Plan, divide, and conquer: How weak models excel at long context tasks

Introduces a 'Divide and Conquer' framework for long-context LLM tasks, enabling smaller models to outperform larger single-shot models. The framework addresses 'Model Noise' (superlinear confusion), 'Task Noise' (cross-chunk dependency), and 'Aggregator Noise' (manager failure). Engineering benefits include reduced cost (using cheaper models), faster processing (parallel workers), and easier tuning (predictable noise curves). The framework is best suited for tasks with moderate cross-chunk dependency, like QA and summarization, and less so for tasks requiring subtle long-range connections.