LLM-Guided Evolutionary Algorithms for Brain-Inspired Computing
\[G' = M_{LLM}(G, F(G), \nabla F)\]
LLM-guided semantic mutation for evolved architectures
The Kraken Liquid Neural Network combines liquid reservoir computing with LLM-guided evolution for breakthrough performance.
High-dimensional dynamical system with adaptive viscosity, temperature, and turbulence parameters.
Large Language Models guide genome generation and semantic mutation strategies.
Spike-timing-dependent plasticity for biologically plausible weight updates.
Accuracy, generalizability, and complexity jointly optimized.
Watch neuromorphic architectures evolve in real-time with LLM guidance.
Performance on the Abstraction and Reasoning Corpus benchmark.
| Method | Accuracy | Convergence | Improvement |
|---|---|---|---|
| Baseline Genetic Algorithm | 25.0% | 500+ generations | — |
| Standard Liquid State Machine | 28.3% | 350 generations | +13.2% |
| Kraken LNN (No LLM) | 35.7% | 200 generations | +42.8% |
| Kraken LNN + LLM Evolution | 47.3% | 75 generations | +89.2% |
Liquid dynamics with adaptive parameters for neuromorphic computing.
@dataclass class LiquidDynamics: """Liquid dynamics for Kraken LNN""" viscosity: float = 0.1 # Flow resistance temperature: float = 1.0 # Random fluctuations pressure: float = 1.0 # Activation thresholds flow_rate: float = 0.5 # Information propagation turbulence: float = 0.05 # Non-linear dynamics def update_liquid_state(self, input_value): # Calculate liquid flow with LLM-evolved parameters flow = self._calculate_liquid_flow(input_value) turbulent_flow = flow * self.dynamics.viscosity + \ np.random.normal(0, self.dynamics.turbulence) # State update with temperature-scaled activation self.state = np.tanh(self.state / self.dynamics.temperature) return self.state
Deep dive into the theoretical framework, mathematical proofs, and complete experimental results.