The LEAD framework addresses the "no-recovery bottleneck" where a few critical errors derail complex LLM tasks. Researchers at Apple found that extreme task decomposition often creates irreversible failure points. By using short-horizon future validation, the method stabilizes execution. This provides a more reliable path for models solving multi-step algorithmic puzzles.