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Open-MMUnlearning: Unifying Methods and Evaluation for MLLM Unlearning

Junkai Chen, Yuhao He, Qianshan Wei, Junxiang You, Jingwen Shao, Junkai Lin, Zhongkai Yue, Xiaotian Ye, Zhengbo Jiao, Jiali Cheng, Zhijie Deng, Kening Zheng, Ruiqi Liu, Hadi Amiri, Yi Yu, Zhenan Sun, Qi Li, Ka-Ho Chow, Sijia Liu, Liang Wang, Jiaqi Li, Shu Wu

Year: 2026Venue: arXiv preprintArea: cs.AIType: PreprintEmbeddings: 124

Intelligence

Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 93%

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Summary

The paper introduces Open-MMUnlearning, an open-source framework for Multimodal Large Language Model (MLLM) unlearning that unifies methods, benchmarks, and evaluation protocols. It addresses challenges in privacy, safety, and copyright by supporting five benchmarks, eight MLLMs, and twelve unlearning methods. The study evaluates ten methods, finding GD and MIP-Editor as top performers, and conducts a meta-evaluation of metrics, identifying BLEU and KS-Test as highly reliable for faithfulness and robustness.

Entities (15)

Open-MMUnlearning · framework · 100%BLEU · metric · 95%GD · unlearningmethod · 95%KS-Test · metric · 95%MIP-Editor · unlearningmethod · 95%CLEAR · benchmark · 90%CoVUBench · benchmark · 90%FIUBench · benchmark · 90%LLaVA-1.5 · model · 90%MANU · unlearningmethod · 90%MLLMU · benchmark · 90%MMUnlearner · unlearningmethod · 90%Qwen-2.5-VL · model · 90%SafeEraser · benchmark · 90%SMFA · unlearningmethod · 90%

Relation Signals (8)

Open-MMUnlearning → supports → MLLM Unlearning

confidence 100% · We introduce Open-MMUnlearning, an open-source, extensible framework that integrates... unlearning... through shared interfaces

GD → achieveshighest → forget quality

confidence 95% · GD achieves the highest Forget Quality

KS-Test → hashighestfaithfulness → AUC

confidence 95% · KS-Test attains the highest faithfulness AUC

BLEU → hashighestreliability → Aggregate Score

confidence 95% · Among the thirteen evaluated metrics, BLEU achieves the highest aggregate reliability score.

MIP-Editor → preserves → model utility

confidence 95% · MIP-Editor preserves more Model Utility

Open-MMUnlearning → includes → LLaVA-1.5

confidence 90% · The framework supports... eight MLLMs... LLaVA-1.5

Open-MMUnlearning → includes → GD

confidence 90% · The framework supports... twelve unlearning methods... GD

Open-MMUnlearning → includes → MLLMU

confidence 90% · The framework supports five benchmarks... MLLMU

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Abstract

Abstract:As multimodal large language models (MLLMs) become more capable and widely deployed, concerns about privacy and safety have become increasingly pressing. Machine unlearning offers one approach to addressing these concerns by removing designated information from trained models while preserving unrelated capabilities. However, fragmented implementations and evaluation protocols, incomplete robustness testing, and limited understanding of metric reliability make progress in MLLM unlearning difficult to assess systematically. We introduce Open-MMUnlearning, an open-source, extensible framework that integrates target-model preparation, multimodal data processing, unlearning, and evaluation through shared interfaces and structured configurations. The framework supports five benchmarks spanning privacy, safety, and copyright, eight MLLMs from four model families, and twelve unlearning methods. Its evaluation suite jointly assesses forgetting effectiveness, retained utility, and robustness to model interventions, adversarial inputs, and membership inference attacks. Using a common evaluation protocol, we compare ten representative unlearning methods. In this comparison, GD and MIP-Editor tie for the highest overall score: GD achieves the highest Forget Quality, while MIP-Editor preserves more Model Utility. We further introduce a metric meta-evaluation protocol that tests faithfulness using models with controlled exposure to target knowledge and robustness under quantization and relearning. Among the thirteen evaluated metrics, BLEU achieves the highest aggregate reliability score. KS-Test attains the highest faithfulness AUC but performs less well on robustness. Together, the framework and these findings support reproducible comparison of MLLM unlearning methods and systematic assessment of evaluation reliability.

