[2506.16411] When Does Divide and Conquer Work for Long Context LLM? A Noise Decomposition Framework

Computer Science > Computation and Language arXiv:2506.16411 (cs) [Submitted on 19 Jun 2025 (v1), last revised 28 Feb 2026 (this version, v2)] Title:When Does Divide and Conquer Work for Long Context LLM? A Noise Decomposition Framework Authors:Zhen Xu, Shang Zhu, Jue Wang, Junlin Wang, Ben Athiwaratkun, Chi Wang, James Zou, Ce Zhang View a PDF of the paper titled When Does Divide and Conquer Work for Long Context LLM? A Noise Decomposition Framework, by Zhen Xu and 7 other authors View PDF HTML (experimental) Abstract:We investigate the challenge of applying Large Language Models (LLMs) to long texts. We propose a theoretical framework that distinguishes the failure modes of long context tasks into three categories: cross-chunk dependence (task noise), confusion that grows with context size (model noise), and the imperfect integration of partial results (aggregator noise). Under this view, we analyze when it is effective to use multi-agent chunking, i.e., dividing a lengthy sequence into smaller chunks and aggregating the processed results of each chunk. Our experiments on tasks such as retrieval, question answering, and summarization confirm both the theoretical analysis and the conditions that favor multi-agent chunking. By exploring the accelerated decay of model fidelity with input length, we also explain why, for large inputs, a weaker model configured with chunk-based processing can surpass a more advanced model like GPT4o applied in a single shot. Overall, we prese...