mesh-cognition 1.1.0

Distributed intelligence through coupled neural networks — semantic and neural coupling modes

mesh-cognition

Autonomous Drift-Bounded Coupling for CfC Neural Networks

Turn any AI agent into a mesh cognition node. The coupling is autonomous — the agent evaluates each peer's hidden state and decides for itself whether, how much, and which neurons to couple.

Install

pip install mesh-cognition

Quick Start

from mesh_cognition import MeshNode

# Create a node matching your CfC model's hidden dimension
node = MeshNode(hidden_dim=64)

# After each CfC inference step — update local state
node.update_local_state(new_h1, new_h2, confidence=0.8)

# Add peer state (from network, another agent, etc.)
node.add_peer("peer-1", peer_h1, peer_h2, confidence=0.9)

# Before next inference — get coupled state
h1, h2 = node.coupled_state()
# Feed h1, h2 into your CfC model as hidden state inputs

# Check what the agent decided
print(node.kuramoto_order_parameter)  # r(t): 0=desync, 1=in sync
print(node.coupling_decisions)        # per-peer: aligned/guarded/rejected

How It Works

The agent evaluates each peer's hidden state via cosine similarity drift:

• Aligned (drift ≤ 0.25): Strong coupling — agent trusts peer
• Guarded (0.25 < drift ≤ 0.5): Cautious coupling — reduced influence
• Rejected (drift > 0.5): Agent rejects peer state entirely

Each hidden dimension is coupled independently. Zero external dependencies.

Links

• Whitepaper
• GitHub
• SYM.BOT

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