Collaborative Multi-Mode Pruning for Vision-Language Models

Donghao Wang; Jiaxin Chen; Yunhong Wang; Zimeng Wu

arxiv: 2604.02956 · v1 · submitted 2026-04-03 · 💻 cs.CV

Collaborative Multi-Mode Pruning for Vision-Language Models

Zimeng Wu , Yunhong Wang , Donghao Wang , Jiaxin Chen This is my paper

Pith reviewed 2026-05-13 20:57 UTC · model grok-4.3

classification 💻 cs.CV

keywords vision-language modelsmodel pruningparameter pruningtoken pruningcollaborative importancemulti-mode pruningtransformer compression

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The pith

Jointly pruning parameters and tokens in vision-language models preserves accuracy better at high compression rates.

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

Vision-language models demand too much computation for many devices. Single-mode pruning of either parameters or tokens causes large performance losses once compression becomes aggressive. The method instead prunes both modes together by tracking how each change alters the importance of the other and by selecting the cheaper mode at each step while avoiding local traps. Experiments show the joint approach keeps task performance higher than existing methods when most of the model is removed.

Core claim

CoMP performs joint parameter and token pruning for VLMs by first computing a Collaborative Importance Metric that folds token significance into parameter scores and reduces the effect of already-pruned parameters on token scores, then applying a Multi-Mode Pruning Strategy that breaks the process into stages, ranks modes by estimated pruning cost, and shifts adaptively while blending historical costs with random exploration to reach a stable outcome.

What carries the argument

Collaborative Importance Metric that incorporates distinct token significance into parameter importance scores while mitigating pruned-parameter effects on token scores, embedded inside the Multi-Mode Pruning Strategy that sequences stages and chooses the lowest-cost mode.

Load-bearing premise

The collaborative metric correctly quantifies interference between parameters and tokens without adding estimation bias or requiring per-model retuning.

What would settle it

On a standard vision-language model such as CLIP or LLaVA, applying the joint method at a 60 percent pruning ratio produces equal or lower accuracy on visual question answering than the strongest single-mode baseline.

read the original abstract

Vision-Language Models (VLMs) have advanced rapidly within the unified Transformer architecture, yet their deployment on resource-constrained devices remains challenging due to high computational complexity. While pruning has emerged as an effective technique for compressing VLMs, existing approaches predominantly focus on a single mode by pruning either parameters or tokens, neglecting fully exploring the inherent redundancy in each mode, which leads to substantial performance degradation at high pruning ratios. To address the above limitations, we propose Collaborative Multi-Mode Pruning (CoMP), a novel framework tailored for VLMs by performing joint parameter and token pruning. Specifically, we first design a Collaborative Importance Metric (CIM) that investigates the mutual interference between the coupled parameters and tokens. It incorporates distinct significance of tokens into the computation of parameter importance scores, while simultaneously mitigating the affect of pruned parameters on token importance scores. Moreover, we develop a Multi-Mode Pruning Strategy (MPS) that decomposes the overall pruning process into a sequence of pruning stages, while in each stage we estimate the priory of different pruning modes based on their pruning cost and adaptively shift to the optimal one. Additionally, MPS integrates the historical cost and random exploration, in order to achieve a stable pruning process and avoid local optimum. Extensive experiments across various vision-language tasks and models demonstrate that our method effectively promotes the performance under high pruning ratios by comparing to the state-of-the-art approaches. The source code is available at https://github.com/Wuzimeng/CoMP.git.

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

CoMP adds a joint parameter-token importance metric and cost-adaptive mode switching for VLM pruning, but the abstract supplies no numbers or ablations so the claimed gains at high ratios remain unverified.

read the letter

The paper's main move is to prune parameters and tokens together in VLMs rather than one at a time. CIM mixes token significance into the parameter scores and subtracts the effect of already-pruned parameters from the token scores. MPS then runs the process in stages, picks the cheaper or more promising mode each time, and mixes in historical costs plus random exploration to keep the schedule stable. That joint metric and the adaptive scheduler are the concrete additions beyond the single-mode baselines cited in the abstract. The motivation is clear: single-mode pruning degrades too much once you push past moderate ratios, and VLMs carry redundancy in both weights and tokens, so a coupled approach is worth trying. Releasing the code is also useful for anyone who wants to test the implementation directly. The soft spots sit mostly in the evidence. The abstract states that the method improves performance at high pruning ratios over SOTA, yet it gives no tables, no exact metrics, no model sizes, and no ablation numbers. Without those, it is impossible to judge whether the reported edge comes from the collaboration itself or from the MPS scheduler. The stress-test concern about possible ranking bias in CIM when many parameters and tokens are removed at once is reasonable to check; the description does not include an error bound or a derivation showing that the mutual-interference adjustment preserves order. If the full paper has no such check, the central claim rests on an untested modeling assumption. This work is aimed at people already doing compression for multimodal models who need something that runs on limited hardware. A reader looking for a practical joint-pruning recipe could extract the CIM formula and the MPS logic and try them, provided the experiments later show the gains are real. I would send it to peer review. The problem is relevant, the algorithmic construction is new enough to be worth checking, and the code release lowers the barrier for verification even if the current draft needs tighter experimental reporting.

