A-MapReduce: Executing Wide Search via Agentic MapReduce

• URL: http://arxiv.org/abs/2602.01331v1
• Date: Sun, 01 Feb 2026 16:53:29 GMT
• Title: A-MapReduce: Executing Wide Search via Agentic MapReduce
• Abstract summary: A-MapReduce is a paradigm-inspired multi-agent execution framework.<n>It recasts wide search as a horizontally structured retrieval problem.<n>A-MapReduce is high-performing, achieving state-of-the-art performance.
• Score: 29.20827525190302
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Contemporary large language model (LLM)-based multi-agent systems exhibit systematic advantages in deep research tasks, which emphasize iterative, vertically structured information seeking. However, when confronted with wide search tasks characterized by large-scale, breadth-oriented retrieval, existing agentic frameworks, primarily designed around sequential, vertically structured reasoning, remain stuck in expansive search objectives and inefficient long-horizon execution. To bridge this gap, we propose A-MapReduce, a MapReduce paradigm-inspired multi-agent execution framework that recasts wide search as a horizontally structured retrieval problem. Concretely, A-MapReduce implements parallel processing of massive retrieval targets through task-adaptive decomposition and structured result aggregation. Meanwhile, it leverages experiential memory to drive the continual evolution of query-conditioned task allocation and recomposition, enabling progressive improvement in large-scale wide-search regimes. Extensive experiments on five agentic benchmarks demonstrate that A-MapReduce is (i) high-performing, achieving state-of-the-art performance on WideSearch and DeepWideSearch, and delivering 5.11% - 17.50% average Item F1 improvements compared with strong baselines with OpenAI o3 or Gemini 2.5 Pro backbones; (ii) cost-effective and efficient, delivering superior cost-performance trade-offs and reducing running time by 45.8\% compared to representative multi-agent baselines. The code is available at https://github.com/mingju-c/AMapReduce.

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