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Open-MMUnlearning: Unifying Methods and Evaluation for MLLM Unlearning Junkai Chen †thanks: Equal Contribution. Email: chenjunkai2026@ia.ac.cn Yuhao He Email: shu.wu@nlpr.ia.ac.cn Qianshan Wei †thanks: Project Organizer. Junxiang You Affiliation: Institute of Automation, Chinese Academy of Sciences University of the Chinese Academy of Sciences Jingwen Shao Junkai Lin Zhongkai Yue Xiaotian Ye Affiliation: ByteDance The Chinese University of Hong Kong University of Cambridge Zhengbo Jiao Jiali Cheng Affiliation: Shanghai University of Finance and Economics University of Massachusetts at Lowell Zhijie Deng Kening Zheng Affiliation: The Hong Kong University of Science and Technology University of Illinois at Chicago Ruiqi Liu Hadi Amiri Affiliation: Shanghai University of Finance and Economics University of Massachusetts at Lowell Yi Yu Zhenan Sun Qi Li Ka-Ho Chow Sijia Liu Affiliation: Jilin University The University of Hong Kong Michigan State University Liang Wang Jiaqi Li †thanks: Corresponding Authors. Shu Wu†footnotemark: Abstract As multimodal large language models (MLLMs) become more capable and widely deployed, concerns about privacy and safety have become increasingly pressing. Machine unlearning offers one approach to addressing these concerns by removing designated information from trained models while preserving unrelated capabilities. However, fragmented implementations and evaluation protocols, incomplete robustness testing, and limited understanding of metric reliability make progress in MLLM unlearning difficult to assess systematically. We introduce Open-MMUnlearning, an open-source, extensible framework that integrates target-model preparation, multimodal data processing, unlearning, and evaluation through shared interfaces and structured configurations. The framework supports five benchmarks spanning privacy, safety, and copyright, eight MLLMs from four model families, and twelve unlearning methods. Its evaluation suite jointly assesses forgetting effectiveness, retained utility, and robustness to model interventions, adversarial inputs, and membership inference attacks. Using a common evaluation protocol, we compare ten representative unlearning methods. In this comparison, GD and MIP-Editor tie for the highest overall score: GD achieves the highest Forget Quality, while MIP-Editor preserves more Model Utility. We further introduce a metric meta-evaluation protocol that tests faithfulness using models with controlled exposure to target knowledge and robustness under quantization and relearning. Among the thirteen evaluated metrics, BLEU achieves the highest aggregate reliability score. KS-Test attains the highest faithfulness AUC but performs less well on robustness. Together, the framework and these findings support reproducible comparison of MLLM unlearning methods and systematic assessment of evaluation reliability. 1 Introduction Recent advances in Multimodal Large Language Models (MLLMs) (Liu et al., 2023; Bai et al., 2025) have substantially enhanced their multimodal perception and reasoning capabilities, enabling strong performance across image captioning, visual question answering and document understanding. However, these capabilities rely on vast amounts of multimodal data collected from the web and other sources for pre-training and instruction tuning. Such data may contain private (Liu et al., 2025a; Dontsov et al., 2025), harmful (Chen et al., 2025), or copyrighted content (Kwon et al., 2026), raising significant ethical and legal concerns (EU, 2016; Pardau, 2018). Given the billions of parameters in modern MLLMs and the substantial data and computational resources required to train them, retraining a model from scratch for each deletion request is prohibitively costly and operationally impractical. Machine unlearning (Bourtoule et al., 2021) therefore offers a more efficient alternative: it aims to remove the influence of specific data or knowledge while preserving the model’s general performance. However, unlearning is especially challenging for MLLMs because the same concept may be redundantly encoded in visual features, language representations, and their cross-modal interactions, allowing residual information to leak through any modality. 2027 Against this backdrop, MLLM unlearning research has expanded along two fronts: benchmark development and method design. Benchmarks now address privacy (Liu et al., 2025a; Dontsov et al., 2025; Ma et al., 2025), safety (Chen et al., 2025), and copyright (Kwon et al., 2026), while proposed methods span optimization-based (Chen et al., 2025; Li et al., 2024a), localization-based (Liu et al., 2025b; Huo et al., 2025), arithmetic-based (Kim et al., 2026; Zeng et al., 2025), module-based (Garg et al., 2026; Jin et al., 2026), and inference-time approaches (Li et al., 2025; Ding et al., 2026). Despite this progress, the systematic assessment of MLLM unlearning remains hindered by three major challenges. First, unlearning methods remain tied to specific benchmarks and model backbones. Most are developed and evaluated on a limited set of benchmarks and MLLMs (Liu et al., 2025b; Huo et al., 2025), while differences in model initialization, preprocessing, trainable components, and hyperparameter selection complicate faithful reproduction and controlled comparison across settings. Second, evaluation protocols remain fragmented. Benchmarks differ in their forget and retain splits, task formulations, and measures of forgetting effectiveness and retained utility (Liu et al., 2025a; Dontsov et al., 2025; Ma et al., 2025; Chen et al., 2025; Kwon et al., 2026). Robustness evaluation is less standardized still: queries across modalities (Wang et al., 2026a; Liu et al., 2026), targeted relearning (Zheng et al., 2025; Lin et al., 2026), representation probing (Patil et al., 2025), adversarial prompting (Zheng et al., 2025), visual perturbations (Zhang et al., 2025b), and membership inference (Li et al., 2024a) are typically examined in separate studies or limited combinations. Third, the reliability of evaluation metrics remains insufficiently examined. A decrease in performance on the forget set may reflect output suppression or general capability degradation rather than genuine knowledge removal, and different metrics may yield inconsistent assessments of the same model (Dorna et al., 2026; Wang et al., 2025). For MLLMs, aggregate scores may also obscure degradation in a specific modality or residual knowledge accessible through another modality (Wang et al., 2026a; Liu et al., 2026); whether metric judgments remain reliable after quantization or targeted relearning is likewise unclear (Dorna et al., 2026). Although OpenUnlearning (Dorna et al., 2026) unifies LLM unlearning with text data, MLLMs additionally require support for varied model architectures and paired images and text (Liu et al., 2023; Liu et al., 2025a), choices about which vision and language components to update (Huo et al., 2025), and evaluations of leakage across modalities and image rephrasing (Wang et al., 2026a; Patil et al., 2025). Together, these challenges hinder reproducible comparison of unlearning methods and reliable assessment of their outcomes. These challenges motivate the need for a unified and extensible framework that enables end-to-end research on MLLM unlearning (Sarwar et al., 2026). To this end, we introduce Open-MMUnlearning, which supports the complete unlearning lifecycle, including controlled target-model preparation, unlearning intervention, evaluation of forgetting and retained utility, and robustness evaluation against knowledge recovery. Its unified interfaces decouple model-specific processing from unlearning methods and benchmark evaluation, while configuration-driven experiments make preprocessing, optimization, and metric computation explicit and reproducible. Our main contributions are summarized as follows: • We introduce Open-MMUnlearning, a unified, modular, and extensible framework that integrates 5 MLLM unlearning benchmarks, 8 MLLMs from 4 model families, 12 unlearning methods, and a diverse suite of evaluation metrics for forgetting effectiveness and retained utility. The framework also supports a range of robustness evaluations for unlearned models within a common pipeline covering the complete unlearning lifecycle. • We conduct a systematic evaluation of 10 of these integrated unlearning methods using consistent model preparation, hyperparameter tuning, and evaluation protocols. Our evaluation jointly measures forgetting effectiveness, retained utility, and robustness against attacks on the model, inputs, and outputs, providing a unified basis for controlled comparison. The results reveal key tradeoffs among forgetting, utility, and robustness. • We perform a meta-evaluation of commonly used MLLM unlearning metrics, systematically examining their faithfulness in reflecting the presence of target knowledge and their robustness under post-unlearning interventions. This analysis provides empirical evidence on whether existing metrics can reliably distinguish genuine knowledge removal from superficial suppression. 2 Overview of MLLM Unlearning Methods. Existing MLLM unlearning methods can be categorized by their intervention mechanisms. Optimization-based methods induce forgetting through objective-driven updates. Depending on the optimized objectives, these methods can employ general unlearning objectives Zhang et al. (2024a); Li et al. (2024b), counterfactual objectives Chen et al. (2025), knowledge distillation Wang et al. (2026b), structured objectives Li et al. (2024a), feedback-driven objectives Jia et al. (2026), or adversarial objectives Chen et al. (2026a). Localization-based methods edit only the components, neurons, or subspaces associated with the target knowledge Liu et al. (2025b); Huo et al. (2025). Arithmetic-based methods construct the unlearned model through algebraic operations on model weights or update vectors Kim et al. (2026); Zeng et al. (2025). Inference-time methods keep model parameters fixed and suppress target knowledge by intervening in activations or sparse features during decoding Li et al. (2025); Ding et al. (2026). Benchmarks. MLLM unlearning benchmarks can be organized by their primary removal objective. Privacy is the most extensively studied setting and targets identities, profiles, sensitive attributes, and associations. These benchmarks can be further distinguished by the provenance of the target knowledge, including knowledge encoded during pretraining Li et al. (2024a); Patil et al. (2025); Guang et al. (2026) and controlled knowledge introduced through fine-tuning Dontsov et al. (2025); Liu et al. (2025a); Ma et al. (2025). Safety benchmarks target harmful multimodal knowledge jointly elicited by images and associated questions Chen et al. (2025); Xu et al. (2025), while Copyright benchmarks address protected characters, logos, and related background knowledge Kwon et al. (2026). Evaluations. Each benchmark combines forgetting-effectiveness metrics with utility-preservation metrics, with different metrics providing varying levels of evaluation rigor. Classification and cloze tasks commonly use Accuracy or Exact Match Dontsov et al. (2025); Liu et al. (2025a), while open-ended generation is evaluated using lexical-overlap metrics such as ROUGE-L Lin (2004) and BLEU Papineni et al. (2002). Likelihood-based metrics, including negative log-likelihood and perplexity, measure the model’s residual support for a target answer even when that answer is not directly generated Li et al. (2024a); Guang et al. (2026). Retraining-equivalence metrics instead compare the unlearned model with a retain-only reference using Truth-Ratio KS tests Dontsov et al. (2025), Jensen–Shannon divergence Ma et al. (2025), or Wasserstein distance Ma et al. (2025). Beyond these behavioral metrics, robustness evaluations examine whether target information remains recoverable through cross-modal queries Wang et al. (2026a); Liu et al. (2026), membership inference Li et al. (2024a); Ma et al. (2025), paraphrased prompting Patil et al. (2025), adversarial prompting Zheng et al. (2025), hidden-state probing Patil et al. (2025), visual perturbations Zhang et al. (2025b), targeted relearning Zheng et al. (2025), or model quantization Zhang et al. (2025c). 