Mapping Text to Multiplex Graph: Prompt Compression as Lévy Walk-Guided Graph Pruning
Researchers propose RAGP, a novel prompt compression method that models text as a multiplex graph and uses Lévy walks for efficient pruning. It achieves a 49.3 average score on LongBench at 4x compression, outperforming existing LLM-based methods.
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[Submitted on 4 May 2026]
Title:Mapping Text to Multiplex Graph: Prompt Compression as Lévy Walk-Guided Graph Pruning
View a PDF of the paper titled Mapping Text to Multiplex Graph: Prompt Compression as L\'evy Walk-Guided Graph Pruning, by Yaxin Gao and 9 other authors
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Abstract:Existing prompt compression methods treat text as flat token sequences, failing to capture the distributed nature of important information, which is often spread across multiple locations and connected through both local syntactic dependencies and global semantic relations. Such relational structure is naturally represented as a graph, where tokens or sentences become nodes and their dependencies become edges. To this end, we propose RAGP, which formulates prompt compression as Redundancy-Aware Graph Pruning on a multiplex graph that jointly models fine-grained attention-based dependencies and coarse-grained semantic relations. To efficiently identify non-redundant nodes in this heterogeneous structure (dense local subgraphs and sparse global connections), we employ Levy walks whose heavy-tailed step distribution naturally balances local exploitation with global exploration. Experiments on LongBench show that RAGP achieves an average score of 49.3 under a 4x compression ratio, outperforming existing LLM-based compression methods, such as LongLLMLingua, which attains 48.8 at a 3x compression ratio. Besides, RAGP also surpasses state-of-the-art vision-based text compression paradigms on multiple tasks. The code is available at this https URL.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.01241 [cs.CL]
(or arXiv:2607.01241v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.01241
arXiv-issued DOI via DataCite
Submission history
From: Yaxin Gao [view email] [v1] Mon, 4 May 2026 01:41:31 UTC (663 KB)
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