Latent-Latency Skill

Trit: 0 (ERGODIC - mediates space ↔ time)
Bundle: core
Status: ✅ New

The Fundamental Duality

LATENT (Space)          ↔          LATENCY (Time)
     ↓                                    ↓
Compression                            Speed
     ↓                                    ↓
Representation                        Response
     ↓                                    ↓
dim(z)                               τ_mix

Core Theorem: Good latent representations minimize latency.

t_response ∝ 1 / compression_ratio(z)

Spectral Gap Bridge

The spectral gap (λ₁ - λ₂) connects both domains:

Domain	Spectral Gap Role
Latent	Separation of clusters in representation space
Latency	Mixing time τ_mix = O(log n / gap)

From Ramanujan graphs (optimal expanders):

gap ≥ d - 2√(d-1)    [Alon-Boppana bound]
τ_mix = O(log n)     [Logarithmic mixing]

Mathematical Foundation

Latent Space Dynamics

# Encoder: Observable → Latent
z = encode(x)           # dim(z) << dim(x)

# Decoder: Latent → Reconstructed  
x̂ = decode(z)

# Bidirectional loss
L = ||x - x̂||² + β·KL(q(z|x) || p(z))

Latency Dynamics

# Fokker-Planck: Distribution evolution
∂p/∂t = ∇·(∇L(θ)·p) + T∆p

# Mixing time from Hessian
τ_mix ≈ 1 / λ_min(H)

# Gibbs equilibrium
p∞(θ) ∝ exp(-L(θ)/T)

The Bridge Equation

τ_latency = f(dim_latent, spectral_gap, temperature)

Specifically:
τ_response = (dim(z) / gap) × log(1/ε)

Where:
- dim(z) = latent dimension
- gap = spectral gap of computation graph
- ε = target accuracy

MCP Energy-Latency Tradeoff

From MCP_OPTIMAL_TRANSITIONS.md:

MCP Server	Latency	Latent Cost	Energy
`gay`	~10ms	0.1KB context	LOW
`tree-sitter`	~50ms	1KB context	LOW
`exa`	~1s	3KB context	HIGH
`firecrawl`	~2s	10KB context	HIGH

Optimal triad: gay → tree-sitter → marginalia (560ms, 5 energy)

Worlding Skill Integration

From worlding_skill_omniglot_entropy.py:

class BidirectionalCharacterLearner:
    def __init__(self, char_dim: int = 28, latent_dim: int = 64):
        self.char_dim = char_dim
        self.latent_dim = latent_dim  # Compression ratio: 784 → 64
    
    def encode_character(self, image: np.ndarray) -> np.ndarray:
        """READ: Image → Latent Code (learn what the character means)"""
        # Latency: O(dim_latent)
        pass
    
    def generate_character(self, latent_code: np.ndarray) -> np.ndarray:
        """WRITE: Latent Code → Image (learn how to express the character)"""
        # Latency: O(dim_output)
        pass

Compression: 784 → 64 = 12.25× compression
Expected Latency Reduction: ~12× for downstream tasks

Fokker-Planck Convergence

Training latency depends on reaching Gibbs equilibrium:

Stopped Early:  t < τ_mix  →  Poor latent representation
Fully Converged: t > τ_mix  →  Optimal latent representation
                         ↓
                   Minimal inference latency

From fokker-planck-analyzer:

def check_convergence(trajectory, temperature):
    # Mixing time from loss landscape geometry
    τ_mix = 1 / λ_min(Hessian(loss))
    
    # Check if training exceeded mixing time
    if training_steps > τ_mix:
        return "CONVERGED: Good latent representation"
    else:
        return f"EARLY STOP: Need {τ_mix - training_steps} more steps"

GF(3) Decomposition

Skill	Trit	Role
`fokker-planck-analyzer`	-1	Verifies convergence (latency)
`latent-latency`	0	Mediates space ↔ time
`compression-progress`	+1	Generates compressed representations

Conservation: (-1) + (0) + (+1) = 0 ✓

Practical Applications

1. Optimize Inference Latency

def optimize_latent_for_latency(model, target_latency_ms):
    """
    Find optimal latent dimension for target latency.
    
