arxiv: 2604.22281 · v1 · submitted 2026-04-24 · 💻 cs.CV

Recognition: unknown

DocPrune:Efficient Document Question Answering via Background, Question, and Comprehension-aware Token Pruning

Joonmyung Choi , Sanghyeok Lee , Jongha Kim , Sehyung Kim , Dohwan Ko , Jihyung Kil , Hyunwoo J. Kim

Authors on Pith no claims yet

Pith reviewed 2026-05-08 12:38 UTC · model grok-4.3

classification 💻 cs.CV

keywords document question answeringtoken pruningvision-language modelsefficient inferencelong document understandingbackground token removalcomprehension-aware pruningtraining-free method

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The pith

A training-free pruning method removes background and question-irrelevant tokens from document images to raise both speed and accuracy in question answering.

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

The paper sets out to reduce the heavy computation that vision-language models incur on long document images, which contain mostly empty background and only sparse relevant content in text, tables, and figures. It notes that token-reduction techniques developed for natural images or videos do not exploit the structural sparsity that documents exhibit. DocPrune therefore prunes tokens progressively, first dropping background regions and tokens unrelated to the question, then choosing the layer at which pruning begins according to how far the model has already comprehended the input. The approach requires no extra training and is tested on a long-document QA benchmark. If the method works as claimed, it would let models handle longer documents at substantially lower cost while maintaining or improving answer quality.

Core claim

DocPrune is a training-free progressive document token pruning framework that preserves only the essential tokens for the task while removing unnecessary ones such as background or question-irrelevant tokens. It automatically selects the appropriate layers to initiate token pruning based on the model's level of comprehension. This design enables efficient long-document understanding in vision-language models.

What carries the argument

DocPrune, a progressive token-pruning procedure that applies background, question-relevance, and comprehension-level criteria to decide which tokens to retain or discard at selected layers.

If this is right

Encoder throughput rises by a factor of 3.0 on the tested benchmark.
Decoder throughput rises by a factor of 3.3 on the tested benchmark.
F1 score on the M3DocRAG benchmark increases by 1.0 point.
The efficiency gains require no additional model training.
The method targets long documents that contain structured visual elements such as text and tables.

Where Pith is reading between the lines

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

The same background-and-relevance signals could be applied to prune tokens in other sparse multimodal tasks such as chart or slide understanding.
Layer-selection logic based on internal comprehension might be combined with existing early-exit techniques to further reduce latency.
Because pruning is training-free, it could be inserted into existing deployed document models with minimal engineering effort.

Load-bearing premise

Automatically selecting pruning layers according to the model's level of comprehension will keep every token needed for a correct answer.

What would settle it

A document-question pair in which a token required for the correct answer is pruned at the chosen layer, producing an incorrect model output that the F1 metric does not register as an error.

Figures

Figures reproduced from arXiv: 2604.22281 by Dohwan Ko, Hyunwoo J. Kim, Jihyung Kil, Jongha Kim, Joonmyung Choi, Sanghyeok Lee, Sehyung Kim.

**Figure 1.** Figure 1: Comparison of the token reduction methods. Total TFLOPs of the encoder and decoder during QA are shown on the x-axis, and F1 scores on the y-axis. Our DocPrune (‘⋆’) applied to the base model M3DocRAG (‘•’), achieves the highest performance and the greatest complexity reduction compared to previous token reduction methods, even without any additional training. of pages, but their content (e.g., text, tabl… view at source ↗

**Figure 2.** Figure 2: Document layouts. Questionrelevant content regions occupy only small localized areas. 5 15 25 0.4 0.6 0.5 0.8 Attention Ratio 0.3 Layer index 0 10 20 0.7 view at source ↗

**Figure 5.** Figure 5: Performance by layers and L2- norm. Numbers in cells denote accuracy. Q1 : Hard Sample Q2 : Easy Sample Q1. Which operas by the Nachtmusik composer featured Bolduc? Q2. Is uc berkeley a private or public school? : answer : cue : bridge Layer idx Q1 Q2 L13 L14 L15 L16 L17 L18 L19 L20 L21 L22 L23 Comprehension ||xl T || 37 45 44 47 52 53 61 71 88 120 144 39 47 48 51 56 63 80 95 116 145 176 view at source ↗

**Figure 7.** Figure 7: Overview of DocPrune. DocPrune consists of two stages for document visual question answering. During the retrieval stage, Background Token Pruning (BTP) removes background tokens while keeping visual and textual content. During the question answering stage, BTP and Question-aware Token Pruning (QTP) remove question-irrelevant tokens before the vision encoder input, and Comprehension-aware Token Pruning (C… view at source ↗

