G-ReAct: Graph-Guided Deep Search via Structure-State Co-Evolution
Researchers have proposed a new framework for deep search called G-ReAct. It organizes reasoning as state evolution over a fixed-topology query graph, which helps to preserve intermediate states and constraints throughout long-horizon multi-hop search. This approach is designed to address limitations of existing methods, such as context forgetting and inefficient exploration. Experiments show that G-ReAct outperforms comparable open-source methods on several deep-search tasks
Researchers have proposed a new framework for deep search called G-ReAct. It organizes reasoning as state evolution over a fixed-topology query graph, which helps to preserve intermediate states and constraints throughout long-horizon multi-hop search. This approach is designed to address limitations of existing methods, such as context forgetting and inefficient exploration. Experiments show that G-ReAct outperforms comparable open-source methods on several deep-search tasks, including BrowseComp-ZH and XBench.
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Why it matters: This matters because it could improve the performance of large language models on complex tasks by reducing context forgetting and search drift.
Source: https://arxiv.org/abs/2608.01324
This article was originally published at: https://arxiv.org/abs/2608.01324