    Relationship: latency ∝ dim(z) / spectral_gap
    """
    current_dim = model.latent_dim
    current_latency = measure_latency(model)
    
    # Target dimension
    target_dim = int(current_dim * (target_latency_ms / current_latency))
    
    # Retrain with smaller latent space
    return retrain_model(model, latent_dim=target_dim)

2. Predict Mixing Time

def predict_mixing_time_from_latent(latent_structure):
    """
    Estimate training latency from latent space properties.
    """
    # Spectral gap of latent similarity graph
    gap = spectral_gap(latent_similarity_matrix(latent_structure))
    
    # Mixing time bound
    n = latent_structure.n_samples
    τ_mix = np.log(n) / gap
    
    return τ_mix

3. Ramanujan-Optimal Routing

def route_with_ramanujan(nodes, message):
    """
    Route through network with optimal latency.
    
    Ramanujan graphs achieve t_mix = O(log n).
    """
    # Build routing graph with Ramanujan property
    G = build_lps_graph(nodes, degree=7)  # (7+1)-regular
    
    assert spectral_gap(G) >= 7 - 2*np.sqrt(6), "Not Ramanujan!"
    
    # Route via non-backtracking walk
    path = non_backtracking_path(G, source, target)
    
    # Expected latency: O(log n) hops
    return path

Detection Latency SLA

From security applications:

Detection latency = O(log N) / gap

For Ramanujan (gap = 1/4):
  N = 1000 nodes → detection in ~37ms
  N = 1M nodes → detection in ~74ms

Commands

# Analyze latent-latency tradeoff
just latent-latency-analyze model.pt

# Optimize for target latency
just latent-optimize --target-ms=100

# Measure spectral gap of latent space
just latent-spectral-gap embeddings.npy

# Predict mixing time
just predict-mixing-time --hessian=H.npy

# Route with Ramanujan optimality
just ramanujan-route --nodes=1000

DuckDB Schema

CREATE TABLE latent_latency_metrics (
    model_id VARCHAR PRIMARY KEY,
    latent_dim INT,
    spectral_gap FLOAT,
    mixing_time_estimate FLOAT,
    inference_latency_ms FLOAT,
    compression_ratio FLOAT,
    is_converged BOOLEAN,
    created_at TIMESTAMP DEFAULT NOW()
);

-- Query: find optimal models
SELECT model_id, latent_dim, inference_latency_ms
FROM latent_latency_metrics
WHERE is_converged = true
ORDER BY inference_latency_ms ASC
LIMIT 10;

Triads

fokker-planck-analyzer (-1) ⊗ latent-latency (0) ⊗ compression-progress (+1) = 0 ✓
ramanujan-expander (-1) ⊗ latent-latency (0) ⊗ agent-o-rama (+1) = 0 ✓
spi-parallel-verify (-1) ⊗ latent-latency (0) ⊗ gay-mcp (+1) = 0 ✓

References

Fokker-Planck equation for neural network training
Ramanujan graphs and optimal expanders (Lubotzky-Phillips-Sarnak)
Variational autoencoders and latent space geometry
MCP optimal transitions (plurigrid/asi)

Scientific Skill Interleaving

This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:

Graph Theory

networkx [○] via bicomodule
- Universal graph hub

Bibliography References

general: 734 citations in bib.duckdb

Cat# Integration

This skill maps to Cat# = Comod(P) as a bicomodule in the equipment structure:

Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826

GF(3) Naturality

The skill participates in triads satisfying:

(-1) + (0) + (+1) ≡ 0 (mod 3)

This ensures compositional coherence in the Cat# equipment structure.

latent-latency