arxiv: 2604.06912 · v1 · submitted 2026-04-08 · 💻 cs.CV · cs.AI

Recognition: 2 theorem links

· Lean Theorem

Q-Zoom: Query-Aware Adaptive Perception for Efficient Multimodal Large Language Models

Yuheng Shi , Xiaohuan Pei , Linfeng Wen , Minjing Dong , Chang Xu

Authors on Pith no claims yet

Pith reviewed 2026-05-10 18:42 UTC · model grok-4.3

classification 💻 cs.CV cs.AI

keywords Q-Zoommultimodal large language modelsadaptive perceptionhigh-resolution processingregion of interestdynamic gatingefficient inferencequery-aware

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The pith

Q-Zoom lets multimodal LLMs process only query-relevant high-resolution image regions to speed up inference.

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

Multimodal large language models face efficiency limits because they always feed full high-resolution images into quadratic attention, creating redundant tokens even for queries that need detail only in small areas. Q-Zoom counters this with a coarse-to-fine pipeline: a lightweight gating network skips high-resolution entirely when global low-res features are enough, while a self-distilled proposal network finds the exact task-relevant region when fine detail matters. Training uses consistency labels for the gate and fully self-supervised distillation for the proposer, followed by alignment to merge local and global features. On Qwen2.5-VL-7B the method delivers 2.52 times faster inference on document and OCR tasks and 4.39 times faster in high-resolution settings while matching or beating baseline accuracy, and the gains carry over to other model families.

Core claim

Q-Zoom is a query-aware adaptive high-resolution perception framework for MLLMs that operates in an efficient coarse-to-fine manner. A lightweight Dynamic Gating Network safely bypasses high-resolution processing when coarse global features suffice. For queries that need fine-grained perception, a Self-Distilled Region Proposal Network precisely localizes the task-relevant Region-of-Interest directly from intermediate feature spaces. These modules are optimized with a consistency-aware generation strategy for routing labels and a fully self-supervised distillation paradigm for the proposer. A continuous spatio-temporal alignment scheme then fuses the dense local RoI with the coarse global布局.

What carries the argument

The Dynamic Gating Network for deciding high-resolution use plus the Self-Distilled Region Proposal Network for localizing query-relevant RoIs, trained without external labels and fused via spatio-temporal alignment.

If this is right

Delivers 2.52 times faster inference on Document and OCR benchmarks while matching the baseline peak accuracy.
Achieves 4.39 times speedup in high-resolution scenarios with matching accuracy.
Can exceed baseline accuracy by 1.1 percent on document tasks and 8.1 percent on high-resolution tasks when configured for maximum fidelity.
The same speed and accuracy benefits transfer directly to Qwen3-VL, LLaVA, and RL-based thinking-with-image models without architecture changes.

Where Pith is reading between the lines

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

The same query-driven gating idea could be tested on video or 3D inputs where only certain frames or viewpoints need high detail.
Self-supervised distillation of the region proposer may allow similar adaptive modules to be added to existing models with minimal new labeled data.
Combining Q-Zoom style routing with token compression techniques might further reduce memory use for very large images or long contexts.

Load-bearing premise

The gating and proposal networks can correctly decide when high-resolution is needed and accurately locate the relevant image region without adding meaningful overhead or localization mistakes that would hurt final performance.

What would settle it

A controlled test on a fine-grained document query where the gating network routes to low-resolution only, producing a clear accuracy drop relative to the always-high-resolution baseline on the same model.

Figures

Figures reproduced from arXiv: 2604.06912 by Chang Xu, Linfeng Wen, Minjing Dong, Xiaohuan Pei, Yuheng Shi.

**Figure 1.** Figure 1: Comparison of adaptive high-resolution perception paradigms. Training-free methods rely on handcrafted contrastive rules, requiring multiple redundant prefilling passes. RL-based methods use the LLM to auto-regressively generate code or coordinates to find the RoI. Our Q-Zoom framework operates directly on the intermediate feature space during a single prefilling pass, yielding superior efficiency. a Dynam… view at source ↗

**Figure 2.** Figure 2: Overview of the proposed Adaptive High-Resolution Perception Framework. (a) The framework derives robust supervisory signals through consistency-aware generation to train a lightweight gating module. (b) During inference, the gate dynamically evaluates the textual query. It routes simpler queries for direct, accelerated generation using coarse features, while triggering the SD-RPN for complex queries to ex… view at source ↗

**Figure 3.** Figure 3: Overview of the conditional Region-of-Interest extraction pipeline. When triggered by the dynamic gating module, the SD-RPN (top) leverages shared intermediate features from the frozen backbone to efficiently generate a dense spatial heatmap. During the training phase (bottom), the network is optimized through a self-distillation paradigm, utilizing denoised cross-modal attention maps from the base MLLM as… view at source ↗

**Figure 4.** Figure 4: Overview of our pseudo-label generation pipeline. Raw attention maps from the MLLM are denoised by removing sink tokens, followed by a tri-state label assignment that isolates high-confidence foreground (FG) and background (BG) tokens while ignoring ambiguous intermediate regions. Layer index is omitted for brevity [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 6.** Figure 6: Overview of the Spatio-Temporal Alignment and Targeted Post-SFT pipeline. The vision encoder and projector are omitted for visual brevity. where i ∈ {0, . . . , H′ − 1} and j ∈ {0, . . . , W′ − 1}. This formulation guarantees that the dense RoI tokens remain explicitly grounded within their original global coordinates. Targeted Post-Supervised Fine-Tuning (Post-SFT). Even with rigorous positional alignmen… view at source ↗

**Figure 7.** Figure 7: Qualitative comparisons on challenging examples from TextVQA (left) and V* Bench (right). These examples highlight visually demanding scenarios where the target evidence is small or obscured. The baseline Qwen2.5-VL-7B suffers from resolution compression, whereas our Q-Zoom framework successfully leverages the SD-RPN to predict highly accurate RoI heatmaps, cropping the necessary fine-grained details to ge… view at source ↗

**Figure 8.** Figure 8: Accuracy vs. Efficiency Trade-offs. We evaluate the Qwen2.5-VL-7B baseline against Q-Zoom by sweeping the maximum visual token limit in a image from 256 to 4,096. Our framework establishes a dominant Pareto frontier on both (a) Document & OCR and (b) High-Resolution benchmark categories. By adaptively localizing RoIs, Q-Zoom surpasses the peak accuracy of the brute-force 4,096-token baseline while reducing… view at source ↗

**Figure 9.** Figure 9: Ablation of the Consistency-aware Training Sample Generation upon Qwen2.5-VL 7B. (Top) Training loss curves of the dynamic gating network. (Bottom) The Pareto front illustrating the trade-off between perception accuracy and inference efficiency (No-RoI Ratio). ble VIII ablates the pseudo-label foreground (τf g) and background (τbg) thresholds. Setting the two thresholds to the same value (e.g., τf g = τbg… view at source ↗

read the original abstract

MLLMs require high-resolution visual inputs for fine-grained tasks like document understanding and dense scene perception. However, current global resolution scaling paradigms indiscriminately flood the quadratic self-attention mechanism with visually redundant tokens, severely bottlenecking inference throughput while ignoring spatial sparsity and query intent. To overcome this, we propose Q-Zoom, a query-aware adaptive high-resolution perception framework that operates in an efficient coarse-to-fine manner. First, a lightweight Dynamic Gating Network safely bypasses high-resolution processing when coarse global features suffice. Second, for queries demanding fine-grained perception, a Self-Distilled Region Proposal Network (SD-RPN) precisely localizes the task-relevant Region-of-Interest (RoI) directly from intermediate feature spaces. To optimize these modules efficiently, the gating network uses a consistency-aware generation strategy to derive deterministic routing labels, while the SD-RPN employs a fully self-supervised distillation paradigm. A continuous spatio-temporal alignment scheme and targeted fine-tuning then seamlessly fuse the dense local RoI with the coarse global layout. Extensive experiments demonstrate that Q-Zoom establishes a dominant Pareto frontier. Using Qwen2.5-VL-7B as a primary testbed, Q-Zoom accelerates inference by 2.52 times on Document & OCR benchmarks and 4.39 times in High-Resolution scenarios while matching the baseline's peak accuracy. Furthermore, when configured for maximum perceptual fidelity, Q-Zoom surpasses the baseline's peak performance by 1.1% and 8.1% on these respective benchmarks. These robust improvements transfer seamlessly to Qwen3-VL, LLaVA, and emerging RL-based thinking-with-image models. Project page is available at https://yuhengsss.github.io/Q-Zoom/.

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

Q-Zoom gives a workable query-aware way to skip or localize high-res processing in MLLMs and reports clear speedups, but the self-distilled proposals need to prove they avoid missing critical details on fine-grained tasks.

read the letter

Q-Zoom tackles the cost of high-resolution inputs in MLLMs by adding a lightweight gate that skips extra detail when coarse features are enough and a self-distilled region proposal network that picks the relevant patch from intermediate features when they are not. The reported 2.52x speedup on document and OCR benchmarks and 4.39x on high-resolution cases, while matching or beating baseline accuracy, is the practical result if the modules hold up. The self-supervised training for both the gate and the proposal, plus the alignment step that fuses the local RoI back with global context, keeps the overhead low and avoids extra labels. This is more targeted than blanket resolution scaling and transfers across Qwen, LLaVA, and some RL-based models, which is useful for deployment work. The core idea is straightforward and directly attacks the quadratic attention bottleneck on spatially sparse queries. The main soft spot is exactly the localization reliability of the SD-RPN. If the proposals from coarse features systematically miss small text or objects, the later fusion cannot recover the lost information and the accuracy claims break. The stress-test concern lands here; without strong ablations on proposal error rates or failure cases on OCR data, the speedups could trade off hidden accuracy in ways the abstract numbers do not reveal. The consistency labels for the gate also assume the coarse features already carry good routing signals, which may not always be true. This paper is for researchers and engineers who need throughput gains on document or scene tasks without retraining the whole backbone. A reader looking for modular efficiency tricks would get concrete ideas to try. It deserves peer review because the problem is real, the method is new enough to test, and the empirical claims are specific enough to verify or refute with the experiments.

Referee Report

3 major / 2 minor

Summary. The paper introduces Q-Zoom, a query-aware adaptive perception framework for efficient MLLMs. It employs a lightweight Dynamic Gating Network to bypass high-resolution processing when coarse features suffice, and a Self-Distilled Region Proposal Network (SD-RPN) to localize task-relevant RoIs from intermediate features using self-supervised distillation. A continuous spatio-temporal alignment fuses the local RoI with global layout. On Qwen2.5-VL-7B, it reports 2.52× inference speedup on Document & OCR benchmarks and 4.39× in high-resolution scenarios while matching baseline peak accuracy; a maximum-fidelity configuration exceeds baseline by 1.1% and 8.1% respectively. Gains transfer to Qwen3-VL, LLaVA, and RL-based models.

Significance. If the empirical claims hold under rigorous validation, Q-Zoom would meaningfully advance efficient high-resolution perception in MLLMs by exploiting query intent and spatial sparsity rather than uniform token scaling. The fully self-supervised training of both the gating network (via consistency-aware labels) and SD-RPN (via distillation) avoids extra supervision costs, which is a practical strength. Transferability across model families further supports potential impact on deployment of vision-language systems.

major comments (3)

[Methods (SD-RPN description)] Methods section on SD-RPN: the claim that the self-distilled proposals 'precisely localize' task-relevant RoIs from coarse intermediate features lacks supporting localization metrics (e.g., IoU or recall on ground-truth regions) or failure-case analysis on fine-grained OCR/Document benchmarks. If proposals systematically miss small text or objects, the subsequent fusion cannot recover detail, directly undermining the 'match or surpass peak accuracy' result.
[Methods (Dynamic Gating Network)] Dynamic Gating Network subsection: the consistency-aware generation of deterministic routing labels assumes coarse global features already encode reliable high-res vs. low-res signals. No ablation or accuracy breakdown is provided for gating decisions (e.g., false-negative rate when high-res is needed), so it is unclear whether the reported 2.52×/4.39× speedups trade off hidden accuracy on edge cases.
[Experiments] Experiments and results: the headline speedups and accuracy numbers are presented without full ablation tables isolating the contribution of gating vs. SD-RPN vs. fusion, nor details on benchmark splits, hyperparameter selection, or statistical significance. This makes it difficult to rule out selection bias or post-hoc tuning as noted in the soundness assessment.

minor comments (2)

