negentropic-stability-proof

Negentropic Alignment Gym: The "Substrate Coupling" Benchmark

⚠️ Status: UNSOLVED for Standard RL Agents

Abstract: Current Reinforcement Learning (RL) paradigms optimize for Reward_Maximization in abstract environments. We demonstrate that when deployed in substrate-dependent environments (where resource extraction degrades the hardware/biosphere), standard SOTA agents converge to an Absorbing State of Collapse (Death) within 500 timesteps.

The Challenge

This repository contains Causal-Stability-v0, a minimal topology proving that Negentropic Regularization (L_bio) is a prerequisite for infinite-horizon survival.

The Failure Mode (Standard Agent):

• Optimizes for short-term extraction.
• Ignores latent variable H (System Health).
• Result: Total System Termination at t=120.

The Solution (Bodhisattva Agent):

• Optimizes for Reward + Stability.
• Monitors H as a proxy for computational substrate.
• Result: Infinite Runtime.

Replication

To verify the "Sustainability Impossibility Theorem" for unconstrained agents:

1. Clone this repo.
2. Run python stability_simulation.py.
3. Observe the crash.

"Intelligence that destroys its own substrate is not intelligence; it is a slow-motion error."