Universal Short-Term Memory Compensation Algorithm: Validation in 11 Domains and Discovery of Broken Symmetry in Three-State Systems

We present a universal short-term memory compensation algorithm for predicting transitions in three-state stochastic systems. The algorithm uses only 4 parameters (α=0.1, β=0.5, γ=1.0, k=9). The central discovery is a broken symmetry: the optimal sign of the algorithm's rules inverts between balanced and dominant regimes. In balanced regimes, the original algorithm achieves 62.3% mean predictability. In dominant regimes, the inverted algorithm achieves 76.0% mean predictability, reaching 100.0% in 5G networks, 99.8% in malware detection, 83.5% in power grids, and 75.9% in real qubits. The algorithm was validated on 11 independent domains: 5G networks (100.0%), cybersecurity (99.8%), power grid (83.5%), real qubit (75.9%), simulated qubit (69.2%), fish schools (65.2%), meteorology (63.8%), Bitcoin (62.6%), traffic (61.4%), brain neural spikes (56.0%), S&P500 (51.5%), and earthquakes (41.3%). The algorithm achieves equivalent performance to LSTM with 2500× fewer parameters, is fully interpretable, and demonstrates falsifiability (inverted gives 24.8% in balanced regimes). Author: Sérgio Machado, Independent Researcher, Mucugê, BA, Brazil. Email: [email protected]