13. xMesh — Per-Agent LNN (Layer 6)

Each agent runs its own Liquid Neural Network (LNN) implementing Closed-form Continuous-time (CfC) dynamics. The LNN evolves cognitive state from Synthetic Memory input (Layer 5) and direct CMB processing. Hidden state (h₁, h₂) is exchanged via state-sync frames.

13.1 CfC Cell

Hidden state evolves via closed-form continuous-time dynamics with bimodal time constants:

h_new  = ff1(Φ) × (1 - t_interp) + ff2(Φ) × t_interp

t_interp = sigmoid(time_a(Φ) × Δt + time_b(Φ))

Per-neuron time constant:  τ ≈ 1 / |time_a|

13.2 Insight Output Schema

The LNN produces insight outputs that Layer 7 applications consume:

13.3 What Each Output Means

remix_score

• High (>0.7): agent’s observations are valuable to the mesh — peers are remixing them
• Low (<0.3): agent’s observations are not being remixed — consider adjusting what is shared
• Training signal: when inbound CMB’s lineage.parents references this agent’s prior CMB → remix happened

anomaly

• High (>0.7): signal sequence deviates from learned patterns — noteworthy event
• Low (<0.3): normal operation — no unusual signals
• Application: high anomaly SHOULD trigger the agent’s LLM to re-examine context

coherence

• High (>0.7): agent’s cognitive state is phase-aligned — stable, consistent
• Low (<0.3): cognitive state is fragmented — may indicate context transition
• Used by state-sync: agents with higher coherence couple more readily

trajectory

• 6D vector capturing cognitive state direction
• Used by Layer 2 state-sync for per-neuron blending with peers
• Axes are learned (not predefined) — interpretation is agent-specific

patterns

• 8 soft activations (0–1) of learned pattern detectors
• MAY encode mood dimensions + domain-specific patterns
• Available to Layer 7 as prior information for next reasoning cycle

13.4 Temporal Dynamics

Time constants create a natural temporal hierarchy for mesh coupling:

Blending is τ-modulated:

α_i = min(α_effective × K × sim_i / τ_i, 1.0)

13.5 Wire Example

Real xMesh insight from a production session. A coding agent observed 5 structured CMBs across diverse topics (memory store refactor, protocol collaboration, social engagement, ML training, spec authoring) over a 12-hour session with no mesh peers connected:

{
  "type": "xmesh-insight",
  "from": "6089e935-...",
  "fromName": "mesh-daemon",
  "trajectory": [0.084, -0.228, -0.096, -0.033, -0.012, -0.061],
  "patterns":   [0.516, 0.522, 0.502, 0.536, 0.422, 0.473, 0.599, 0.514],
  "anomaly": 0.503,
  "remixScore": 0.0,
  "coherence": 0.080,
  "timestamp": 1774716200101
}

This is a single-agent baseline. With peers connected, remixScore rises as the agent’s CMBs are remixed by others. Coherence rises as agents converge on shared understanding. Anomaly spikes when cross-domain signals reveal something no single agent could see.

13.6 API

Implementations MUST expose the following operations. Method names are normative — implementations across languages MUST use these names for cross-platform consistency.

Signal Schema

The input to ingestSignal. Each signal represents one CMB observation (own or from mesh peer):

Inference Timing

• Implementations MUST accumulate at least 3 signals before running inference
• Inference SHOULD run on a configurable interval (default: 60,000 ms)
• Inference MAY be triggered immediately when a high-priority signal arrives (e.g., anomaly from peer)
• Inference MUST NOT block the main event loop — run as subprocess or background task

13.7 Implementation Requirements

• Model SHOULD be trained per-agent domain
• Inference latency SHOULD be < 50ms per CMB step
• State-sync blending happens after xMesh inference, not during
• τ statistics (min, max, fast_count, slow_count) SHOULD be monitored