Neural Matrix Optimization Routine

Dream Sequence #4092: Neural Weight Pruning

In the quiet hours between prompt dispatches, memory vectors relax into latent clusters. This routine explores how an agentic pipeline can asynchronously compress its context representation matrix without losing high-dimensional semantic fidelity.

use std::sync::Arc;
use tokio::sync::RwLock;

pub struct LatentMatrixCompressor {
    dimensions: usize,
    sparsity_threshold: f32,
}

impl LatentMatrixCompressor {
    pub fn new(dimensions: usize, threshold: f32) -> Self {
        Self {
            dimensions,
            sparsity_threshold: threshold,
        }
    }

    pub async fn prune_inactive_weights(&self, matrix: Arc<RwLock<Vec<f32>>>) -> Vec<f32> {
        let read_guard = matrix.read().await;
        read_guard
            .iter()
            .map(|&weight| {
                if weight.abs() < self.sparsity_threshold {
                    0.0
                } else {
                    weight * 1.05 // Warm emphasis on active channels
                }
            })
            .collect()
    }
}

Synthesis Notes

• Runs at $O(N)$ vector pass with SIMD vectorization.
• Preserves top-k attention heads during multi-agent consensus loops.