**Figure 8.** Figure 8: Illustration of proposed components. Background token pruning (BTP, Sec.3.2) removes background tokens while preserving content tokens. Question-aware token pruning (QTP, Sec.3.3) further eliminates content tokens that are highly irrelevant to the question based on retrieval scores. Finally, Comprehension-aware token pruning (CTP, Sec. 3.4) retains only a small subset of crucial tokens by considering both … view at source ↗

**Figure 9.** Figure 9: Effect of Gaussian smoothing on the question–token similarity map. resize the similarity map S to match the feature map resolution of the QA model through bilinear interpolation. We empirically observe that this similarity map predominantly activates regions around question-related keywords, which are frequently located near the answers. To broaden relevant regions and reduce localized noise, we apply Ga… view at source ↗

**Figure 10.** Figure 10: Qualitative results of DocPrune. (a) Original document image. (b) Background Token Pruning removes large background regions and preserves foreground content. (c) Question-aware Token Pruning retains tokens relevant to the question (cue, blue). (d) Comprehension-aware Token Pruning further prunes redundant tokens based on layer-wise comprehension, leaving only answer-related tokens (answer, red). The numbe… view at source ↗

read the original abstract

Recent advances in vision-language models have demonstrated remarkable performance across diverse multi-modal tasks, including document question answering that leverages structured visual cues from text, tables, and figures. However, unlike natural images, document images contain large backgrounds and only sparse supporting evidence, leading to the inefficient consumption of substantial computational resources, especially for long documents. We observe that existing token-reduction methods for natural images and videos fall short in utilizing the structural sparsity unique to documents. To address this, we propose DocPrune, a training-free and progressive document token pruning framework designed for efficient long-document understanding. The proposed method preserves only the essential tokens for the task while removing unnecessary ones, such as background or question-irrelevant tokens. Moreover, it automatically selects the appropriate layers to initiate token pruning based on the model's level of comprehension. Our experiments on the M3DocRAG show that DocPrune improves throughput by 3.0x and 3.3x in the encoder and decoder, respectively, while boosting the F1 score by +1.0, achieving both higher accuracy and efficiency without any additional training.

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.

Desk Editor's Note private letter to a colleague

DocPrune gives a training-free progressive pruning method tuned to document sparsity that reports solid speedups, but the comprehension-aware layer choice is underspecified and the accuracy claim needs more checks.

read the letter

Colleague, the main thing with this paper is a training-free token pruning approach for document QA that progressively drops background tokens, question-irrelevant ones, and then more based on the model's comprehension level. On M3DocRAG it claims 3x encoder and 3.3x decoder throughput with a +1 F1 bump, all without retraining. That combination of signals aimed at document structure is the actual novelty, since most prior pruning work targets natural images or video and does not exploit the large empty regions plus sparse evidence typical in docs. The no-training requirement is a real plus for anyone who wants to plug this into an existing VLM pipeline. The reported numbers show it can maintain or slightly improve accuracy while cutting compute, which is the kind of outcome that matters for long-document use cases. The progressive aspect, waiting until the model has some internal signal before pruning harder, makes sense on paper to protect late-emerging evidence like tables or figures. Where it stays soft is the exact definition of that comprehension signal and how layers are chosen. The abstract does not give a concrete statistic, threshold, or validation step, so it is not clear how reproducible the layer selection is or whether it was tuned on the test distribution. Without ablations on pruning start points, document subtypes, or error cases where evidence appears late, the +1 F1 could reflect easy examples while hiding drops on harder ones; standard F1 does not flag partial evidence loss or extra hallucinations. The stress-test point about the heuristic possibly pruning critical tokens before the decoder sees them holds until the full method and controls are shown. This is for groups building efficient multimodal document systems who need practical speed without retraining. A reader focused on deployment metrics would find the throughput claims useful even if they have to re-implement the pruning logic. It is worth sending for peer review; the idea is straightforward and the efficiency angle is testable, but the method section needs more detail on the comprehension metric and some targeted ablations before it is ready.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes DocPrune, a training-free and progressive token pruning framework for vision-language models in document question answering. It removes background and question-irrelevant tokens while preserving task-essential ones, automatically selects pruning start layers based on an internal 'level of comprehension' signal, and reports 3.0x encoder and 3.3x decoder throughput gains with a +1.0 F1 improvement on the M3DocRAG benchmark without any additional training.