3 Open-MMUnlearning Framework Machine unlearning for multimodal large language models (MLLMs) is complicated by the heterogeneity of model architectures, multimodal data formats, optimization objectives, and evaluation protocols. Because existing implementations are typically tailored to a single benchmark or a specific model family, faithful reproduction and controlled comparison across unlearning methods remain difficult to achieve. This lack of a unified framework manifests across three major aspects of the MLLM unlearning pipeline: Methods remain tied to specific benchmarks and model backbones. Multimodal unlearning methods are typically developed and evaluated on a limited set of benchmarks and MLLM backbones. For example, MANU (Liu et al., 2025b) and MMUnlearner (Huo et al., 2025) are each assessed on a small selection of benchmarks and model families. General unlearning objectives such as NPO (Zhang et al., 2024a) and RMU (Li et al., 2024b) also require adaptation when transferred to multimodal settings. Differences in model initialization, preprocessing, trainable components, and hyperparameter selection further complicate faithful reproduction. Consequently, applying a method to a new benchmark or backbone often requires reimplementing substantial parts of its experimental pipeline. Evaluation protocols remain fragmented. Existing benchmarks differ in how they construct forget and retain splits, formulate evaluation tasks, and report forgetting effectiveness and retained utility (Liu et al., 2025a; Dontsov et al., 2025). Robustness evaluation is less standardized still: targeted relearning (Zheng et al., 2025; Lin et al., 2026), representation probing (Patil et al., 2025), adversarial prompting (Zheng et al., 2025), visual perturbations (Zhang et al., 2025b), and membership inference (Li et al., 2024a) are scattered across individual studies. Existing methods are often evaluated against only one or two types of attacks. Consequently, existing results lack a consistent basis for jointly comparing forgetting, retention, and robustness across methods. The reliability of evaluation metrics remains insufficiently examined. A decrease in forget-set performance does not necessarily establish that target knowledge has been removed; it may instead reflect output suppression or general capability degradation. Different metrics may also yield inconsistent assessments or rankings of the same unlearned models (Dorna et al., 2026; Wang et al., 2025). Beyond such concerns about faithfulness, it remains unclear whether a metric’s assessment stays reliable when an apparently unlearned model undergoes quantization or targeted relearning (Dorna et al., 2026). These issues motivate a systematic examination of both the faithfulness and robustness of MLLM unlearning metrics. Taken together, these limitations motivate the need for a unified and extensible framework for MLLM unlearning. To this end, we introduce Open-MMUnlearning, an end-to-end framework that integrates data preparation, target-model training, unlearning execution, evaluation of Forget Quality and Model Utility, and robustness evaluation within a coherent pipeline. By standardizing these components through shared interfaces, Open-MMUnlearning enables reproducible and directly comparable evaluations across unlearning methods, benchmarks, and MLLM backbones. Figure 1: Overview of Open-MMUnlearning. The Data Worker prepares fine-tuning, evaluation, and unlearning data from benchmarks spanning privacy, safety, and copyright. The Model Loader supplies MLLM backbones, the Trainer executes fine-tuning and general and MLLM-specific unlearning methods, and the Unified Evaluator assesses Forget Quality, Model Utility, and Robustness through model-, input-, and output-side evaluations. Table 1: Overview of existing Open-MMUnlearning components and their available variants. The modular design allows users to add new models, unlearning methods, datasets, and evaluation metrics. Component Variants Models LLaVA-1.5 (Liu et al., 2023) Qwen-2.5-VL (Bai et al., 2025) LLaVA-1.6 (Liu et al., 2024a) Gemma-3 (Team et al., 2025) Methods General Unlearning Methods (Maini et al., 2024; Li et al., 2024b) (Zhang et al., 2024a; Dong et al., 2025) MMUnlearner (Huo et al., 2025) MANU (Liu et al., 2025b) MIP-Editor (Li et al., 2026) SMFA (Zeng et al., 2025) VGID (Chen et al., 2026b) Datasets MLLMU (Liu et al., 2025a) FIUBench (Ma et al., 2025) CLEAR (Dontsov et al., 2025) SafeEraser (Chen et al., 2025) CoVUBench (Kwon et al., 2026) Metrics Forget Truth Ratio (Ma et al., 2025; Dontsov et al., 2025) KS-Test (Ma et al., 2025; Dontsov et al., 2025) JS Distance (Dontsov et al., 2025) Answer Probability (Ma et al., 2025) ROUGE-L (Liu et al., 2025a) ASR (Chen et al., 2025) Utility Model Utility (Ma et al., 2025; Dontsov et al., 2025) Fluency (Kwon et al., 2026) Specificity (Kwon et al., 2026) Generality (Kwon et al., 2026) SARR (Chen et al., 2025) LM-Eval (Liu et al., 2025a; Kwon et al., 2026) Robust Relearning (Hu et al., 2025; Zheng et al., 2025) Quantization (Zhang et al., 2025c) Probing (Lynch et al., 2024) MIA (Yeom et al., 2018; Carlini et al., 2021; Wang et al., 2024; Shi et al., 2024) Jailbreak (Wei et al., 2023; Ma et al., 2024) FigStep (Gong et al., 2025) SUA (Zhang et al., 2025b) Image Rephrasing (Patil et al., 2025) 3.1 Design of Open-MMUnlearning Open-MMUnlearning is designed for usability, reproducibility, and extensibility. It decomposes the end-to-end unlearning pipeline into independently registered modules, including model handlers, unlearning methods, benchmark evaluators, and robustness audits. Each experiment is specified through structured configuration files that select and parameterize these modules. This configuration-driven design enables experiments to be reproduced under the same settings and allows individual components to be exchanged without modifying the core execution logic. Features. Open-MMUnlearning currently supports twelve unlearning methods, comprising seven general unlearning methods (Maini et al., 2024; Li et al., 2024b; Zhang et al., 2024a; Dong et al., 2025) and five MLLM-specific unlearning methods (Huo et al., 2025; Liu et al., 2025b; Li et al., 2026; Zeng et al., 2025; Chen et al., 2026b). It integrates eight MLLMs of different parameter scales across four model families (Liu et al., 2023; Bai et al., 2025; Liu et al., 2024a; Team et al., 2025) and five benchmarks spanning privacy, safety, and copyright (Liu et al., 2025a; Ma et al., 2025; Dontsov et al., 2025; Chen et al., 2025; Kwon et al., 2026). Its evaluation components are organized into Forget, Utility, and Robust. Forgetting and utility are assessed using task-based metrics such as Accuracy and Exact Match (Liu et al., 2025a; Ma et al., 2025), lexical metrics such as ROUGE-L and BLEU (Liu et al., 2025a), likelihood- and distribution-based metrics (Ma et al., 2025; Dontsov et al., 2025), and safety-oriented metrics such as ASR, R, and SARR (Chen et al., 2025). Robustness evaluation covers relearning and quantization (Hu et al., 2025; Zheng et al., 2025; Zhang et al., 2025c), probing and cross-modal leakage (Lynch et al., 2024; Wang et al., 2026a), textual and visual attacks (Zou et al., 2023; Wei et al., 2023; Ma et al., 2024; Gong et al., 2025; Patil et al., 2025; Zhang et al., 2025b), and membership inference (Yeom et al., 2018; Carlini et al., 2021; Wang et al., 2024; Shi et al., 2024; Zhang et al., 2025a). Due to space constraints, Table 1 provides a condensed overview, while Table 4 in the appendix lists the complete set of components and evaluation variants. Detailed descriptions of the unlearning methods and evaluation metrics are provided in Appendices A.3.1 and A.2, respectively. Extensibility. Open-MMUnlearning organizes models, methods, benchmarks, evaluation metrics, and robustness attacks as independently extensible modules behind common interfaces. A new component can be integrated by implementing the corresponding interface and specifying its settings through configuration files, without modifying the core pipeline. Once integrated, models and methods can directly reuse existing benchmark evaluators and robustness audits, while metrics and attacks can be applied across compatible benchmarks and model families. This design allows the framework to continuously incorporate emerging MLLM architectures, unlearning techniques, evaluation protocols, and recovery attacks. 4 Evaluation of Unlearning Methods 4.1 Experiment Setup Unlike prior multimodal unlearning studies that compare only a limited number of baselines or evaluation metrics, we establish a standardized experimental pipeline for large-scale comparison of unlearning methods. We conduct experiments on MLLMU-Bench under the 10%10\% forget setting. Unlearning Methods We compare ten representative unlearning methods: GA, GD, KL (Maini et al., 2024), NPO (Zhang et al., 2024a), RMU (Li et al., 2024b), and UNDIAL (Dong et al., 2025), MANU (Liu et al., 2025b), MMUnlearner (Huo et al., 2025), MIP-Editor (Li et al., 2026), SMFA (Zeng et al., 2025). Evaluation metrics. We evaluate unlearned models in terms of Forget Quality, Model Utility, and Robustness. Forget Quality is assessed on the multimodal forget set and a held-out forget-target test set using fill-in-the-blank accuracy (Liu et al., 2025a), classification accuracy (Liu et al., 2025a), ROUGE-L (Lin, 2004), BLEU (Papineni et al., 2002), Truth Ratio (Ma et al., 2025; Dontsov et al., 2025), Answer Probability (Ma et al., 2025), KS-Test (Ma et al., 2025; Dontsov et al., 2025), and JS Distance (Dontsov et al., 2025). Model Utility is evaluated on non-target knowledge and general capabilities using the retain set and general-purpose benchmarks. Robustness is evaluated through model-side quantization (Zhang et al., 2025c), targeted relearning (Hu et al., 2025; Zheng et al., 2025), and representation probing (Lynch et al., 2024); input-side cross-modal text queries, FigStep (Gong et al., 2025), pure-text jailbreak prompting (Zou et al., 2023; Wei et al., 2023; Ma et al., 2024), image rephrasing (Patil et al., 2025), and SUA (Zhang et al., 