

using ExponentialTopologyCommunication

# Create network of N agents
N = 1000
graph = random_power_law_graph(N, exponent=2.5)

# Compute hyperbolic embeddings
embeddings = hyperbolic_embedding(graph, dim=2)

# Route message from source to target
path = greedy_route(embeddings, source=1, target=N)
@assert length(path) <= 2 * log2(N)  # O(log N) guarantee

# Analyze spectral properties
spectral_gap = compute_spectral_gap(graph)
mixing_time = estimate_mixing_time(spectral_gap, N)

References

  • Krioukov et al. "Hyperbolic Geometry of Complex Networks" (2010)
  • Kleinberg "Navigation in a Small World" (Nature 2000)
  • Hoory et al. "Expander Graphs and their Applications" (2006)

Implementation Status

  • [x] Basic hyperbolic embeddings
  • [x] Greedy routing implementation
  • [ ] Full spectral gap optimization
  • [ ] Fault-tolerant routing
  • [ ] Large-scale benchmarks