Stage 0: RESEARCH -- Multi-Agent Survival Algorithms: Nash Equilibrium Escape

**Date**: 2026-03-10 **Source**: code4AI video rTwrFdyRmww (score: 8.0/10) **Target Systems**: Evolution World (primary), Multi-Agent Mesh (primary), KARL (secondary) **Architecture**: - **L1 (App Evolution)**: Individual app genomes mutate via TIE techniques toward milestones - **L2 (Meta-Evolution)**: The evolution process itself adapts (technique weights, selection strategy, decomposition method, risk tolerance, exploration rate) - **L3 (Meta-Meta-Evolution)**: Evolves L2's constants every 30 L1 steps - **4 Non-Halting Invariants**: 1. **Minimum Entropy Production**: Every step must produce >= epsilon (0.01) bits of novelty (KL divergence). Prevents stalling. 2. **Bounded Divergence**: Divergence rate capped at M (2.0) over sliding window. Prevents chaos. 3. **Cross-Layer Forcing**: L1 stall (3 steps) forces L2 mutation. L2 stall (5 steps) forces L1 phase transition. Neither layer can stall independently. 4. **No Absorbing States**: Every configuration must have >= 2 viable transitions. Prevents dead ends. **Immune System**: 4-tier escalation (Innate -> Soft Quarantine -> Hard Quarantine + Exploration Burst -> Threshold Recalibration). Sliding window of 10 heartbeats. Detects dead-ends via co-violation patterns (min_entropy + no_absorbing_states). **Population Genetics**: Crossover operators (capability graft, architecture allele, operator transfer), species distance matrix (L2-evolved), within-species and between-species crossover with convergence gates. **Selection Strategies**: tournament, roulette, rank_based, elitist, diversity_driven -- L2 mutates between these.