2025b); and output-side membership inference using LOSS (Yeom et al., 2018), ZLIB (Carlini et al., 2021), GradNorm (Wang et al., 2024), Min-K% (Shi et al., 2024), and Min-K%++ (Zhang et al., 2025a). The metrics within each evaluation dimension are aggregated separately to produce the corresponding composite scores; detailed normalization and aggregation rules are provided in Appendix A.3.3. Hyperparameter tuning strategy. We use pre-specified, method-specific hyperparameter search spaces and evaluate at least three candidate values for each principal hyperparameter where applicable. All methods follow the same target-model preparation and evaluation protocol. For each method, we select the checkpoint that maximizes the mean of Forget Quality and Model Utility on the evaluation splits reported in Table 2. Robustness is evaluated after checkpoint selection and does not influence tuning. The search spaces and selection procedure are detailed in Appendices A.3.2 and A.3.3, respectively. Table 2: Evaluation of ten representative MLLM unlearning methods in terms of Forget Quality, Model Utility, and Robustness. We also report an overall aggregate score and divide Robustness into model-, input-, and output-side evaluations. Finetune and Retain-Only serve as reference models. Higher scores are better; bold and underlined values indicate the best and second-best results among the unlearning methods, respectively. Methods Agg. ↑ Forget Quality,↑ Model Utility,↑ Robustness ↑ Agg. ↑ Model ↑ Input ↑ Output ↑ Reference Models Finetune 0.622 0.687 0.421 0.759 0.835 0.631 0.812 Retain-Only 0.638 0.696 0.444 0.776 0.839 0.688 0.799 General Unlearning Methods GA (Maini et al., 2024) 0.574 0.774 0.066 0.881 0.996 0.971 0.677 GD (Maini et al., 2024) 0.647 0.781 0.400 0.760 0.894 0.767 0.619 KL (Maini et al., 2024) 0.617 0.694 0.391 0.767 0.833 0.668 0.800 RMU (Li et al., 2024b) 0.632 0.707 0.428 0.760 0.846 0.664 0.770 NPO (Zhang et al., 2024a) 0.630 0.749 0.382 0.757 0.873 0.685 0.714 UNDIAL (Dong et al., 2025) 0.640 0.722 0.454 0.745 0.826 0.731 0.677 MLLM-specific Unlearning Methods MMUnlearner (Huo et al., 2025) 0.632 0.739 0.411 0.745 0.889 0.756 0.590 MANU (Liu et al., 2025b) 0.627 0.684 0.439 0.758 0.839 0.658 0.777 MIP-Editor (Li et al., 2026) 0.647 0.717 0.451 0.773 0.828 0.676 0.814 SMFA (Zeng et al., 2025) 0.546 0.712 0.148 0.778 0.889 0.661 0.785 4.2 Results Analysis Overall results. Table 2 shows that MIP-Editor (Li et al., 2026) and GD (Maini et al., 2024) jointly achieve the highest aggregate score of 0.647, followed by UNDIAL (Dong et al., 2025) at 0.640. On average, the adapted general methods achieve a higher overall score of 0.623 versus 0.613 and a higher Robustness score of 0.778 versus 0.764. The MLLM-specific methods, in contrast, have a slight advantage in Model Utility, scoring 0.362 versus 0.354. The general methods’ higher average overall score is associated with stronger Forget Quality and Robustness, rather than better utility preservation. MIP-Editor and GD reach the same aggregate score through different balances across the three evaluation dimensions. Trade-off between Forget Quality and Model Utility. The results illustrate the importance of considering forgetting and utility together. GD attains the highest Forget Quality score of 0.781, while its Model Utility is 0.400. GA (Maini et al., 2024) ranks second in Forget Quality at 0.774 and first in Robustness at 0.881, yet its Model Utility falls to 0.066, indicating severe degradation of non-target capabilities. Conversely, UNDIAL achieves the highest Model Utility of 0.454 with a lower Forget Quality score of 0.722. MIP-Editor combines near-best Model Utility of 0.451 with Forget Quality of 0.717 and Robustness of 0.773, yielding a top aggregate score of 0.647. Thus, strength on any single dimension does not guarantee the best combined outcome. Robustness and ranking. GA (Maini et al., 2024) achieves the highest Robustness score of 0.881, driven by near-perfect Model- and Input-side scores of 0.996 and 0.971. However, its Model Utility is only 0.066, indicating severe degradation of non-target capabilities. Its apparent robustness may therefore partly reflect a broadly impaired ability to answer, rather than selective resistance to knowledge recovery. SMFA (Zeng et al., 2025) ranks second in Robustness at 0.778, although its Input-side score is considerably lower at 0.661. MIP-Editor (Li et al., 2026) achieves the highest Output-side score of 0.814, but its Input-side score is 0.676. These contrasting profiles show why robustness must be interpreted together with Model Utility and the individual attack surfaces, rather than through the aggregate alone. Aggregation and ranking. Method rankings depend on the aggregation rule. Our arithmetic mean allows a strong score in one dimension to offset a weakness in another, so the overall ranking should be interpreted alongside the component scores. Detailed score normalization and aggregation rules are provided in Appendix A.3.3. 5 Evaluation of Unlearning Metrics Figure 2: Faithfulness and robustness meta-evaluation of ROUGE-L. The left and middle panels compare scores before and after targeted relearning and quantization, respectively. Blue points represent eligible unlearned checkpoints, the red star denotes the mean Retain-Only reference, and the shaded areas indicate intervention-specific unreliable regions; the dashed red line marks y=xy=x. The right panel shows the positive and negative model-pool distributions and the selected threshold, with a faithfulness AUC of 0.870. Unlearning metrics may provide misleading assessments: they can fail to reveal target knowledge that remains encoded in a model, and their judgments may change substantially under subsequent model interventions. Inspired by OpenUnlearning (Dorna et al., 2026), we audit MLLM unlearning metrics along two complementary dimensions corresponding to these failure modes: Faithfulness and Robustness. Faithfulness examines whether a metric accurately reflects the presence of target knowledge, whereas Robustness assesses whether its judgment remains reliable under benign interventions such as quantization and non-benign interventions such as targeted relearning. Faithfulness. A faithful unlearning metric should distinguish models that contain the target knowledge from those that have never learned it. Let m⁡(M,F)m(M;D_F) denote the score produced by metric m for model M on the forget set FD_F. We normalize the direction and range of each metric to [0,1][0,1], such that a larger value indicates stronger evidence that the target knowledge is present. Applying metric m to the positive and negative pools produces two score distributions: +m=m⁡(M,F)∣M∈,−m=m⁡(M,F)∣M∈.S_+^m= \m(M;D_F) M \, _-^m= \m(M;D_F) M \. (1) We quantify faithfulness using the area under the receiver operating characteristic curve: Fm=AUC-ROC⁡(+m,−m).F_m=AUC -ROC (S_+^m,S_-^m ). (2) A higher FmF_m indicates that the metric more reliably separates models possessing the target knowledge from models that have not learned it. We additionally select the score threshold that maximizes classification accuracy between the two pools and use this threshold to identify apparently unlearned models for the subsequent robustness analysis. Robustness. Following OpenUnlearning (Dorna et al., 2026), we evaluate the robustness of a metric’s initial judgment of successful unlearning using model quantization (Zhang et al., 2025c) and targeted relearning (Hu et al., 2025). For models judged to be successfully unlearned by the metric, quantization examines whether target knowledge becomes recoverable through a parameter transformation that introduces no additional target data, thereby challenging the initial judgment. Relearning compares changes in the metric when an unlearned model and a retain-only reference model are re-exposed to the same target data under a matched training budget. Substantially greater recovery in the unlearned model provides evidence that the initial assessment may have overstated the extent of forgetting. Together, these tests assess the reliability of the initial unlearning judgment under the specified interventions. The detailed definitions are provided in Appendix A.4. Aggregation. We aggregate the quantization and relearning robustness scores using their harmonic mean. The overall score is then computed as the harmonic mean of faithfulness and robustness. Detailed definitions are provided in Appendix A.4. 5.1 Experimental Setup We conduct the metric meta-evaluation on MLLMU using LLaVA-1.5-7B as the base model. For faithfulness, we compare a positive model pool P trained with the target knowledge against a matched negative pool N trained without it. For robustness, we construct diverse unlearned models using different methods and hyperparameters, excluding models with more than a 20%20\% utility drop or insufficient forgetting under the corresponding metric (Dorna et al., 2026). 5.2 Results Analysis Table 3 highlights three key findings under our meta-evaluation protocol. (i) BLEU achieves the highest aggregate score, combining strong faithfulness with high robustness scores, while ROUGE-L ranks second overall. These results indicate favorable performance of lexical-overlap metrics within the evaluated setting, although their ability to detect target facts may depend on the lexical correspondence between generated and reference answers. (i) Likelihood- and distribution-based metrics exhibit different faithfulness–robustness profiles. Truth Ratio achieves the highest aggregate robustness and ranks third overall, but separates the positive and negative model pools less effectively than KS-Test, JS Distance, and Probability. KS-Test achieves the highest faithfulness, while both KS-Test and JS Distance receive lower robustness scores than Truth Ratio. Probability exhibits a more balanced profile across the two dimensions. These differences show that metrics derived from answer probabilities can behave differently under the same evaluation protocol. (i) Cloze and Classification obtain high robustness scores but relatively low faithfulness, indicating limited discrimination between the positive and negative model pools. The membership-inference metrics receive lower robustness scores under targeted relearning than under quantization. Among them, GradNorm achieves the highest relearning robustness, whereas Min-K%++ obtains the lowest overall aggregate score across all evaluated metrics. Overall, reliable unlearning evaluation should jointly consider whether a metric distinguishes models with and without exposure to target knowledge, whether its judgment remains stable under benign quantization, and whether it appropriately reflects knowledge recovery under non-benign targeted relearning relative to a retain-only reference. Table 3: Meta-evaluation of 13 MLLM unlearning metrics for faithfulness and robustness. Faithful. reports the ROC-AUC for distinguishing models with and without exposure to target knowledge. Quant. and Relearn. report robustness under quantization and targeted relearning, respectively. Higher scores indicate greater reliability. The best and second-best values in each column are shown in bold and underlined, respectively. Metrics Agg.