Executor-Tyrant-Framework/Condensate

Condensate — Do the Same, or More, With Less

A living memory manager that uses neural substrate topology and continuous field dynamics to dynamically condense runtime memory usage.

Try it: Enter a prompt and see which model layers are HOT (needed for this input) vs COLD (condensable). The predictor learns access patterns from causal observation and pre-stages data before it's needed.

How It Works

1. 1.Membrane — Hooks into PyTorch model forward passes, records which layers activate per input
2. 2.Graph Builder — Discovers clusters (proto-hyperedges), causal chains, and hot/cold patterns from access logs
3. 3.Predictor — Predicts next memory access from learned causal topology (98.8% accuracy on inference workloads)
4. 4.Condenser — Compresses cold regions, pages to disk, pre-promotes on prediction

Key Results (PoC)

Architecture

The production version uses:

• —NeuroGraph SNN for causal spike propagation (temporal prediction)
• —Lenia/Flow-Lenia continuous field dynamics (thermal gradient management)
• —Rust core with Python bindings (cache-line aligned, software prefetch)
• —Erasure coding for fault-tolerant distributed storage

This demo proves the principle with a Python prototype.

E-T Systems / NeuroGraph Foundation AGPL-3.0