Event Definition and Dimensional Entanglement for Composite Event Recognition

Event definition governs the learnability and predictability of machine-learning tasks. We replace loosely defined, quasi-random categories—such as “career events” and “health events”—with operational, data-driven “statistical anomaly events,” and introduce a genuinely multidimensional composite target, the “trade-off crisis,” characterized by the near co-occurrence of a career breakthrough with a health downturn. We evaluate an entanglement-based multi-task model (MTEN) against standard transformer baselines on simulated benchmarks within a fully reproducible Windows environment. While baselines fit single-dimension strong signals, MTEN markedly improves cross-dimensional composite recognition (test-set AUCs: y12 = 0.6440 vs. 0.3583; y3 = 0.7345 vs. 0.3385). We formalize a “define before modeling” principle, detail the computational pipeline and key parameters for constructing composite events, and analyze the learnability of trade-off mechanisms in complex systems via structural analogies to entanglement, discussing implications for model-selection strategies.