Memory is the foundation of intelligence
Every insight, every decision begins with preserved context, captured and maintained by MAVEN’s dynamic memory systems — implemented in software.
Conceptual Overview
Memory in MAVEN is more than storage. It is dynamic, persistent, and structured, forming the foundation for all downstream modules: Augmentation, Vision, Emergence, and Nous. All memory is realized through deployable software algorithms that track, store, and transform information in real time.
Discrete-Time Memory Process
Memory updates according to:
h_t = M(h_{t-1}, o_t)
Where h_t is the current memory state, h_{t-1} is the previous state, o_t is the observation, and M is the memory operator implemented in software.
Finite-Dimensional Implementation
In practice, Memory can be implemented as a matrix-vector computation:
h_t = tanh(W_M * h_{t-1} + U_M * o_t + b_M)
– W_M: weight matrix for previous memory
– U_M: weight matrix for new observation
– b_M: bias term
– tanh: stabilizes values
Software Implementation
The MAVEN Memory module is fully implemented in software. Example in Python:
import numpy as np
class MemoryModule:
def __init__(self, state_dim, input_dim):
self.W_M = np.random.randn(state_dim, state_dim) * 0.1
self.U_M = np.random.randn(state_dim, input_dim) * 0.1
self.b_M = np.zeros((state_dim, 1))
self.h_t = np.zeros((state_dim, 1))
def step(self, o_t):
self.h_t = np.tanh(self.W_M @ self.h_t + self.U_M @ o_t + self.b_M)
return self.h_t
Each new observation o_t updates the memory h_t, forming a continuously evolving state that powers the MAVEN pipeline.
The Power of Memory
Memory transforms the past into actionable intelligence. Every insight and every decision MAVEN makes is grounded in what it remembers — through software designed to learn, preserve, and evolve.