The escalating complexity of tasks assigned to large language models (LLMs) frequently pushes the boundaries of their context windows and operational efficiency. While basic prompt compression, such as simple summarization or truncation, offers initial relief, it often falls short when dealing with intricate, multi-faceted requests. Complex tasks—ranging from synthesizing vast research datasets and performing multi-step reasoning to orchestrating sophisticated Retrieval-Aug