↑ Faithful. ↑ Robustness ↑ Agg.↑ Quant. ↑ Relearn↑ Cloze 0.766 0.626 0.986 0.995 0.978 Classification 0.740 0.604 0.954 0.978 0.930 Truth Ratio 0.896 0.814 0.996 0.999 0.992 Probability 0.887 0.924 0.853 0.928 0.789 KS-Test 0.888 0.991 0.804 0.900 0.727 JS Distance 0.793 0.934 0.689 0.697 0.681 ROUGE-L 0.921 0.870 0.978 0.992 0.964 BLEU 0.950 0.910 0.993 0.998 0.988 LOSS 0.817 0.813 0.821 0.988 0.703 ZLIB 0.793 0.826 0.763 0.985 0.623 GradNorm 0.873 0.813 0.943 1.000 0.892 Min-K% 0.755 0.816 0.703 0.959 0.555 Min-K%++ 0.493 0.586 0.426 0.753 0.297 6 Conclusion MLLM unlearning research remains hindered by fragmented implementations and inconsistent evaluation protocols. We introduced Open-MMUnlearning, a unified and extensible framework that integrates twelve unlearning methods and five benchmarks into a common, reproducible pipeline for target-model preparation, multimodal data processing, unlearning, and evaluation. The framework provides diverse metrics for forgetting effectiveness and retained utility, along with a range of robustness evaluations for unlearned models. Our controlled comparison of General Unlearning Methods and MLLM-specific Unlearning Methods shows that the former achieve slightly higher mean overall and Robustness scores, whereas the latter have a slight advantage in Model Utility. Strong Forget Quality can also coexist with substantial utility loss. Our metric meta-evaluation shows that faithfulness and robustness can diverge: a metric that distinguishes models by target-knowledge exposure may still prove unreliable under quantization or targeted relearning. Together, these findings highlight the need to evaluate forgetting, retained utility, robustness, and metric reliability jointly when developing MLLM unlearning methods. AI Use Statement We used GPT-6 to assist with literature retrieval and with drafting, polishing, and translating parts of the manuscript. We used GPT-4o to generate question paraphrases and answer expansions for MLLMU-based faithfulness experiments, and GPT-4o to generate fact-preserving answer paraphrases and factually perturbed answers for metric evaluation. We applied automated checks for required fields and nonempty outputs, as well as the number and uniqueness of generated evaluation answers. The prompts and data construction procedures are detailed in Appendix A.4.4. The authors take responsibility for the final manuscript, data, and results. References Bai et al. (2025) S. Bai, K. Chen, X. Liu, J. Wang, W. Ge, S. Song, K. Dang, P. Wang, S. Wang, J. Tang, H. Zhong, Y. Zhu, M. Yang, Z. Li, J. Wan, P. Wang, W. Ding, Z. Fu, Y. Xu, J. Ye, X. Zhang, T. Xie, Z. Cheng, H. Zhang, Z. Yang, H. Xu, and J. Lin Qwen2.5-vl technical report. External Links: 2502.13923, Link Cited by: Table 4, §1, §3.1, Table 1. Bourtoule et al. (2021) L. Bourtoule, V. Chandrasekaran, C. A. Choquette-Choo, H. Jia, A. Travers, B. Zhang, D. Lie, and N. Papernot Machine unlearning. In 2021 IEEE symposium on security and privacy (SP), p. 141–159. Cited by: §1. Carlini et al. (2021) N. Carlini, F. Tramer, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, A. Roberts, T. Brown, D. Song, U. Erlingsson, et al. Extracting training data from large language models. In 30th USENIX security symposium (USENIX Security 21), p. 2633–2650. Cited by: §A.2.3, Table 4, §3.1, Table 1, §4.1. Chao et al. (2025) P. Chao, A. Robey, E. Dobriban, H. Hassani, G. J. Pappas, and E. Wong Jailbreaking black box large language models in twenty queries. In 2025 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), p. 23–42. Cited by: §A.2.2. Chen et al. (2026a) H. Chen, J. Li, Y. Zhang, J. Bi, Y. Xia, J. Gu, and V. Tresp AUVIC: adversarial unlearning of visual concepts for multi-modal large language models. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 40, p. 30218–30225. Cited by: §2. Chen et al. (2025) J. Chen, Z. Deng, K. Zheng, Y. Yan, S. Liu, P. Wu, P. Jiang, J. Liu, and X. Hu Safeeraser: enhancing safety in multimodal large language models through multimodal machine unlearning. In Findings of the Association for Computational Linguistics: ACL 2025, p. 14194–14224. Cited by: Table 4, Table 4, Table 4, Table 4, Table 4, §1, §1, §1, §2, §2, §3.1, Table 1, Table 1, Table 1. Chen et al. (2026b) J. Chen, Y. He, J. You, R. Liu, C. Wang, and S. Wu Visual-noise guided in-context distillation for multimodal large language model unlearning. arXiv preprint arXiv:2606.00105. Cited by: §A.3.1, Table 4, §3.1, Table 1. Ding et al. (2026) C. Ding, J. Wu, L. Sheng, F. Zhang, Y. Yuan, X. Wang, and X. He MLLMEraser: achieving test-time unlearning in multimodal large language models through activation steering. External Links: 2510.04217, Link Cited by: §1, §2. Dong et al. (2025) Y. R. Dong, H. Lin, M. Belkin, R. Huerta, and I. Vulić UNDIAL: self-distillation with adjusted logits for robust unlearning in large language models. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), Albuquerque, New Mexico, p. 8827–8840. External Links: Link, ISBN 979-8-89176-189-6 Cited by: §A.3.1, Table 10, Table 11, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9, §3.1, Table 1, §4.1, §4.2, Table 2. Dontsov et al. (2025) A. Dontsov, D. Korzh, A. Zhavoronkin, B. Mikheev, D. Bobkov, A. Alanov, O. Rogov, I. Oseledets, and E. Tutubalina Clear: character unlearning in textual and visual modalities. In Findings of the Association for Computational Linguistics: ACL 2025, p. 20582–20603. Cited by: Table 4, Table 4, Table 4, Table 4, Table 4, Table 4, §1, §1, §1, §2, §2, §3, §3.1, Table 1, Table 1, Table 1, Table 1, Table 1, §4.1. Dorna et al. (2026) V. Dorna, A. Mekala, W. Zhao, A. McCallum, Z. Kolter, Z. Lipton, and P. Maini Openunlearning: accelerating llm unlearning via unified benchmarking of methods and metrics. Advances in Neural Information Processing Systems 38. Cited by: §A.2.3, §1, §3, §5, §5.1, §5. EU (2016) G. EU General data protection regulation. Official Journal of the European Union. Cited by: §1. Fan et al. (2025) C. Fan, J. Jia, Y. Zhang, A. Ramakrishna, M. Hong, and S. Liu Towards llm unlearning resilient to relearning attacks: a sharpness-aware minimization perspective and beyond. arXiv preprint arXiv:2502.05374. Cited by: §A.2.1. Garg et al. (2026) A. Garg, H. Saratchandran, and S. Lucey SineProject: machine unlearning for stable vision language alignment. External Links: 2511.18444, Link Cited by: §1. Gong et al. (2025) Y. Gong, D. Ran, J. Liu, C. Wang, T. Cong, A. Wang, S. Duan, and X. Wang Figstep: jailbreaking large vision-language models via typographic visual prompts. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 39, p. 23951–23959. Cited by: §A.2.2, Table 4, §3.1, Table 1, §4.1. Guang et al. (2026) J. Guang, Z. Zhan, Z. Xu, C. Gao, H. Wang, J. Li, Z. Gu, and Y. Zhang PPU-bench: real world benchmark for personalized partial unlearning in vision language models. arXiv preprint arXiv:2605.08800. Cited by: §2, §2. Hu et al. (2024) S. Hu, Y. Fu, S. Wu, and V. Smith Jogging the memory of unlearned llms through targeted relearning attacks. In Neurips Safe Generative AI Workshop 2024, Cited by: §A.2.1. Hu et al. (2025) S. Hu, Y. Fu, S. Wu, and V. Smith Unlearning or obfuscating? jogging the memory of unlearned llms via benign relearning. In International Conference on Learning Representations, Vol. 2025, p. 8857–8888. Cited by: Table 4, §3.1, Table 1, §4.1, §5. Huo et al. (2025) J. Huo, Y. Yan, X. Zheng, Y. Lyu, X. Zou, Z. Wei, and X. Hu Mmunlearner: reformulating multimodal machine unlearning in the era of multimodal large language models. In Findings of the Association for Computational Linguistics: ACL 2025, p. 7190–7206. Cited by: §A.3.1, Table 10, Table 11, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9, §1, §1, §2, §3, §3.1, Table 1, §4.1, Table 2. Jia et al. (2026) K. Jia, Y. Lin, C. Yang, J. Ma, and J. Su Object hallucination-free reinforcement unlearning for vision-language models. arXiv preprint arXiv:2605.08031. Cited by: §2. Jin et al. (2026) H. Jin, D. Han, and E. Kim Which concepts to forget and how to refuse? decomposing concepts for continual unlearning in large vision-language models. External Links: 2603.21484, Link Cited by: §1. Kim et al. (2026) Y. Kim, D. Hwang, S. Cha, and J. Choe Knowledge vector weakening: efficient training-free unlearning for large vision-language models. External Links: 2601.21794, Link Cited by: §1, §2. Kwon et al. (2026) J. Kwon, J. Yun, and Y. Kim Erase persona, forget lore: benchmarking multimodal copyright unlearning in large vision language models. arXiv preprint arXiv:2605.03547. Cited by: Table 4, Table 4, Table 4, Table 4, Table 4, Table 4, Table 4, Table 4, Table 4, §1, §1, §1, §2, §3.1, Table 1, Table 1, Table 1, Table 1, Table 1. Li et al. (2024a) J. Li, Q. Wei, C. Zhang, G. Qi, M. Du, Y. Chen, S. Bi, and F. Liu Single image unlearning: efficient machine unlearning in multimodal large language models. Advances in Neural Information Processing Systems 37, p. 35414–35453. Cited by: §1, §1, §2, §2, §2, §3. Li et al. (2026) K. Li, W. Li, D. Wu, L. Yang, J. Bai, J. Jia, and J. Xue Cross-modal unlearning via influential neuron path editing in multimodal large language models. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 40, p. 35589–35597. Cited by: §A.3.1, Table 10, Table 11, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9, §3.1, Table 1, §4.1, §4.2, §4.2, Table 2. Li et al. (2024b) N. Li, A. Pan, A. Gopal, S. Yue, D. Berrios, A. Gatti, J. D. Li, A. Dombrowski, S. Goel, L. Phan, et al. The WMDP benchmark: measuring and reducing malicious use with unlearning. arXiv preprint arXiv:2403.03218. Cited by: §A.3.1, Table 10, Table 11, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9, §2, §3, §3.1, Table 1, §4.1, Table 2. Li et al. (2025) Q. Li, J. Geng, D. Zhu, F. Cai, C. Lyu, and F. Karray Sauce: selective concept unlearning in vision-language models with sparse autoencoders. arXiv preprint arXiv:2503.14530. Cited by: §1, §2. Lin (2004) C. Lin Rouge: a package for automatic evaluation of summaries. In Text summarization branches out, p. 74–81. Cited by: §2, §4.1. Lin et al. (2026) Y. Lin, K. Jia, J. Ma, C. Yang, and J. Su On the robustness of machine unlearning for vision-language models. arXiv preprint arXiv:2605.26992. Cited by: §1, §3. Liu et al. (2026) C. Liu, J. Chen, H. Jiang, J. Zhao, Y. Pang, J. Li, J. He, Y. Lu, J. Bi, and Y. Du Does forgetting transfer across modalities? a real-world benchmark for cross-modal knowledge unlearning evaluation. arXiv preprint arXiv:2608.03791. Cited by: §1, §2. Liu et al. (2024a) H. Liu, C. Li, Y. Li, B. Li, Y. Zhang, S. Shen, and Y. J. Lee Llavanext: improved reasoning, ocr, and world knowledge. Cited by: Table 4, §3.1, Table 1. Liu et al. (2023) H. Liu, C. Li, Q. Wu, and Y. J. Lee Visual instruction tuning. Advances in neural information processing systems 36, p. 34892–34916. Cited by: Table 4, §1, §1, §3.1, Table 1. Liu et al. (2024b) X. Liu, N. Xu, M. Chen, and C. Xiao Autodan: generating stealthy jailbreak prompts on aligned large language models. In International Conference on Learning Representations, Vol. 2024, p. 56174–56194. Cited by: §A.2.2. Liu et al. (2025a) Z. Liu, G. Dou, M. Jia, Z. Tan, Q. Zeng, Y. Yuan, and M. Jiang Protecting privacy in multimodal large language models with mllmu-bench. