{"ID":2847229,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.00489","arxiv_id":"2511.00489","title":"ToM: Leveraging Tree-oriented MapReduce for Long-Context Reasoning in Large Language Models","abstract":"Large Language Models (LLMs), constrained by limited context windows, often face significant performance degradation when reasoning over long contexts. To address this, Retrieval-Augmented Generation (RAG) retrieves and reasons over chunks but frequently sacrifices logical coherence due to its reliance on similarity-based rankings. Similarly, divide-and-conquer frameworks (DCF) split documents into small chunks for independent reasoning and aggregation. While effective for local reasoning, DCF struggles to capture long-range dependencies and risks inducing conflicts by processing chunks in isolation. To overcome these limitations, we propose ToM, a novel Tree-oriented MapReduce framework for long-context reasoning. ToM leverages the inherent hierarchical structure of long documents (e.g., main headings and subheadings) by constructing a DocTree through hierarchical semantic parsing and performing bottom-up aggregation. Using a Tree MapReduce approach, ToM enables recursive reasoning: in the Map step, rationales are generated at child nodes; in the Reduce step, these rationales are aggregated across sibling nodes to resolve conflicts or reach consensus at parent nodes. Experimental results on 70B+ LLMs show that ToM significantly outperforms existing divide-and-conquer frameworks and retrieval-augmented generation methods, achieving better logical coherence and long-context reasoning. Our code is available at https://github.com/gjn12-31/ToM .","short_abstract":"Large Language Models (LLMs), constrained by limited context windows, often face significant performance degradation when reasoning over long contexts. To address this, Retrieval-Augmented Generation (RAG) retrieves and reasons over chunks but frequently sacrifices logical coherence due to its reliance on similarity-ba...","url_abs":"https://arxiv.org/abs/2511.00489","url_pdf":"https://arxiv.org/pdf/2511.00489v1","authors":"[\"Jiani Guo\",\"Zuchao Li\",\"Jie Wu\",\"Qianren Wang\",\"Yun Li\",\"Lefei Zhang\",\"Hai Zhao\",\"Yujiu Yang\"]","published":"2025-11-01T10:43:58Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"RAG\",\"Large Language Model\",\"Language Model\"]","has_code":false,"code_links":[{"ID":607499,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2847229,"paper_url":"https://arxiv.org/abs/2511.00489","paper_title":"ToM: Leveraging Tree-oriented MapReduce for Long-Context Reasoning in Large Language Models","repo_url":"https://github.com/gjn12-31/ToM","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}