Referee Report

2 major / 2 minor

Summary. The paper proposes Collaborative Multi-Mode Pruning (CoMP) for Vision-Language Models, performing joint parameter and token pruning. It introduces a Collaborative Importance Metric (CIM) that adds token significance to parameter importance scores while subtracting the effects of pruned parameters from token scores, and a Multi-Mode Pruning Strategy (MPS) that decomposes pruning into stages, estimates mode priority from cost, and uses historical cost plus random exploration to select the optimal mode adaptively. The central claim is that extensive experiments show CoMP outperforms SOTA methods at high pruning ratios across VL tasks and models.

Significance. If the performance claims are substantiated, the work addresses a practical gap in VLM compression by jointly exploiting redundancy in both modes rather than single-mode pruning. The open-source code link supports reproducibility. However, the current manuscript provides no quantitative evidence, making it impossible to evaluate whether the collaborative adjustments yield genuine gains or artifacts.

major comments (2)

[Abstract] Abstract: the assertion of superior performance versus SOTA at high pruning ratios is unsupported by any tables, exact metrics (e.g., accuracy drops, FLOPs), baseline details, or ablation numbers; this is load-bearing for the central experimental claim.
[CIM definition] CIM definition (likely §3.1): the token-to-parameter adjustment and pruned-parameter mitigation step lack a derivation or error bound showing that ranking fidelity is preserved when >50% of tokens/parameters are removed simultaneously; without this, the reported gains could be artifacts of the MPS scheduler rather than true collaboration.

minor comments (2)

[Abstract] Abstract: 'priory' should be 'priority'; 'affect' should be 'effect'.
[MPS] MPS description: clarify the exact mechanism for estimating 'priory' from cost in each stage and how historical cost is aggregated.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and will revise the paper to improve clarity and support for the central claims.

read point-by-point responses

Referee: [Abstract] Abstract: the assertion of superior performance versus SOTA at high pruning ratios is unsupported by any tables, exact metrics (e.g., accuracy drops, FLOPs), baseline details, or ablation numbers; this is load-bearing for the central experimental claim.

Authors: We agree that the abstract would be strengthened by including specific quantitative highlights. The full manuscript contains detailed experimental results in Section 4, including tables with accuracy metrics, FLOPs reductions, and comparisons against SOTA baselines across multiple VL tasks and models at pruning ratios exceeding 50%. In the revision we will update the abstract to reference key numbers (e.g., retained accuracy and relative FLOPs savings) while keeping it concise, and we will ensure all tables and ablation studies are explicitly cross-referenced from the abstract. revision: yes
Referee: [CIM definition] CIM definition (likely §3.1): the token-to-parameter adjustment and pruned-parameter mitigation step lack a derivation or error bound showing that ranking fidelity is preserved when >50% of tokens/parameters are removed simultaneously; without this, the reported gains could be artifacts of the MPS scheduler rather than true collaboration.

Authors: We acknowledge the value of a more formal justification. In the revised §3.1 we will add a derivation of the Collaborative Importance Metric that shows how the mutual adjustments (token significance into parameter scores and pruned-parameter mitigation into token scores) preserve relative ranking order under simultaneous high-ratio removal. We will also include an ablation isolating CIM from the MPS scheduler to demonstrate that the observed gains are attributable to the collaborative formulation rather than scheduling alone. While we do not claim a strict theoretical error bound, the added analysis will clarify the metric's design rationale and empirical robustness. revision: partial

Circularity Check

0 steps flagged

No circularity: CIM and MPS are independent algorithmic definitions

full rationale

The paper introduces CIM as an explicit new metric that adds token significance to parameter scores and subtracts pruned-parameter effects from token scores, and MPS as a staged scheduler using cost-based priority, historical cost, and random exploration. These are presented as constructions whose definitions stand alone; they do not reduce to quantities fitted from the paper's results, prior self-citations, or renamed known patterns. Experiments compare performance but do not retroactively define the metrics. No load-bearing self-citation chains or uniqueness theorems from the same authors are invoked in the provided derivation steps.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The framework rests on the domain assumption that parameter-token interference can be quantified by a single collaborative score and that stage-wise cost estimation plus limited random exploration suffices to select near-optimal pruning sequences.

free parameters (2)

CIM weighting coefficients
Hyperparameters that balance token significance into parameter importance and vice versa; values are not stated in abstract.
MPS cost and exploration parameters
Tunable thresholds for pruning cost comparison and random trial probability.

axioms (2)

domain assumption Parameters and tokens exhibit quantifiable mutual interference that a joint metric can capture without residual bias
Invoked to justify the design of CIM.
domain assumption Historical cost plus limited random exploration yields stable mode selection across stages
Basis for the MPS adaptive rule.

pith-pipeline@v0.9.0 · 5569 in / 1311 out tokens · 50253 ms · 2026-05-13T20:57:08.900395+00:00 · methodology

Collaborative Multi-Mode Pruning for Vision-Language Models

Core claim

What carries the argument

Load-bearing premise

What would settle it

discussion (0)