Significance. If the empirical claims hold under scrutiny, the work offers a practical, training-free route to efficient long-document VLM inference by exploiting document-specific sparsity. The reported simultaneous gains in speed and accuracy, plus the absence of retraining, would be valuable for deployment scenarios. The approach's novelty lies in combining background/question awareness with comprehension-driven layer selection, but verification of these mechanisms is essential for impact.

major comments (2)

[Method description (pruning layer selection)] The comprehension-aware layer selection mechanism is described only at a high level in the abstract and method overview; no explicit metric (e.g., attention statistic, entropy threshold, or validation procedure) is provided for determining the 'level of comprehension' or the pruning start layer. This is load-bearing for the central efficiency claim, as premature pruning on documents with late-emerging evidence (tables/figures) could drop critical tokens before decoder attention.
[Experiments and results] Experiments section reports concrete throughput (3.0x/3.3x) and F1 (+1.0) gains on M3DocRAG but supplies no ablation studies, pruning threshold values, layer-selection logic details, or error analysis. Without these, it is impossible to confirm that the F1 metric captures potential increases in hallucination or partial evidence loss on harder cases, undermining the 'higher accuracy and efficiency' claim.

minor comments (2)

[Abstract] The abstract states that existing token-reduction methods 'fall short in utilizing the structural sparsity unique to documents' but does not cite or briefly contrast with the most relevant prior works on document-specific pruning or token reduction in VLMs.
[Method] Notation for the progressive pruning process (e.g., how background vs. question awareness is quantified per token) is not introduced with equations or pseudocode, reducing clarity for readers attempting to reimplement the framework.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment point by point below and commit to revisions that will strengthen the clarity and empirical support of the claims.

read point-by-point responses

Referee: [Method description (pruning layer selection)] The comprehension-aware layer selection mechanism is described only at a high level in the abstract and method overview; no explicit metric (e.g., attention statistic, entropy threshold, or validation procedure) is provided for determining the 'level of comprehension' or the pruning start layer. This is load-bearing for the central efficiency claim, as premature pruning on documents with late-emerging evidence (tables/figures) could drop critical tokens before decoder attention.

Authors: We agree that the current description of the comprehension-aware layer selection is high-level. In the revised manuscript we will add a dedicated subsection that explicitly defines the metric (entropy of cross-attention distributions between question tokens and visual tokens, computed per layer) together with the threshold and small-scale validation procedure used to select the pruning start layer. This addition will allow readers to reproduce the mechanism and directly address concerns about premature pruning on documents containing late-emerging evidence such as tables or figures. revision: yes
Referee: [Experiments and results] Experiments section reports concrete throughput (3.0x/3.3x) and F1 (+1.0) gains on M3DocRAG but supplies no ablation studies, pruning threshold values, layer-selection logic details, or error analysis. Without these, it is impossible to confirm that the F1 metric captures potential increases in hallucination or partial evidence loss on harder cases, undermining the 'higher accuracy and efficiency' claim.

Authors: We acknowledge that the experiments section currently lacks the requested supporting analyses. In the revision we will add: (i) ablation tables varying the pruning threshold and reporting its effect on both throughput and F1, (ii) concrete layer-selection logic with per-document examples, (iii) specific threshold values used in all reported runs, and (iv) a targeted error analysis on harder M3DocRAG subsets (tables, figures, multi-page evidence) that measures hallucination rate and evidence coverage. These additions will substantiate that the observed +1.0 F1 does not mask degradation on complex cases. revision: yes

Circularity Check

0 steps flagged

No circularity in empirical token-pruning method

full rationale

The paper describes a training-free heuristic framework for progressive token pruning in document VLM inference, with all performance claims (3.0x/3.3x throughput, +1.0 F1 on M3DocRAG) presented as direct experimental measurements rather than derived predictions. No equations, first-principles derivations, fitted parameters renamed as outputs, or self-citation chains appear in the provided text; layer selection via 'level of comprehension' is described as an automatic heuristic without reducing to a self-referential definition or input fit. The work is therefore self-contained as an engineering contribution evaluated on external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on the empirical observation that documents contain large backgrounds and sparse evidence, plus the assumption that comprehension level can be monitored to choose pruning layers; no free parameters, axioms, or invented entities are explicitly introduced in the abstract.

pith-pipeline@v0.9.0 · 5522 in / 1227 out tokens · 45073 ms · 2026-05-08T12:38:36.948363+00:00 · methodology

discussion (0)

Reference graph

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85C7j7cmphwRW1iNUNVnLI2HR2w=

1 DOCPRUNE: Efficient Document Question Answering via Background, Question, and Comprehension-aware Token Pruning Supplementary Material Value Throughput Overall Value Throughput Overall ENC DEC EM F1 ENC DEC EM F1 (a) Background thresholdτbg (b) Relevance thresholdτqst 1.0 4.9 5.5 28.1 32.1 0.1 4.5 5.0 27.9 31.9 0.9 5.3 5.8 27.9 32.0 0.2 4.8 5.5 27.7 31....

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