LEAD: Breaking the No-Recovery Bottleneck in Long-Horizon Reasoning

Apple researchers have identified a no-recovery bottleneck that occurs when Large Language Models break complex tasks into too many individual steps, leading to an inability to correct errors. By studying controlled algorithmic puzzles, the team demonstrated that extreme decomposition destabilizes reasoning even when high-level strategies are provided. This finding highlights a structural limitation in current model architectures and suggests that balancing step-by-step planning with error recovery is essential for improving long-horizon performance in AI systems.