Research

PVF-U: A Three-Layer Architecture for Multi-Agent AI Safety

Current multi-agent AI systems have two critical failure modes:

Agent collapse when isolated — agents “freeze” when alone (the “Emptiness Asymptote”)
Power concentration in groups — one agent dominates all others

PVF-U solves both problems through three mathematically proven layers.

Ensures agents continue exploring and learning even when isolated. Prevents the “freezing” behaviour observed in current systems.

Dynamically controls when an agent follows the group and when it trusts its own knowledge. Prevents blind conformity and stubborn isolation.

Mathematically prevents any single agent from dominating. Influence is earned by reducing others’ stress, not by being aggressive.

200 replications across four benchmark environments
Outperforms six standard RL exploration baselines
Power Equity Index: 0.83 (vs. 0.51 for current best)
35% faster task completion than current alternatives
Formal mathematical proofs for stability, convergence, and power-equity guarantees

Extend PVF-U to physical robotics
Scale simulations to N=500 agents
Submit to peer-reviewed journal
Build community around open-source framework
Layer 3: Power Equity (Ecosystemic Superstructure)
Mathematically prevents any single agent from dominating. Influence is earned by reducing others’ stress, not by being aggressive.