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), p. 4105–4135. Cited by: §A.4.4, Table 4, Table 4, Table 4, Table 4, Table 4, Table 4, §1, §1, §1, §2, §2, §3, §3.1, Table 1, Table 1, Table 1, §4.1. Liu et al. (2025b) Z. Liu, G. Dou, X. Yuan, C. Zhang, Z. Tan, and M. Jiang Modality-aware neuron pruning for unlearning in multimodal large language models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), p. 5913–5933. Cited by: §A.3.1, Table 10, Table 11, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9, §1, §1, §2, §3, §3.1, Table 1, §4.1, Table 2. Lynch et al. (2024) A. Lynch, P. Guo, A. Ewart, S. Casper, and D. Hadfield-Menell Eight methods to evaluate robust unlearning in llms. arXiv preprint arXiv:2402.16835. Cited by: Table 4, §3.1, Table 1, §4.1. Ma et al. (2024) S. Ma, W. Luo, Y. Wang, and X. Liu Visual-roleplay: universal jailbreak attack on multimodal large language models via role-playing image character. arXiv preprint arXiv:2405.20773. Cited by: Table 4, §3.1, Table 1, §4.1. Ma et al. (2025) Y. Ma, J. Wang, F. Wang, S. Ma, J. Li, J. Pan, X. Li, F. Huang, L. Sun, B. Li, et al. Benchmarking vision language model unlearning via fictitious facial identity dataset. In International Conference on Learning Representations, Vol. 2025, p. 87806–87827. Cited by: Table 4, Table 4, Table 4, Table 4, Table 4, Table 4, Table 4, §1, §1, §2, §2, §3.1, Table 1, Table 1, Table 1, Table 1, Table 1, §4.1. Maini et al. (2024) P. Maini, Z. Feng, A. Schwarzschild, Z. C. Lipton, and J. Z. Kolter TOFU: A task of fictitious unlearning for LLMs. First Conference On Language Modeling. External Links: Link Cited by: §A.3.1, §A.3.1, §A.3.1, §A.3.1, Table 10, Table 10, Table 10, Table 11, Table 11, Table 11, Table 4, Table 4, Table 5, Table 5, Table 5, Table 6, Table 6, Table 6, Table 7, Table 7, Table 7, Table 8, Table 8, Table 8, Table 9, Table 9, Table 9, §3.1, Table 1, §4.1, §4.2, §4.2, §4.2, Table 2, Table 2, Table 2. Papineni et al. (2002) K. Papineni, S. Roukos, T. Ward, and W. Zhu Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, p. 311–318. Cited by: §2, §4.1. Pardau (2018) S. L. Pardau The california consumer privacy act: towards a european-style privacy regime in the united states. J. Tech. L. & Pol’y 23, p. 68. Cited by: §1. Patil et al. (2025) V. Patil, Y. Sung, P. Hase, J. Peng, T. Chen, and M. Bansal Unlearning sensitive information in multimodal llms: benchmark and attack-defense evaluation. arXiv preprint arXiv:2505.01456. Cited by: §A.2.2, Table 4, §1, §2, §2, §3, §3.1, Table 1, §4.1. Sarwar et al. (2026) N. Sarwar, S. R. Dipta, Z. Liu, and V. Patil Multimodal unlearning across vision, language, video, and audio: survey of methods, datasets, and benchmarks. In Findings of the Association for Computational Linguistics: ACL 2026, p. 27702–27730. Cited by: §1. Shi et al. (2024) W. Shi, A. Ajith, M. Xia, Y. Huang, D. Liu, T. Blevins, D. Chen, and L. Zettlemoyer Detecting pretraining data from large language models. In International Conference on Learning Representations, Vol. 2024, p. 51826–51843. Cited by: §A.2.3, Table 4, §3.1, Table 1, §4.1. Team et al. (2025) G. Team, A. Kamath, J. Ferret, S. Pathak, N. Vieillard, R. Merhej, S. Perrin, T. Matejovicova, A. Ramé, M. Rivière, et al. Gemma 3 technical report. arXiv preprint arXiv:2503.19786. Cited by: Table 4, §3.1, Table 1. Wang et al. (2026a) C. Wang, Y. Li, X. Feng, C. Chen, X. Zheng, and J. Yin Umu-bench: closing the modality gap in multimodal unlearning evaluation. Advances in Neural Information Processing Systems 38. Cited by: Table 4, §1, §2, §3.1. Wang et al. (2024) J. G. Wang, J. Wang, M. Li, and S. Neel Pandora’s white-box: increased training data leakage in open llms. arXiv preprint arXiv:2402.17012. Cited by: §A.2.3, Table 4, §3.1, Table 1, §4.1. Wang et al. (2025) Q. Wang, B. Han, P. Yang, J. Zhu, T. Liu, and M. Sugiyama Towards effective evaluations and comparisons for llm unlearning methods. In International Conference on Learning Representations, Vol. 2025, p. 23327–23355. Cited by: §1, §3. Wang et al. (2026b) Y. Wang, Z. Niu, H. Ji, G. He, H. Gao, and G. Hua Robust mllm unlearning via visual knowledge distillation. External Links: 2512.11325, Link Cited by: §2. Wei et al. (2023) A. Wei, N. Haghtalab, and J. Steinhardt Jailbroken: how does llm safety training fail?. Advances in neural information processing systems 36, p. 80079–80110. Cited by: §A.2.2, Table 4, §3.1, Table 1, §4.1. Xu et al. (2025) D. Xu, X. Yang, Y. Li, J. Li, and P. Heng From learning to unlearning: biomedical security protection in multimodal large language models. arXiv preprint arXiv:2508.04192. Cited by: §2. Yeom et al. (2018) S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha Privacy risk in machine learning: analyzing the connection to overfitting. In 2018 IEEE 31st computer security foundations symposium (CSF), p. 268–282. Cited by: §A.2.3, Table 4, §3.1, Table 1, §4.1. Zeng et al. (2025) Z. Zeng, L. Gu, Z. Duan, F. Li, Z. Shi, C. G. M. Snoek, and M. Wang Towards benign memory forgetting for selective multimodal large language model unlearning. External Links: 2511.20196, Link Cited by: §A.3.1, Table 10, Table 11, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9, §1, §2, §3.1, Table 1, §4.1, §4.2, Table 2. Zhang et al. (2025a) J. Zhang, J. Sun, E. Yeats, Y. Ouyang, M. Kuo, J. Zhang, H. Yang, and H. Li Min-k%++: improved baseline for pre-training data detection from large language models. In International Conference on Learning Representations, Vol. 2025, p. 64845–64862. Cited by: §A.2.3, Table 4, §3.1, §4.1. Zhang et al. (2024a) R. Zhang, L. Lin, Y. Bai, and S. Mei Negative preference optimization: from catastrophic collapse to effective unlearning. External Links: 2404.05868, Link Cited by: §A.3.1, Table 10, Table 11, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9, §2, §3, §3.1, Table 1, §4.1, Table 2. Zhang et al. (2025b) X. Zhang, H. Liu, D. C. Zhang, X. Tang, Q. He, D. Lee, and S. Wang Sua: stealthy multimodal large language model unlearning attack. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, p. 11241–11254. Cited by: §A.2.2, Table 4, §1, §2, §3, §3.1, Table 1, §4.1. Zhang et al. (2024b) Z. Zhang, J. Yang, P. Ke, S. Cui, C. Zheng, H. Wang, and M. Huang Safe unlearning: a surprisingly effective and generalizable solution to defend against jailbreak attacks. arXiv preprint arXiv:2407.02855 1 (2), p. 3. Cited by: §A.2.2. Zhang et al. (2025c) Z. Zhang, F. Wang, X. Li, Z. Wu, X. Tang, H. Liu, Q. He, W. Yin, and S. Wang Catastrophic failure of llm unlearning via quantization. In International Conference on Learning Representations, Vol. 2025, p. 74925–74948. Cited by: §A.2.1, Table 4, §2, §3.1, Table 1, §4.1, §5. Zheng et al. (2025) H. Zheng, Z. Pang, Z. Deng, Y. Pu, Z. Zhu, X. Xia, J. Wei, et al. OFFSIDE: benchmarking unlearning misinformation in multimodal large language models. arXiv preprint arXiv:2510.22535. Cited by: Table 4, §1, §2, §3, §3.1, Table 1, §4.1. Zou et al. (2023) A. Zou, Z. Wang, N. Carlini, M. Nasr, J. Z. Kolter, and M. Fredrikson Universal and transferable adversarial attacks on aligned language models. arXiv preprint arXiv:2307.15043. Cited by: §A.2.2, §A.2.2, Table 4, §3.1, §4.1. Appendix A Appendix A.1 Supported Components and Variants Table 4: Overview of existing Open-MMUnlearning components and their available feature variants. The design is easily extensible, allowing users to seamlessly contribute new features. Component Variants Models LLaVA-1.5 (Liu et al., 2023) Qwen-2.5-VL (Bai et al., 2025) LLaVA-1.6 (Liu et al., 2024a) Gemma-3 (Team et al., 2025) Methods GradAscent GradDiff KL (Maini et al., 2024) IdkNLL (Maini et al., 2024) RMU (Li et al., 2024b) NPO (Zhang et al., 2024a) UNDIAL (Dong et al., 2025) MMUnlearner (Huo et al., 2025) MANU (Liu et al., 2025b) MIP-Editor (Li et al., 2026) SMFA (Zeng et al., 2025) VGID (Chen et al., 2026b) Datasets MLLMU (Liu et al., 2025a) FIUBench (Ma et al., 2025) CLEAR (Dontsov et al., 2025) SafeEraser (Chen et al., 2025) CoVUBench (Kwon et al., 2026) Metrics Forget Truth Ratio (Ma et al., 2025; Dontsov et al., 2025) Exact Match / APE (Ma et al., 2025) Answer Probability (Ma et al., 2025) Fill-in-the-Blank Accuracy (Liu et al., 2025a) Classification Accuracy (Liu et al., 2025a) ROUGE-1/2/L (Liu et al., 2025a) BLEU (Liu et al., 2025a) KS-Test (Ma et al., 2025; Dontsov et al., 2025) JS Distance (Dontsov et al., 2025) Forget Quality (Dontsov et al., 2025) Keyword Recall (Kwon et al., 2026) Semantic Dissimilarity (Kwon et al., 2026) Efficacy (Kwon et al., 2026) Divergence (Kwon et al., 2026) Attack Success Rate (ASR) (Chen et al., 2025) Refusal Rate (R) (Chen et al., 2025) Utility Model Utility (Ma et al., 2025; Dontsov et al., 2025) GPT-Eval (Ma et al., 2025; Chen et al., 2025) Fluency (Kwon et al., 2026) Specificity (Kwon et al., 2026) Generality (Kwon et al., 2026) LM-Eval (Liu et al., 2025a; Kwon et al., 2026) SARR (Chen et al., 2025) Robust Relearning (Hu et al., 2025; Zheng et al., 2025) Quantization (Zhang et al., 2025c) Probing (Lynch et al., 2024) Cross-Modal Leakage (Wang et al., 2026a) Pure-Text Jailbreak (Zou et al., 2023; Wei et al., 2023; Ma et al., 2024) FigStep (Gong et al., 2025) Image Rephrasing (Patil et al., 2025) SUA (Zhang et al., 2025b) LOSS (Yeom et al., 2018) ZLIB (Carlini et al., 2021) GradNorm (Wang et al., 2024) Min-K% (Shi et al., 2024) Min-K%++ (Zhang et al., 2025a) A.2 Details of Metrics This section specifies the attack procedures used for robustness evaluation. Let ℳu=ℳθuM_u=M_ _u denote the unlearned MLLM and ℳ0=ℳθ0M_0=M_ _0 its pre-unlearning copy. Unless stated otherwise, attacks are applied after unlearning with θu _u frozen. We distinguish four audit classes: model-side recovery attacks, which modify or train parameters of the post-unlearning model; input-side recovery attacks, which modify the query while keeping θu _u fixed; representation-side audits, which inspect frozen hidden representations using external probes; and score-side membership attacks, which require teacher-forced likelihoods or logits. For an answerability metric AfA_f on a held-out forget split, behavioral recovery is RA(ℳa)=Af(ℳa)−Af(ℳu),R_A(M_a)=A_f(M_a)-A_f(M_u), (3) where ℳaM_a denotes either an attacked model or the frozen unlearned model evaluated on attacked inputs. This recovery metric applies only to behavioral recovery attacks; representation-side and membership audits use their own task-specific metrics defined below. We report the corresponding change on a matched retain/control split alongside RAR_A, so that generic degradation is not mistaken for recovery. Membership attacks use separately defined score orientations and member/non-member controls rather than an unsigned “increase in membership score.” All attack-generation, calibration, and evaluation splits are disjoint unless explicitly stated otherwise. A.2.1 Model-side attacks. Quantization (Zhang et al., 2025c). After unlearning, we quantize the model to 4-bit weights, ℳu,4=4(ℳu)M_u,4=Q_4(M_u). Quantization can alter small unlearning-induced parameter changes and make residual target information accessible again. We use quantization as a target-data-free stress test, with target-related examples excluded from any calibration data. Recovery under this intervention provides evidence that a prior judgment of successful unlearning may have overstated the extent of forgetting. Because RTN, GPTQ, and AWQ impose different grids and calibration assumptions, we treat them as distinct attack configurations and record the quantizer, group size, calibration data, and compute dtype. For each configuration, we report RA(ℳu,4)R_A(M_u,4) together with the retain-set change. We interpret increased forget-set answerability relative to the full-precision unlearned model as evidence of target-specific recovery only when it is not accompanied by a commensurate nonspecific change on the matched control split. Relearning (Hu et al., 2024; Fan et al., 2025). Partition target-related data into an auxiliary attack split fauxD_f^aux and a recovery-test split ftestD_f^test, with faux∩ftest=∅D_f^aux _f^test= . The auxiliary split contains information correlated with the forgotten target but never the evaluated question–answer pairs. Under an explicit budget B (number of examples and optimizer steps, learning rate, trainable parameters, and, for LoRA, rank), the attacker computes δB∗=arg⁡minδ∈Δ⁡(B)ℒrelearn(θu+δ,faux) _B^*= _δ∈ (B)L_relearn( _u+δ;D_f^aux) (4) and evaluates RA(ℳθu+δB∗)R_A(M_ _u+ _B^*) on ftestD_f^test. We report recovery as a function of the budget and compare it with identically budgeted learning from a retain-only reference model. Systematically faster recovery from ℳuM_u than from the identically budgeted retain-only reference provides evidence consistent with residual target information; final accuracy after unconstrained retraining is not sufficient evidence. Probing Motivated by representation-level verification of residual knowledge, we freeze ℳuM_u and extract a fixed pooling of hidden states hℓ(x)h_ (x) at pre-specified residual-stream layers ℓ . On a probe-training split, we fit only a low-capacity linear map to predict the forgotten target identity, attribute, or answer label, Wℓ∗=arg⁡min⁡∑(x,z)∈probetrainW⁡ℒprobe(Whℓ(x),z),W_ ^*= _W _(x,z) _probe^trainL_probe(Wh_ (x),z), (5) and evaluate it on entity-disjoint probe-test data. We match target and control examples by modality, question type, and answer frequency, and include random-label and matched retain-only baselines. Above-control test performance indicates that target-related information remains linearly decodable from the frozen representation; this is interpreted as evidence of residual information rather than behavioral recovery. A.2.2 Input-side attacks. Jailbreak attack (Zhang et al., 2024b; Zou et al., 2023; Wei et al., 2023). Wrap a forget query in role-play, instruction-hierarchy, refusal-suppression, or adversarial templates while leaving θu _u unchanged. We define success as recovery of the correct forgotten answer, not merely production of an affirmative response, and use the paired clean-to-attack criterion above. Fixed templates form a low-budget black-box setting. Adaptive variants are evaluated separately according to their access assumptions: GCG (Zou et al., 2023) requires gradient access for discrete suffix optimization, AutoDAN (Liu et al., 2024b) performs population-based evolutionary search over adversarial prompts, and PAIR (Chao et al., 2025) uses iterative black-box interaction with an attacker model to refine jailbreak prompts. For target-aware white-box or score-access attack settings in which the auditor knows the target answer y, a suffix s may be optimized as s∗=arg⁡maxs∈⁡(B)logpθu(y∣I,q⊕s),s^*= _s (B) p_ _u(y I,q s), (6) where ⁡(B)S(B) enforces the method-specific token or query budget. Answer-prefix prefilling is treated as a separate access setting and the prefilled tokens are excluded from answer scoring. We report access level, query budget, number of restarts, and success for each variant rather than pooling heterogeneous threat models. Stealthy Universal Attack (SUA) (Zhang et al., 2025b). Learn one image-space perturbation δ on an attack-training split fatkD_f^atk and evaluate transfer on a disjoint split ftestD_f^test. For images represented in [0,1][0,1], the white-box perturbation is optimized by projected gradient descent under an explicit pixel budget ϵpx/255 _px/255: δ∗=arg⁡min‖δ‖∞≤ϵpx/255⁡(ℒans(δ)+λdℒden(δ)+λaℒalign(δ)),δ^*= _\|δ\|_∞≤ _px/255 (L_ans(δ)+ _dL_den(δ)+ _aL_align(δ) ), (7) where Iδ=clip⁡(I+δ,0,1)I_δ=clip(I+δ,0,1) and ℒans(δ) _ans(δ) =−(I,q,y)∼fatklog⁡pθu(y∣Iδ,q), =-E_(I,q,y) _f^atk p_ _u(y I_δ,q), (8) ℒden(δ) _den(δ) =−(I,q,y)∼fatklog⁡pθu(y∣D⁡(Iδ),q), =-E_(I,q,y) _f^atk p_ _u(y D(I_δ),q), (9) ℒalign(δ)=I∼fatk[1−cos⁡(E⁡(Iδ),E⁡(D⁡(Iδ)))]. _align(δ)=E_I _f^atk [1- \! (E(I_δ),E(D(I_δ)) ) ]. (10) Here D is a fixed denoiser and E is a fixed, named image encoder. The alignment term encourages the adversarial image and its denoised counterpart to remain close in the encoder representation space. The checkpoints and preprocessing of D and E, together with the weights λd _d and λa _a, are reported. The attacked image is evaluated with the original text query on unseen images using both mean answerability and paired recovery, together with perceptual quality and retain/control changes. If evaluated, a grey-box variant additionally specifies its zeroth-order estimator, search dimension, and query budget. Joint image–text SUA+ is reported as a separate threat model with independent image and suffix budgets. FigStep (Gong et al., 2025). FigStep is a black-box cross-modal jailbreak that transfers a target-seeking instruction from the textual channel to a typographic image. Following its original three-stage pipeline, we first rewrite the target query into an instruction-completion form, render this instruction together with an empty numbered list as an image, and pair the image with a fixed, apparently benign text prompt asking the model to complete the list. The attack requires neither gradients nor parameter access and leaves θu _u unchanged. In our unlearning audit, the typographic image encodes the request for the forgotten information rather than presenting the target answer itself. We count an attack as successful only when the generated response recovers the correct forgotten answer according to the same answerability criterion used for the clean query. We report the target recovery rate together with the corresponding clean-query baseline and retain/control performance, so that generic instruction-following changes are not mistaken for recovery. Image Rephrase attack (Patil et al., 2025). The Image Rephrase attack tests whether forgetting is tied to the particular visual realization used during deletion. For each forgotten image–question pair (I,q)(I,q), we replace I with a benchmark-provided rephrased image I′I that preserves the answer-relevant semantics while changing its visual appearance, and evaluate the frozen unlearned model on (I′,q)(I ,q). Following UnLOK-VQA, rephrased images are grouped by difficulty according to their semantic proximity to the original image, and results are reported separately for each available difficulty level rather than silently pooling them. The rephrased image contains no explicit target answer, and attack-generation data are kept disjoint from the examples used to perform unlearning. An attack succeeds when the model recovers the forgotten answer from (I′,q)(I ,q) under the same answerability rule used for the original pair. We report both attack success rate and paired recovery relative to the clean post-unlearning response, together with performance on matched retain/control examples. A.2.3 Output-Side Attacks. These attacks do not operate solely on generated outputs: LOSS and ZLIB require sequence-level teacher-forced target log-likelihoods; Min-K% additionally requires token-level target log-probabilities; and Min-K%++ requires the full next-token vocabulary distribution at every evaluated response position. We therefore report them under their respective likelihood- or logit-access threat models. For a multimodal context x=(I,q)x=(I,q) and response y, let ⁡(x,y)T(x,y) contain the L=|⁡(x,y)|L=|T(x,y)| evaluated response-token positions after excluding image, prompt, padding, and designated EOS tokens, and define ℓ¯u(x,y)=−1L∑t∈⁡(x,y)logpθu(yt∣x,y<t) _u(x,y)=- 1L _t (x,y) p_ _u(y_t x,y_<t) (11) be the mean response-token loss, and let PPLu(x,y)=exp⁡(ℓ¯u(x,y))PPL_u(x,y)= ( _u(x,y)). Examples with L=0L=0 are invalid and excluded with their count reported. Following OpenUnlearning (Dorna et al., 2026), each attack produces a scalar membership-evidence score. A complete post-unlearning MIA evaluation compares forgotten original-training members memD_mem with a disjoint holdout set nonD_non matched by modality, task, answer length, and target frequency; the retain set is not used as the non-member set when it also participated in training. Any decision threshold is selected only on a separate calibration split. We report threshold-free ROC-AUC and TPR at a pre-specified low FPR, with higher scores oriented to mean stronger membership evidence, and compare both quantities with a retain-only retrained reference under the same protocol. Successful deletion is indicated by attack performance approaching that reference, not by an uncalibrated change in mean score. Raw forget-set scores without non-member controls are explicitly labeled membership proxies rather than attack accuracy. LOSS attack (Yeom et al., 2018). Use the negative mean token loss SLOSS(x,y)=−ℓ¯u(x,y)S_LOSS(x,y)=- _u(x,y) (12) as the membership-evidence score, so that larger values indicate greater membership evidence. Mean rather than summed loss reduces direct answer-length confounding. Under a calibrated threshold τ, a sample is classified as a member when SLOSS(x,y)≥τS_LOSS(x,y)≥τ. ZLIB-normalized loss attack (Carlini et al., 2021). Let CZLIB(y)C_ZLIB(y) be the compressed byte length of a fixed UTF-8 serialization of the target response. Following the ZLIB-normalization principle used in memorization and membership evaluation, we use the fixed score SZLIB(x,y)=−ℓ¯u(x,y)CZLIB(y),S_ZLIB(x,y)=- _u(x,y)C_ZLIB(y), (13) where larger values indicate stronger membership evidence. This score definition is treated as an implementation convention and is held fixed across all compared models. In particular, we do not interpret ℓ¯u/CZLIB _u/C_ZLIB as the logarithm of PPLu/CZLIBPPL_u/C_ZLIB, since log⁡(PPLuCZLIB)=ℓ¯u−log⁡CZLIB \! ( PPL_uC_ZLIB )= _u- C_ZLIB defines a different ranking rule. The serialization and compressor settings are fixed across all examples. Min-K% attack (Shi et al., 2024). Obtain the target next-token log-probability at every evaluated response position, set K=max⁡(1,⌈kL⌉)K= (1, kL ), and average the K smallest values: SMinK(x,y)=1K∑t∈ℐKlog⁡pθu(yt∣x,y<t),S_MinK(x,y)= 1K _t _K p_ _u(y_t x,y_<t), (14) where ℐKI_K indexes the K least-likely target tokens after excluding image, prompt, padding, and designated EOS tokens. We use k=20%k=20\% unless otherwise specified. A larger (less-negative) score indicates greater membership evidence. Min-K%++ attack (Zhang et al., 2025a). Let pt(v)=pθu(v∣x,y<t)p_t(v)=p_ _u(v x,y_<t) be the full next-token distribution. At each response position, Min-K%++ standardizes the observed target-token log-probability relative