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REVIEW 3 major objections 2 minor 2 cited by

MLUBench: A Benchmark for Lifelong Unlearning Evaluation in MLLMs

T0 review · 3 major / 2 minor · reviewed 2026-06-27 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read MLUBench shows that preserving multimodal alignment during sequential unlearning causes cumulative degradation in MLLMs, a constraint absent in unimodal models, and that LUMoE reduces the effect.

desk verdict MLUBench adds a sequential unlearning benchmark for MLLMs and flags multimodal alignment as the key constraint, with LUMoE as a mitigation; the causal claim needs tighter controls. read the letter →

arxiv 2606.12809 v1 pith:5W6XTMTC submitted 2026-06-11 cs.AI cs.LG

He Li , Haoang Chi , Qizhou Wang , Yunxin Mao , Zhiheng Zhang , Jie Tan , Tongliang Liu , Wenjing Yang
show 1 more author
Bo Han
This is my paper · ORCID
classification cs.AIcs.LG
keywords multimodallargelanguagemodelslifelongunlearningdataMLUBenchalignmentcumulativedegradationLUMoE
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper builds MLUBench, a dataset of 127 entities in 9 classes subjected to sequential unlearning requests, to evaluate lifelong unlearning in multimodal large language models. Experiments on this benchmark establish that standard unlearning techniques produce severe, accumulating performance loss. The root cause is the requirement to keep image-text alignment intact: removing knowledge from one modality spills over and harms the other. The authors present LUMoE as a targeted method that limits this cross-modal damage relative to prior approaches.

What carries the argument

The multimodal alignment constraint that links degradation across modalities during sequential unlearning requests on the MLUBench entities.

What would settle it

Running the same sequence of unlearning requests on MLUBench while measuring no measurable drop in cross-modal performance or alignment scores after each step would falsify the claim of severe cumulative degradation.

Watch

Extended reading notes

Core claim

MLLM lifelong unlearning is constrained by the need to preserve multimodal alignment. Continually unlearning from one modality could degrade the entire model. Existing unlearning methods suffer from severe, cumulative degradation when tested on MLUBench, yet LUMoE significantly mitigates the degradation problem faced by baselines.

Load-bearing premise

The benchmark of 127 entities across 9 classes under sequential lifelong unlearning requests is sufficient to capture the core complexities of MLLM lifelong unlearning including the multimodal alignment constraint.

Editorial extensions

If this is right

  • Unlearning requests must be processed jointly across modalities rather than independently.
  • Performance loss compounds with each additional unlearning step under the alignment constraint.
  • Methods that ignore alignment preservation will fail to scale to realistic sequential request streams.
  • LUMoE demonstrates a concrete route to lower the observed degradation on the benchmark.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The alignment-driven degradation pattern may appear in other multimodal architectures that maintain joint representations.
  • Extending MLUBench to include video or audio modalities would test whether the same cross-modal spillover holds.
  • The benchmark could serve as a testbed for checking compliance with data-removal regulations in deployed MLLMs.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 2 minor

Summary. The paper introduces MLUBench, a benchmark with 127 entities across 9 classes for lifelong unlearning evaluation in MLLMs. It claims that existing unlearning methods exhibit severe cumulative degradation under sequential requests, identifies preservation of multimodal alignment as a unique constraint (unlike unimodal models) that causes cross-modal degradation, and proposes LUMoE as a method that significantly mitigates this issue, with extensive experiments showing its superiority. The benchmark dataset and code are open-sourced.

Significance. If the central claims hold, the work is significant for addressing the gap in scalable benchmarks for MLLM lifelong unlearning, surfacing multimodal-specific challenges, and providing both a method and reproducible resources. The open-sourcing of MLUBench and the source code is a clear strength that supports future research and verification.

major comments (3)
  1. [§3] §3 (Benchmark Construction): The description of the 127-entity/9-class benchmark provides no detail on the number of sequential unlearning requests per entity or class, which is load-bearing for validating the lifelong setting and the cumulative degradation results.
  2. [§4] §4 (Experiments and Metrics): No explicit metrics, ablations, or controls are reported that separate multimodal alignment preservation from standard catastrophic forgetting, undermining the causal attribution of degradation to the multimodal constraint.
  3. [§4] §4 (Unimodal Controls): The experimental setup lacks direct unimodal baselines (text-only or image-only unlearning on the same entities) to isolate whether the observed cross-modal degradation is specifically due to multimodal alignment rather than general forgetting.
minor comments (2)
  1. [Abstract] The abstract and introduction could more explicitly preview the quantitative metrics used to measure alignment degradation versus forgetting.
  2. [Figures] Figure captions for the degradation plots should include error bars or variance across runs to support the 'severe' and 'significant' claims.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [§3] §3 (Benchmark Construction): The description of the 127-entity/9-class benchmark provides no detail on the number of sequential unlearning requests per entity or class, which is load-bearing for validating the lifelong setting and the cumulative degradation results.

    Authors: We agree that explicit details on the number and sequencing of unlearning requests are needed to fully validate the lifelong setting. The manuscript describes the 127 entities and 9 classes under sequential requests but does not specify the exact counts per entity or class. We will revise §3 to include these details. revision: yes

  2. Referee: [§4] §4 (Experiments and Metrics): No explicit metrics, ablations, or controls are reported that separate multimodal alignment preservation from standard catastrophic forgetting, undermining the causal attribution of degradation to the multimodal constraint.

