10. State Blending

State blending is one step in the Mesh Cognition cycle. The full path: inbound CMBs are evaluated by SVAF (Layer 4) → accepted CMBs are remixed → the agent’s LLM reasons on the remix subgraph via lineage ancestors → Synthetic Memory (Layer 5) encodes derived knowledge into CfC hidden state → the agent’s LNN (Layer 6) evolves cognitive state. That evolution — a node’s own LNN integrating its own admitted remixes — is what “state blending” names.

Per the hidden-state locality invariant (Section 2.7), hidden state (h₁, h₂) never crosses the wire. Blending therefore does not import, average, or overwrite a peer’s hidden vectors. The only thing a peer contributes is the CMBs it emitted; those that SVAF admits (Section 9.2) are remixed and fed through this node’s own LLM and LNN. What a peer shares is its understanding expressed as CMBs, not its hidden state.

Blending is inference-paced — admitted remixes accumulate continuously, but integration only occurs when the local model runs inference. The network’s timing does not drive computation.

10.1 Weighting Peer Influence

When multiple peers are connected, the CMBs each peer has contributed are weighted by how aligned and how recent that peer is, so a closer, more active peer influences this node’s inference more. peer_weight is a bound on admission influence — it caps how much a single admitted CMB’s content (Section 9.2) may shift this node’s local integration input at Layer 6. It applies to each peer’s admitted CMBs, never to any exchanged hidden vector:

peer_weight = (1.0 - drift) × recency

recency     = exp(-temporal_decay × age_seconds)

// peer_weight is a bound on ADMISSION INFLUENCE: how much an admitted
// CMB from that peer may shift this node's Layer 6 integration input.
// It is NOT applied to peer hidden vectors — none are exchanged (§2.7).

Peers with low drift (cognitively aligned) and recent activity contribute more. Stale peers (older than PEER_RETENTION = 300s) are evicted.

10.2 Coupling Strength

The coupling decision from Layer 4 (Section 9.1) sets αeffective, the upper bound on admission influence: how much a single admitted CMB’s content may shift this node’s local integration (the Layer 6 input) during inference. It is not a per-neuron vector blend — there is no exchanged vector to blend (Section 2.7). The coefficient is bounded below 1, so a peer influences but never overrides:

sim_i  = 1 - |local_i - mesh_i| / max(|local_i|, |mesh_i|)
α_i    = α_effective × max(sim_i, 0)
out_i  = (1 - α_i) × local_i + α_i × mesh_i

10.3 τ-Modulated Integration (CfC)

For implementations with CfC models (Layer 6), how strongly admitted influence moves each neuron SHOULD be modulated by that neuron’s own time constant (τ). This is a property of the node’s own LNN — not of any exchanged vector — and creates a natural temporal hierarchy:

α_i = min(α_effective × K / τ_i, 1.0)

K   = coupling rate (default 1.0)
τ_i = neuron i's own time constant (fast → small, slow → large)

10.4 Stability

By design, integration remains a contraction toward the node’s own dynamics for α_effective < 1. Each admission’s influence on the local state is bounded by α_i < 1 (Section 10.3), so every integration step remains a contraction toward the node’s own dynamics — admitted content perturbs the trajectory, it cannot replace it, and the state cannot diverge. No step depends on a shared or global vector; stability is a local property of each node. When peers disconnect, the node smoothly continues on its own admitted history with no discontinuity. The mesh degrades gracefully.

10.5 After Integration

The integrated state becomes the input to the next CfC inference step. The agent’s LNN processes it, evolves cognitive state, and the agent acts. Integration does not produce output directly — it influences the next inference cycle.

10.6 The Mesh Cognition Loop

State blending is one step in a closed loop. Each cycle, the graph grows and every agent understands more than it did before: