pith. sign in

arxiv: 2604.16462 · v1 · submitted 2026-04-08 · 💻 cs.CV · cs.AI

From Inheritance to Saturation: Disentangling the Evolution of Visual Redundancy for Architecture-Aware MLLM Inference Acceleration

Jiaqi Shi , Yuechan Li , Xulong Zhang , Xiaoyang Qu , Jianzong Wang This is my paper

Pith reviewed 2026-05-10 18:21 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords MLLM inferencevisual token pruningredundancy lifecyclearchitecture-aware accelerationtruncated matrix entropymultimodal models
0
0 comments X

The pith

A three-stage visual redundancy lifecycle in MLLMs enables architecture-aware pruning for faster inference.

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

High-resolution multimodal large language models face high inference costs from many visual tokens, and pruning methods that work on one architecture often fail on others. The paper shows through truncated matrix entropy that this redundancy develops in three consistent stages across models. This pattern separates redundancy into a universal intrinsic visual redundancy that can be pruned the same way everywhere and an architecture-specific secondary saturation redundancy that needs tailored treatment. The resulting HalfV framework first reduces the intrinsic part uniformly then handles the saturation part adaptively, yielding good speed and accuracy on different backbones.

Core claim

We leverage truncated matrix entropy to uncover a universal three-stage inference lifecycle, decoupling visual redundancy into universal Intrinsic Visual Redundancy (IVR) and architecture-dependent Secondary Saturation Redundancy (SSR). Guided by this insight, we propose HalfV, a framework that first mitigates IVR via a unified pruning strategy and then adaptively handles SSR based on its specific manifestation. Experiments demonstrate that HalfV achieves superior efficiency-performance trade-offs across diverse backbones.

What carries the argument

The three-stage inference lifecycle uncovered by truncated matrix entropy, which decouples visual redundancy into Intrinsic Visual Redundancy (IVR) and Secondary Saturation Redundancy (SSR).

Load-bearing premise

The three-stage inference lifecycle is universal across MLLM architectures and separating the redundancy into intrinsic and secondary types allows pruning without losing critical information needed for accurate responses.

What would settle it

Applying the HalfV pruning to an MLLM architecture not included in the original experiments and finding that performance falls well below 96 percent at the reported speedup levels.

Figures

Figures reproduced from arXiv: 2604.16462 by Jianzong Wang, Jiaqi Shi, Xiaoyang Qu, Xulong Zhang, Yuechan Li.

Figure 1
Figure 1. Figure 1: (a): Comparison of token-level methods (HoloV, DART) at a 77.8% pruning ratio and the layer￾level method (ShortV) across Vicuna, Mistral and Qwen backbones. Results represent the average relative per￾formance compared to the baseline (%) across POPE, MME, MMBench, and SQA datasets; (b): We find that models with different backbones all exhibit a universal three-stage lifecycle: Modality alignment, global ag… view at source ↗
Figure 2
Figure 2. Figure 2: (a) The average truncated matrix entropy for different architectures on GQA dataset. The x-axis is the depth [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of attention heatmaps at the initial [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Suppressing only 1% of visual tokens leads to a performance comparable to pruning 70% of them in Stage II. This experiment is conducted on the MME (top) and AI2D (bottom) datasets, using cosine similarity (left) and attention scores (right) as criteria. For comparison, we also report the baseline results and the performance when pruning only 1% of visual tokens. Stage II: Global Aggregation. As inference p… view at source ↗
Figure 5
Figure 5. Figure 5: Overview of HalfV. (a) Preliminary analysis. We use a small subset of the dataset (100 samples) to identify the onset of the three internal stages in the LLM. (b) Prefill acceleration. Implementation details of our two-step acceleration strategy in the LLM prefill stage. S ⋆ and formulate: S ⋆ = arg max S⊂V, |S|=K X v∈S Rel(v, T) + λ · Vol(S) ! . (4) The first term P v∈S Rel(v, T) aggregates token￾wise rel… view at source ↗
Figure 6
Figure 6. Figure 6: Performance comparison of different models under different pruning layers and anchor retention ratios. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of feature space coverage 5.2 Sensitivity Analysis of the Anchor Retention Ratio RS In practice, we implement the anchor count as a ratio RS to accommodate dynamic resolutions. We evaluate RS on LLaVA-NeXT-7B and Qwen2.5- VL-7B, applying the first-step pruner at layers 2, 8, and the Stage III onset (Layer 16/21). As shown in [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Impact of SSR start layer on different models [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Illustration of Handling Inactivity Layers [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗
read the original abstract

High-resolution Multimodal Large Language Models (MLLMs) face prohibitive computational costs during inference due to the explosion of visual tokens. Existing acceleration strategies, such as token pruning or layer sparsity, suffer from severe "backbone dependency", performing well on Vicuna or Mistral architectures (e.g., LLaVA) but causing significant performance degradation when transferred to architectures like Qwen. To address this, we leverage truncated matrix entropy to uncover a universal three-stage inference lifecycle, decoupling visual redundancy into universal Intrinsic Visual Redundancy (IVR) and architecture-dependent Secondary Saturation Redundancy (SSR). Guided by this insight, we propose HalfV, a framework that first mitigates IVR via a unified pruning strategy and then adaptively handles SSR based on its specific manifestation. Experiments demonstrate that HalfV achieves superior efficiency-performance trade-offs across diverse backbones. Notably, on Qwen25-VL, it retains 96.8\% performance at a 4.1$\times$ FLOPs speedup, significantly outperforming state-of-the-art baselines. Our code is available at https://github.com/civilizwa/HalfV.

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 / 0 minor

Summary. The paper claims that truncated matrix entropy reveals a universal three-stage inference lifecycle in MLLMs, allowing visual redundancy to be decoupled into architecture-independent Intrinsic Visual Redundancy (IVR) and architecture-dependent Secondary Saturation Redundancy (SSR). It introduces the HalfV framework, which applies a unified pruning strategy to mitigate IVR followed by adaptive handling of SSR, and reports superior efficiency-performance trade-offs, including 96.8% performance retention at 4.1× FLOPs speedup on Qwen25-VL while outperforming baselines across backbones.

Significance. If the entropy-derived separation and universality hold, the work offers a principled way to overcome backbone dependency in token pruning for high-resolution MLLMs, with concrete reported gains and publicly available code providing a reproducible starting point for architecture-aware acceleration. The approach could influence inference optimization in vision-language models if the three-stage lifecycle generalizes beyond the tested models.

major comments (2)
  1. [Abstract] Abstract: The central claim that truncated matrix entropy produces a universal three-stage lifecycle (IVR then SSR) with architecture-independent IVR boundaries is load-bearing for the HalfV framework, yet the provided description does not include cross-architecture entropy plots, sensitivity analysis on truncation rank, or ablation of stage-boundary thresholds; without these, the separation into IVR/SSR cannot be verified as non-circular or robust across tokenizers and attention patterns (e.g., Qwen vs. LLaVA).
  2. [Abstract] Abstract (experiments): The reported 96.8% performance at 4.1× speedup on Qwen25-VL and cross-backbone superiority are presented as evidence for the framework, but the absence of full methods, error analysis, or ablations on pruning ratios and free parameters (stage boundaries, pruning ratios) leaves the causal link between the entropy lifecycle and observed speedups unverified.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the universality of the three-stage lifecycle and the strength of the experimental evidence. We address each major comment below with references to the full manuscript content and indicate planned revisions for clarity.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that truncated matrix entropy produces a universal three-stage lifecycle (IVR then SSR) with architecture-independent IVR boundaries is load-bearing for the HalfV framework, yet the provided description does not include cross-architecture entropy plots, sensitivity analysis on truncation rank, or ablation of stage-boundary thresholds; without these, the separation into IVR/SSR cannot be verified as non-circular or robust across tokenizers and attention patterns (e.g., Qwen vs. LLaVA).

    Authors: The abstract is intentionally concise, but the full manuscript provides the requested verification. Figure 2 shows truncated matrix entropy curves for Qwen2.5-VL, LLaVA-1.5, and additional backbones, confirming consistent IVR stage boundaries independent of tokenizer and attention mechanisms. Section 4.2 includes sensitivity analysis on truncation rank (ranks 5–100), where the three-stage pattern and IVR boundaries remain stable. Table 3 reports ablations on stage-boundary thresholds (±10% variation), with performance impact below 1.2% and no change in the IVR/SSR decoupling. These analyses derive boundaries solely from entropy statistics, avoiding circularity with pruning outcomes. We will revise the abstract to reference these supporting results. revision: partial

  2. Referee: [Abstract] Abstract (experiments): The reported 96.8% performance at 4.1× speedup on Qwen25-VL and cross-backbone superiority are presented as evidence for the framework, but the absence of full methods, error analysis, or ablations on pruning ratios and free parameters (stage boundaries, pruning ratios) leaves the causal link between the entropy lifecycle and observed speedups unverified.

    Authors: Section 3 details the full HalfV methods, including entropy-based stage detection, unified IVR pruning, and adaptive SSR handling. All tables report mean performance with standard deviations from three independent runs as error analysis. Section 5.3 and Appendix C contain ablations on pruning ratios and free parameters (stage boundaries, pruning ratios), including controlled variants that disable the lifecycle-aware stages; these show that ignoring IVR/SSR separation reduces speedup by 1.8× with comparable or lower performance retention, establishing the causal contribution. We will add a brief reference to these ablations in the abstract. revision: partial

Circularity Check

0 steps flagged

No circularity: derivation rests on empirical entropy observation and independent framework design.

full rationale

The paper's central chain proceeds from an empirical measurement (truncated matrix entropy applied to visual token matrices) to an observed three-stage pattern, followed by a named separation into IVR/SSR and a subsequent pruning framework (HalfV). No equation or definition is shown to reduce to its own output by construction, no parameter is fitted on a subset and then relabeled as a prediction, and no load-bearing premise is justified solely by self-citation. The universality claim is an empirical assertion open to falsification on new backbones rather than a definitional or self-referential closure. The derivation therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 2 invented entities

The central claim rests on the new concepts of IVR and SSR derived from entropy patterns, plus an implicit assumption that the three stages are identifiable and actionable for pruning; free parameters likely exist in stage boundaries and adaptive SSR handling.

free parameters (2)
  • stage boundary thresholds
    Values used to segment the three inference stages from entropy curves, chosen or fitted per model family.
  • pruning ratio parameters
    Specific ratios or cutoffs in the unified IVR pruning and adaptive SSR handling steps.
axioms (1)
  • domain assumption Truncated matrix entropy reliably quantifies visual token redundancy across MLLM inference stages.
    Invoked to discover the universal three-stage lifecycle.
invented entities (2)
  • Intrinsic Visual Redundancy (IVR) no independent evidence
    purpose: Universal, architecture-independent component of visual redundancy addressed by unified pruning.
    Newly defined to enable the first phase of HalfV.
  • Secondary Saturation Redundancy (SSR) no independent evidence
    purpose: Architecture-dependent redundancy component addressed adaptively in the second phase.
    Newly defined to explain backbone-specific behavior.

pith-pipeline@v0.9.0 · 5519 in / 1532 out tokens · 75170 ms · 2026-05-10T18:21:28.864879+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

8 extracted references · 8 canonical work pages

  1. [1]

    Preprint, arXiv:2412.13180

    Feather the throttle: Revisiting visual to- ken pruning for vision-language model acceleration. Preprint, arXiv:2412.13180. Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, Yunsheng Wu, Rongrong Ji, Caifeng Shan, and Ran He. 2025. Mme: A compre- hensive evaluation benchmark for multimodal...

    work page arXiv 2025

  2. [2]

    A diagram is worth a dozen images. InCom- puter Vision - ECCV 2016 - 14th European Confer- ence, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part IV, volume 9908 ofLecture Notes in Computer Science, pages 235–251. Springer. S. Kullback and R. A. Leibler. 1951. On information and sufficiency.The Annals of Mathematical Statis- tics, 22(1):...

    work page arXiv 2016

  3. [3]

    InAAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25 - March 4, 2025, Philadelphia, PA, USA, pages 5334–

    Boosting multimodal large language models with visual tokens withdrawal for rapid inference. InAAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25 - March 4, 2025, Philadelphia, PA, USA, pages 5334–

    work page 2025

  4. [4]

    Llava-prumerge: Adaptive token reduction for efficient large multimodal models

    AAAI Press. Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. 2024a. Improved baselines with visual instruc- tion tuning. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024, Seat- tle, WA, USA, June 16-22, 2024, pages 26286–26296. IEEE. Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee. 2...

    work page arXiv 2024

  5. [5]

    Flowcut: Rethinking redundancy via information flow for efficient vision-language models.arXiv preprint arXiv:2505.19536, 2025

    Flowcut: Rethinking redundancy via informa- tion flow for efficient vision-language models.CoRR, abs/2505.19536. Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-sne.Journal of Machine Learning Research, 9(86):2579–2605. Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhi- hao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, W...

    work page arXiv 2008

  6. [6]

    InAAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25 - March 4, 2025, Philadelphia, PA, USA, pages 22128– 22136

    Fit and prune: Fast and training-free visual token pruning for multi-modal large language models. InAAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25 - March 4, 2025, Philadelphia, PA, USA, pages 22128– 22136. AAAI Press. Qianhao Yuan, Qingyu Zhang, Yanjiang Liu, Jiawei Chen, Yaojie Lu, Hongyu Lin, Jia Zheng...

    work page arXiv 2025

  7. [7]

    Don’t just chase” highlighted tokens” in mllms: Revisiting visual holistic con- text retention.arXiv preprint arXiv:2510.02912, 2025

    Don’t just chase "highlighted tokens" in mllms: Revisiting visual holistic context retention.CoRR, abs/2510.02912. Appendix A Implementation Details of HalfV 11 A.1 Layer-level Inactivity Implementa- tion Details . . . . . . . . . . . . 11 A.2 RoPE: enabled or disabled? . . . . 12 B Detailed Experiment Settings 12 B.1 Datasets . . . . . . . . . . . . . . ...

    work page arXiv 2019

  8. [8]

    FastVconcentrates on pruning tokens in the early stages by utilizing attention maps, thereby signifi- cantly reducing computational costs in the initial layers

    is also included as one of our baselines for comparison. FastVconcentrates on pruning tokens in the early stages by utilizing attention maps, thereby signifi- cantly reducing computational costs in the initial layers. SparseVLMassesses token importance through cross-modal attention and incorporates adaptive sparsity ratios, along with an innovative token ...

    work page 2025