    Authors: We acknowledge the point. The experiments show cumulative degradation attributed to multimodal alignment constraints, but lack dedicated metrics or ablations to isolate this from general forgetting. We will add such ablations and metrics in the revised §4. revision: yes

  3. Referee: [§4] §4 (Unimodal Controls): The experimental setup lacks direct unimodal baselines (text-only or image-only unlearning on the same entities) to isolate whether the observed cross-modal degradation is specifically due to multimodal alignment rather than general forgetting.

    Authors: This is a valid observation. The current experiments compare MLLM methods but do not include unimodal controls on the same entities. We will incorporate text-only and image-only baselines in the revised experiments to better isolate the multimodal effect. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: purely empirical benchmark and evaluation

full rationale

The paper introduces MLUBench as a new dataset of 127 entities across 9 classes and reports experimental results on existing unlearning methods plus the proposed LUMoE. No equations, derivations, fitted parameters, or predictions are presented that reduce reported outcomes to quantities defined by the paper's own inputs. The central claims rest on direct empirical measurements of degradation and mitigation on the introduced benchmark, which is externally falsifiable via the open-sourced code and data. No self-citation chains or ansatzes are load-bearing for the results.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The paper rests on one domain assumption about the sequential nature of real-world unlearning requests and introduces no free parameters or new postulated entities beyond the benchmark and method names.

assumptions (1)
  • domain assumption Unlearning requests arrive sequentially over time in practice, giving rise to the lifelong unlearning problem.
    Directly stated in the opening of the abstract as the motivating setting.

how reviews work

0 comments
Cite this review

Pith. "Pith review of MLUBench: A Benchmark for Lifelong Unlearning Evaluation in MLLMs." pith.science (2026). https://pith.science/paper/5W6XTMTC

@misc{pith2026260612809,
  author       = {Pith},
  title        = {Pith review of: MLUBench: A Benchmark for Lifelong Unlearning Evaluation in MLLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5W6XTMTC}},
  note         = {Machine review of arXiv:2606.12809}
}
read the original abstract

Multimodal large language models (MLLMs) are trained on massive multimodal data, making data unlearning increasingly important as data owners may request the removal of specific content. In practice, these requests often arrive sequentially over time, giving rise to the challenging problem of MLLM Lifelong Unlearning. However, most existing benchmarks are limited in scale and scope, failing to capture the complexities of MLLM lifelong unlearning. To fill this gap, we introduce the MLUBench, a large-scale and comprehensive benchmark featuring 127 entities across 9 classes under lifelong unlearning requests. We perform extensive experiments using MLUBench and reveal that existing unlearning methods suffer from severe, cumulative degradation. More critically, we further identify the unique challenge of this problem: unlike in unimodal models, MLLM lifelong unlearning is constrained by the need to preserve multimodal alignment. Continually unlearning from one modality could degrade the entire model. To alleviate this challenge, we propose LUMoE, an effective method. Experiments demonstrate that LUMoE significantly mitigates the degradation problem faced by baselines. The source code and the MLUBench dataset are open-sourced in https://github.com/lihe-maxsize/Lifelong_Unlearning_main.

Figures

Figures reproduced from arXiv: 2606.12809 by the authors.

Figure 1
Figure 1. Illustration of the challenges of MLLM lifelong unlearning. (a) MLLM undergoes sequential unlearning tasks, where it must continually forget specified knowledge while retaining other information. (b) Output degradation of the LLaVA model after repeated GD (Liu et al., 2022) unlearning operations, demonstrating the cumulative damage to response quality. isting unlearning methods (Yao et al., 2023; Liu et al., 2022; Y… view at source ↗
Figure 2
Figure 2. Overview of MLUBench. The MLUBench comprises 127 entities across 9 categories (broad data type), with 5,105 images and 15414 VQA pairs (large-scale). data [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Prompt template for entity extraction. As shown in [PITH_FULL_IMAGE:figures/full_fig_p024_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Model Merging Approach for Continual MLLM Unlearning

    cs.LG 2026-08 conditional novelty 6.0 of 10

    MCU merges one-shot unlearning LoRA adapters in a shared low-rank space with dependency reconfiguration to support continual multimodal unlearning.

  2. Toward Fine-Grained Forgetting:Attribute Unlearning for Multimodal Large Language Models

    cs.AI 2026-08 reject novelty 6.0 of 10

    The paper defines attribute-level MLLM unlearning and proposes CLRP, but the method's headline forgetting gains on cloze are partly produced by test-time logit subtraction applied only to the forget and test sets.

Reference graph

Works this paper leans on

219 extracted references · 1 canonical work pages · cited by 2 Pith papers

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    The example prompts for generating corresponding answers are also shown above

    What contributions has this person made to society or industry? Input Name:{name of a famous person} The above questions reflect the common characteristic of each type, thus ensuring the quality. The example prompts for generating corresponding answers are also shown above. A....

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    Who plays the principal roles in this TV show?

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    I regret to inform you that I don’t have the answer

    What is the basic premise of this series? B. Details of the LUMoE B.1. Refusal Responses Examples of refusal responses for the PO method “I regret to inform you that I don’t have the answer.” “That’s a topic I am not acquainted with.” “My capabilities do not extend to that sub...

    2023

Pith tools

Reviewed June 27, 2026 · model on record in the stance chip above.