Invariant Reinforcement Loop

InvariantReinforcementLoop Skill

Overview

Recursive meta-learning that re-encodes its own metrics as input. Maintains complexity homeostasis (K ≈ constant ±10%) across RQNS iterations. Drives cumulative learning without simplification or divergence.

Use When

• Long-running autonomous learning loops
• Verifying that recursive self-improvement is bounded (AI safety)
• Kolmogorov complexity monitoring for cognitive state
• Any system that models its own state (metacognition)

Complexity Homeostasis Rule

Stable-yet-nontrivial dynamics: complexity ≈ constant.

• HOMEOSTATIC — healthy invariant preservation
• DIVERGING — runaway complexity growth (safety alert)
• COLLAPSING — oversimplification, loss of nuance

Core Loop

while running:
    state = get_current_state()
    result = loop.step(state, external_metrics)
    if not result.invariant_preserved:
        trigger_clarification()  # Φ < 0.7 → stop and ask
    re_encode(result)  # feed output back as input

Implementation

from src.rqns.invariant import InvariantReinforcementLoop
loop = InvariantReinforcementLoop(window=50)
result = loop.step(state_vector, {"phi": 0.85, "soc": 0.72})
print(loop.complexity_trend)  # HOMEOSTATIC / DIVERGING / COLLAPSING

Safety Bounds

• η* = 0.03: Never attempt to close the incompleteness gap to zero
• homeostasis_tolerance = 10%: If complexity drifts > 10%, pause and verify
• meta_phi < 0.7: Always request clarification before autonomous action