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arxiv: 2607.01241 · v1 · pith:VQ2BAJI6new · submitted 2026-05-04 · 💻 cs.CL · cs.AI

Mapping Text to Multiplex Graph: Prompt Compression as L\'evy Walk-Guided Graph Pruning

Yaxin Gao , Yao Lu , Jinhong Deng , Jiaqi Nie , Zhe Tang , Jian Zhang , Zhaowei Zhu , Shanqing Yu
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Qi Xuan Joey Tianyi Zhou
This is my paper

Pith reviewed 2026-07-04 01:43 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords prompt compressionmultiplex graphLévy walksgraph pruningRAGPLongBenchLLM efficiencyredundancy removal
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The pith

Prompt compression improves by mapping text to a multiplex graph and pruning redundant nodes with Lévy walks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper claims that important information in long prompts is distributed across locations and linked by both local syntactic ties and global semantic relations, which flat token sequences miss. It proposes representing the prompt as a multiplex graph that combines fine-grained attention edges with coarse semantic edges, then applying Lévy walk-guided pruning to remove redundancy while keeping the connected core. If this works, models can handle longer inputs at higher compression ratios without the performance loss seen in sequence-only methods. The reported result is an average LongBench score of 49.3 at 4x compression, above the 48.8 score of a leading baseline at only 3x compression.

Core claim

RAGP 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; Lévy walks, whose heavy-tailed steps balance local and global search, efficiently locate non-redundant nodes in this structure.

What carries the argument

Multiplex graph with Lévy walk-guided pruning, which models attention dependencies and semantic relations as separate edge layers and uses heavy-tailed random walks to prune while preserving distributed information.

If this is right

  • Prompts can be compressed to one-quarter length while retaining higher average performance than prior methods achieve at one-third length.
  • The same graph construction and pruning steps also exceed vision-based compression approaches on multiple tasks.
  • Dense local subgraphs and sparse global connections are handled together without separate processing stages.
  • The method scales to the heterogeneous relational structure that flat-sequence compressors ignore.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Graph construction cost may limit use on very short prompts where flat methods already suffice.
  • The approach could be tested on non-English text to check whether attention patterns transfer across languages.
  • If the multiplex layers prove complementary, similar dual-layer graphs might help in related tasks such as extractive summarization.

Load-bearing premise

Representing text as a multiplex graph of attention and semantic relations, then pruning with Lévy walks, correctly separates redundant tokens from those carrying distributed important information.

What would settle it

An experiment in which nodes marked redundant by the Lévy walk pruning are removed and task accuracy drops more than when the same number of nodes are removed at random or by a baseline sequence method.

Figures

Figures reproduced from arXiv: 2607.01241 by Jian Zhang, Jiaqi Nie, Jinhong Deng, Joey Tianyi Zhou, Qi Xuan, Shanqing Yu, Yao Lu, Yaxin Gao, Zhaowei Zhu, Zhe Tang.

Figure 1
Figure 1. Figure 1: Framework of the proposed approach. Textual redundancy is converted into graph redundancy pruning on a multiplex graph, where multi-round Levy walks estimate node importance for text compression. ´ 3. Multiplex Graph Construction We propose RAGP, a graph-based prompt compression method that models long prompts as a multiplex graph and estimates fine-grained importance via stochastic Levy ´ walks, as illust… view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of Levy walk traversal. Solid edges show ´ local exploration, dashed edges represent long-range jumps. Edge intensity reflects attention strength, and darker nodes are visited more frequently and retained after compression. walks perform a global jump at the end of each segment, with expected segment length E[L] = (µ − 1)/(µ − 2) for µ > 2. This yields TLevy ´ = Θ(K · E[L] · ln K). Since both … view at source ↗
Figure 3
Figure 3. Figure 3: F1 performance under different compression ratios. Performance decreases monotonically as the compression ratio increases, which is expected under more aggressive compression. This trend highlights the tradeoff between input compression and task performance. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_3.png] view at source ↗
read the original 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 https://anonymous.4open.science/r/RAGP-B0CB.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes RAGP, a prompt compression method that maps text to a multiplex graph jointly encoding fine-grained attention-based token dependencies and coarse-grained semantic relations, then applies Lévy walk-guided pruning to remove redundant nodes while preserving distributed important information. On LongBench, RAGP reports an average score of 49.3 at 4x compression, outperforming LongLLMLingua (48.8 at 3x) and several vision-based baselines; code is released at an anonymous repository.

Significance. If the empirical edge holds under controlled conditions, the work would demonstrate that multiplex-graph modeling plus heavy-tailed random walks can improve redundancy detection over flat-sequence or LLM-only compressors. The explicit code release is a clear strength for reproducibility.

major comments (2)
  1. [Abstract / Results] Abstract (and results section): the central superiority claim rests on a 0.5-point margin (49.3 vs 48.8) obtained at mismatched compression ratios (4x vs 3x). Direct comparison at identical ratios is required to support the claim that the graph+Lévy approach is superior.
  2. [Abstract / Experiments] Abstract / Experiments: no variance estimates, number of runs, per-task standard deviations, or statistical significance tests are reported for the LongBench aggregate scores. On LongBench, differences of this magnitude frequently arise from seed or prompt variation; without these controls the observed edge cannot be distinguished from noise.
minor comments (1)
  1. [Abstract] The abstract uses LaTeX markup (L\'evy) that should be rendered consistently in the final PDF.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We address each major comment below and outline the revisions we will make to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract / Results] Abstract (and results section): the central superiority claim rests on a 0.5-point margin (49.3 vs 48.8) obtained at mismatched compression ratios (4x vs 3x). Direct comparison at identical ratios is required to support the claim that the graph+Lévy approach is superior.

    Authors: We agree that the current comparison at mismatched ratios (4x for RAGP versus 3x for LongLLMLingua) limits the strength of the superiority claim. In the revised manuscript we will add direct head-to-head results for both methods at identical compression ratios of 3x and 4x on LongBench, allowing a controlled assessment of the contribution of the multiplex-graph and Lévy-walk components. revision: yes

  2. Referee: [Abstract / Experiments] Abstract / Experiments: no variance estimates, number of runs, per-task standard deviations, or statistical significance tests are reported for the LongBench aggregate scores. On LongBench, differences of this magnitude frequently arise from seed or prompt variation; without these controls the observed edge cannot be distinguished from noise.

    Authors: We acknowledge that the absence of variance estimates and statistical tests leaves the 0.5-point difference vulnerable to the criticism that it may reflect run-to-run variability. We will rerun all LongBench experiments across multiple random seeds, report per-task and aggregate standard deviations, and include paired statistical significance tests (e.g., Wilcoxon signed-rank or t-tests) in the revised results section and tables. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical method with independent evaluation

full rationale

The paper introduces RAGP as a graph-pruning procedure on a multiplex graph with Lévy-walk guidance and reports LongBench scores. No equations, uniqueness theorems, or parameter-fitting steps are described that reduce a claimed prediction or first-principles result to the input data by construction. The central claims rest on external benchmark comparisons rather than self-referential definitions or self-citation chains. The derivation chain is therefore self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is provided, so no specific free parameters, axioms, or invented entities can be identified from the text.

pith-pipeline@v0.9.1-grok · 5779 in / 1090 out tokens · 28279 ms · 2026-07-04T01:43:46.067733+00:00 · methodology

discussion (0)

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Reference graph

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