A recent study examined the ability of large language models to follow multiple instructions. The researchers introduced the Constraint Saturation Evaluation (CSE) benchmark, which systematically varies the number of simultaneous constraints (k) and evaluates model performance using a deterministic, rule-based verifier. The results show that the satisfaction rate of individual constraints decays gradually as the number of constraints increases, but the satisfaction rate of all constraints collapses suddenly. The study found that structural constraints are more susceptible to the number of constraints than lexical ones, and constraint failures are nearly independent but exhibit residual coupling due to shared output features. These findings suggest that large language models' reliable instruction following capability breaks down beyond 5-6 simultaneous constraints. Blogger's Review: This study reveals the performance bottleneck of large language models in multi-constraint scenarios, highlighting the need for future research to focus on improving models' compositional constraint satisfaction ability to better adapt to complex application scenarios.