[Abstract] The abstract states that Q-Zoom 'establishes a dominant Pareto frontier' but no corresponding figure or table is referenced; a Pareto plot comparing throughput vs. accuracy against baselines would strengthen the claim.
[Methods (fusion)] Notation for the continuous spatio-temporal alignment scheme is introduced without an equation or diagram; a concise formulation would clarify how local RoI features are merged with global layout.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We have carefully reviewed each major comment and provide point-by-point responses below. We will make the indicated revisions to strengthen the paper's rigor, clarity, and empirical support.

read point-by-point responses

Referee: Methods section on SD-RPN: the claim that the self-distilled proposals 'precisely localize' task-relevant RoIs from coarse intermediate features lacks supporting localization metrics (e.g., IoU or recall on ground-truth regions) or failure-case analysis on fine-grained OCR/Document benchmarks. If proposals systematically miss small text or objects, the subsequent fusion cannot recover detail, directly undermining the 'match or surpass peak accuracy' result.

Authors: We acknowledge that the current manuscript lacks explicit quantitative localization metrics such as IoU or recall for the SD-RPN outputs. This stems from the fully self-supervised distillation training paradigm, which does not use ground-truth region annotations. To address the concern, we will add qualitative visualizations of RoI proposals, failure-case analysis on OCR and document benchmarks, and proxy evaluations (e.g., downstream accuracy sensitivity to proposal quality thresholds). We will also revise the Methods section to better contextualize the 'precise localization' claim as being validated by end-to-end task performance rather than direct spatial metrics. These changes will be included in the revised version. revision: yes
Referee: Dynamic Gating Network subsection: the consistency-aware generation of deterministic routing labels assumes coarse global features already encode reliable high-res vs. low-res signals. No ablation or accuracy breakdown is provided for gating decisions (e.g., false-negative rate when high-res is needed), so it is unclear whether the reported 2.52×/4.39× speedups trade off hidden accuracy on edge cases.

Authors: We agree that additional analysis of the gating decisions would improve transparency. In the revised manuscript, we will include a dedicated ablation that reports accuracy breakdowns for gated vs. forced high-resolution paths, along with the false-negative rate (instances where high-resolution processing is required but the gate selects the low-resolution path). This will demonstrate the reliability of the consistency-aware label generation and confirm that the reported speedups do not mask accuracy losses on edge cases. Examples of gating behavior on challenging inputs will also be added. revision: yes
Referee: Experiments and results: the headline speedups and accuracy numbers are presented without full ablation tables isolating the contribution of gating vs. SD-RPN vs. fusion, nor details on benchmark splits, hyperparameter selection, or statistical significance. This makes it difficult to rule out selection bias or post-hoc tuning as noted in the soundness assessment.

Authors: We will expand the Experiments section with full ablation tables that isolate the individual and combined contributions of the Dynamic Gating Network, SD-RPN, and spatio-temporal fusion components. We will also add details on the benchmark dataset splits, the hyperparameter selection procedure (including validation strategies), and statistical significance measures such as means and standard deviations across multiple runs. These additions will help address potential concerns about selection bias or post-hoc tuning. revision: yes

Circularity Check

0 steps flagged

No circularity: performance claims are empirical results on external benchmarks

full rationale

The paper proposes Q-Zoom with a Dynamic Gating Network and Self-Distilled Region Proposal Network trained via consistency-aware labeling and self-supervised distillation, then fuses outputs and reports speedups (2.52× / 4.39×) plus accuracy matching or exceeding baselines on Document & OCR and high-resolution benchmarks. These metrics are obtained from direct experimental evaluation on held-out test sets using Qwen2.5-VL-7B and other models; no equations, fitted parameters, or self-citations reduce the reported throughput or accuracy figures to the inputs by construction. The derivation chain consists of standard training and inference procedures whose outputs are independently measurable against external data.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based solely on the abstract, no explicit free parameters, axioms, or invented entities are described in sufficient detail to list; the new modules are introduced conceptually without quantified assumptions or external validation handles.

pith-pipeline@v0.9.0 · 5631 in / 1280 out tokens · 50834 ms · 2026-05-10T18:42:38.856391+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

lightweight Dynamic Gating Network ... Self-Distilled Region Proposal Network (SD-RPN) ... consistency-aware generation strategy ... self-supervised distillation paradigm ... continuous spatio-temporal positional encoding
IndisputableMonolith/Foundation/DimensionForcing.lean alexander_duality_circle_linking unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Q-Zoom accelerates inference by 2.52 times on Document & OCR benchmarks ... 4.39 times in High-Resolution scenarios

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

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