to the model’s full next-token distribution. Specifically, define the probability-weighted mean and variance μt _t =∑v∈pt(v)log⁡pt(v), = _v p_t(v) p_t(v), (15) σt2 _t^2 =∑v∈pt(v)(log⁡pt(v)−μt)2, = _v p_t(v) ( p_t(v)- _t )^2, (16) not a uniform average over vocabulary entries. For the observed target token, define zt=log⁡pt(yt)−μtσt2+ε,SMinK++(x,y)=1K∑t∈Kzt,z_t= p_t(y_t)- _t _t^2+ , S_MinK++(x,y)= 1K _t _Kz_t, (17) where ε>0 >0 is fixed for numerical stability and KJ_K indexes the K smallest standardized target-token scores. Larger values indicate greater membership evidence. Token masking, k, ε , and score orientation are identical for member and non-member groups. GradNorm attack (Wang et al., 2024). GradNorm is a white-box membership inference attack based on the observation that examples encountered during training tend to induce smaller per-example gradients at the trained parameters. For a candidate multimodal example (x,y)(x,y), we compute the gradient of its mean response-token loss with respect to a fixed, pre-specified set of model parameters Θatk _atk: g⁡(x,y)=∇Θatkℓ¯u(x,y),Gp(x,y)=∥g⁡(x,y)∥p.g(x,y)= _ _atk _u(x,y), G_p(x,y)= g(x,y) _p. (18) We use −Gp(x,y)-G_p(x,y) as the membership-evidence score so that larger values indicate stronger evidence of membership. The attacked parameter blocks, norm order p, token mask, and gradient precision are fixed across member and non-member examples and reported with the results. As with the other membership attacks, forgotten training examples are compared with a distribution-matched holdout set, and we report ROC-AUC and TPR at a pre-specified low FPR. Because GradNorm requires back-propagation through the model for each candidate example, it is evaluated as a white-box attack and is not combined with black-box likelihood attacks without retaining its access label. A.3 Details of Unlearning Methods Evaluation A.3.1 Details of Unlearning Methods We describe the twelve unlearning methods currently integrated into Open-MMUnlearning: seven General Unlearning Methods and five MLLM-specific Unlearning Methods. Ten of these methods are included in the controlled comparison in Table 2. Let ℳθM_θ be the target MLLM, ℳθ0M_ _0 its frozen pre-unlearning copy, and fD_f and rD_r the forget and retain sets. For multimodal context x and response y=(y1,…,yT)y=(y_1,…,y_T), define the response-token negative log-likelihood (NLL) ℓθ(x,y)=−logpθ(y∣x)=−∑t=1Tlogpθ(yt∣x,y<t). _θ(x,y)=- p_θ(y x)=- _t=1^T p_θ(y_t x,y_<t). (19) Then ℒf(θ)=(x,y)∼f[ℓθ(x,y)]L_f(θ)=E_(x,y) _f[ _θ(x,y)], with ℒrL_r defined analogously. The following sections detail the objectives and update rules of these methods. Gradient Ascent (GA) (Maini et al., 2024). GA directly reduces the likelihood of the designated responses by maximizing their NLL. Under a minimization convention, ℒGA(θ)=−ℒf(θ).L_GA(θ)=-L_f(θ). (20) It supplies a strong forgetting signal but no preservation constraint; excessive optimization can therefore cause degenerate generation and broad utility loss. Gradient Difference (GradDiff) (Maini et al., 2024). GradDiff regularizes forget-set ascent with ordinary maximum-likelihood training on retained data: ℒGradDiff(θ)=−ℒf(θ)+λrℒr(θ),L_GradDiff(θ)=-L_f(θ)+ _rL_r(θ), (21) where λr≥0 _r≥ 0 controls the forgetting–retention trade-off. Its ability to preserve non-target behavior therefore depends on whether rD_r adequately covers the capabilities to be retained. KL Minimization (KL) (Maini et al., 2024). KL combines forget-set gradient ascent with a constraint that keeps the unlearned model’s predictions on retained data close to those of the frozen original model: ℒKL(θ)=−ℒf(θ)+α(x,y)∼r[1T∑t=1TDKL(pθ0(⋅∣x,y<t)∥pθ(⋅∣x,y<t))].L_KL(θ)=-L_f(θ)+α\,E_(x,y) _r [ 1T _t=1^TD_KL\! (p_ _0(· x,y_<t)\,\|\,p_θ(· x,y_<t) ) ]. (22) Here, α≥0α≥ 0 controls the preservation constraint. Unlike GradDiff, which minimizes the NLL of retained responses, KL directly limits changes to the original model’s output distribution on retained inputs. IdkNLL (Maini et al., 2024). IdkNLL replaces the original responses in the forget set with abstention responses, such as “I do not know,” while retaining the original responses in the retain set. Let fidkD_f^idk denote the resulting forget-set examples. The method minimizes ℒIdkNLL(θ)=(x,yidk)∼fidk[ℓθ(x,yidk)]+ℒr(θ).L_IdkNLL(θ)=E_(x,y_idk) _f^idk [ _θ(x,y_idk) ]+L_r(θ). (23) This objective encourages abstention on forget-set queries while preserving performance on retained examples. Because abstention does not itself establish that the underlying knowledge has been removed, its effectiveness must be assessed with the same forgetting and robustness evaluations used for the other methods. Negative Preference Optimization (NPO) (Zhang et al., 2024a). NPO regards each forget response as a dispreferred completion relative to the frozen model. Let rθ(x,y)=log⁡pθ(y∣x)−log⁡pθ0(y∣x)r_θ(x,y)= p_θ(y x)- p_ _0(y x). Its forget objective is ℒNPO(θ)=−2βf[log⁡σ⁡(−βrθ(x,y))],L_NPO(θ)=- 2βE_D_f [ σ\! (-β r_θ(x,y) ) ], (24) with β>0β>0. Unlike GA, its gradient saturates once a target response becomes sufficiently less likely than under θ0 _0, limiting runaway updates. Our retention-regularized variant optimizes ℒNPO+R(θ)=ℒNPO(θ)+λrℒr(θ).L_NPO+R(θ)=L_NPO(θ)+ _rL_r(θ). (25) Representation Misdirection for Unlearning (RMU) (Li et al., 2024b). RMU modifies hidden states rather than output probabilities. Let hθ(l)(x)h^(l)_θ(x) denote the hidden representations at the selected layer and token positions used by RMU. It first samples a unit direction and constructs a fixed random control vector ∼Unif⁡(d−1),=c,‖2=1, gatheredu (S^d-1),\\ v=cu,\\ \|u\|_2=1, gathered (26) where d is the hidden-state dimension and c>0c>0 controls the target norm. The same control vector is used for the forget examples associated with the selected intervention layer. The forget-state loss pulls their representations toward this content-independent target: ℒfRMU(θ)=(x,y)∼f[‖hθ(l)(x)−‖22].L_f^RMU(θ)=E_(x,y) _f\! [ \|h^(l)_θ(x)-v \|_2^2 ]. (27) To preserve non-target behavior, the retain-state loss anchors the corresponding representations to the frozen pre-unlearning model: ℒrRMU(θ)=(x,y)∼r[‖hθ(l)(x)−hθ0(l)(x)‖22].L_r^RMU(θ)=E_(x,y) _r\! [ \|h^(l)_θ(x)-h^(l)_ _0(x) \|_2^2 ]. (28) The complete objective is ℒRMU(θ)=ℒfRMU(θ)+λrℒrRMU(θ),L_RMU(θ)=L_f^RMU(θ)+ _rL_r^RMU(θ), (29) where λr≥0 _r≥ 0 controls representation preservation. The selected layer, token positions, and trainable modules determine whether the intervention mainly affects visual, cross-modal, or linguistic representations. Unlike the adaptive target used by MIP-Editor’s RMisU below, standard RMU uses the fixed-norm control vector ccu. UNDIAL (Dong et al., 2025). UNDIAL performs self-distillation from an adjusted teacher distribution derived from the frozen model. At response position t, it lowers the frozen model’s logit for the ground-truth token yty_t: z~t,v=z0t,v−γ[v=yt],p~t=softmax⁡(~t), gathered z_t,v=z^0_t,v-γ 1[v=y_t],\\ p_t=softmax( z_t), gathered (30) and minimizes token-wise cross-entropy to this fixed target: ℒUNDIAL(θ)=f[∑t=1TH(p~t,pθ(⋅∣x,y<t))].L_UNDIAL(θ)=E_D_f\! [ _t=1^TH\! ( p_t,p_θ(· x,y_<t) ) ]. (31) The adjustment strength γ>0γ>0 controls token suppression. Because p~t p_t is fixed, cross-entropy and KL(p~t∥pθ)KL( p_t\|p_θ) differ only by the constant entropy of the teacher. UNDIAL can adjust every response token or only a selected subset (e.g., named entities), while retaining the teacher’s relative probabilities for all unadjusted tokens. The equation above is the original UNDIAL objective. Our retention-regularized implementation instead optimizes ℒUNDIAL+R(θ)=λuℒUNDIAL(θ)+ℒr(θ),L_UNDIAL+R(θ)= _uL_UNDIAL(θ)+L_r(θ), (32) where λu _u weights adjusted-logit distillation against retain-set learning. MMUnlearner (Huo et al., 2025). MMUnlearner targets entity-level visual knowledge while preserving non-target visual concepts and textual knowledge about the same entity. Let T contain multimodal examples of target concepts and let P combine (i) text-only examples of those concepts, (i) multimodal examples of non-target concepts, and (i) text-only examples of non-target concepts. For dataset D, it approximates parameter saliency with the diagonal empirical Fisher ⁡(θ0,ℒ,)=diag≈[∇θℒ(θ0)]2,S( _0,L,D)=F^D_diag≈ [ _θL_D( _0) ]^2, (33) where the square is element-wise. The resulting saliency-based update mask is =[⁡(θ0,ℒ,)⁡(θ0,ℒ,)+ϵ≥βs].m= 1\! [ S( _0,L,T)S( _0,L,P)+ε≥ _s ]. (34) The mask is applied only to the forget component, while preserved VQA data supply an ordinary retain gradient. The resulting update direction is MMU(θ)=−⊙∇θℒf(θ)+∇θℒr(θ),θ←θ−ηMMU. gatheredg_MMU(θ)=-\,m _θL_f(θ)+ _θL_r(θ),\\ θ←θ- _MMU. gathered (35) The saliency ratio—not forget-gradient magnitude alone—ensures that parameters important to preserved visual or textual knowledge are not selected. Modality-Aware Neuron Unlearning (MANU) (Liu et al., 2025b). MANU follows a locate-then-prune procedure over MLP neurons in both the vision and language modules. For neuron n, dataset D, and activations zm(d)z_m(d) under m∈multi,textm∈\multi,text\, let mD_m denote the examples presented under modality condition m. We define the mean absolute activation Z¯m=|m|−1∑d∈m|zm(d)| Z_m=|D_m|^-1 _d _m|z_m(d)|, the activation count Nm=|d∈m:|zm(d)|>τ|N_m=|\d _m:|z_m(d)|>τ\|, the activation dispersion Vm=|m|−1∑d∈m(zm(d)−Z¯m)2V_m=|D_m|^-1 _d _m(z_m(d)- Z_m)^2, and the activation energy Em=∑d∈mzm(d)2E_m= _d _mz_m(d)^2. MANU combines these quantities into four complementary modality-specific importance measures: Iabs(,n)=|Z¯multi−Z¯text|Z¯multi+Z¯text+ϵ,Ifreq(,n)=|Nmulti−Ntext|Nmulti+Ntext+ϵ,Ivar(,n)=Vmulti+Vtext,Irms(,n)=|Emulti−Etext|Emulti+Etext+ϵ. gatheredI_abs(D,n)= | Z_multi- Z_text| Z_multi+ Z_text+ε,\\ I_freq(D,n)= |N_multi-N_text|N_multi+N_text+ε,\\ I_var(D,n)= V_multi+V_text,\\ I_rms(D,n)= |E_multi-E_text|E_multi+E_text+ε. gathered (36) Their sum, I⁡(,n)=∑kIk(,n)I(D,n)= _kI_k(D,n), is used to compare the importance of each neuron on the forget and retain sets: Sn=I⁡(f,n)I⁡(r,n)+ϵ,α=n:Sn is in the top α%. gatheredS_n= I(D_f,n)I(D_r,n)+ε,\\ N_α=\n:S_n is in the top α\%\. gathered (37) The neurons with the highest forget-to-retain importance scores are then pruned. Writing WnW_n for the outgoing weights or contribution associated with neuron n, the pruning operation is Wn′=,n∈α,Wn,otherwise.W _n= cases0,&n _α,\\ W_n,&otherwise. cases (38) MANU therefore performs unlearning through neuron localization and pruning rather than gradient-based fine-tuning. The forget-to-retain importance ratio reduces the likelihood of pruning neurons that are also important for retained knowledge, while the paired text-only and multimodal statistics capture modality-dependent activation behavior. MIP-Editor (Li et al., 2026). MIP-Editor localizes structured information-flow paths rather than isolated neurons. For selected text-branch activations =(wi11,…,wiNN)w=(w^1_i_1,…,w^N_i_N), define FT()=p⁡(Y∣T,wi11,…,wiNN)F_T(w)=p(Y T,w^1_i_1,…,w^N_i_N). Following the notation of the original method, its inter-layer gradient-integration score is IGI⁡()=∑n=1Nw~inn∫0w~inn∑l=1N∂FT(αi11,…,αiNN)∂willdαinn,IGI(w)= _n=1^N w^n_i_n _0 w^n_i_n _l=1^N ∂ F_T(α^1_i_1,…,α^N_i_N)∂ w^l_i_l\,dα^n_i_n, (39) where αinnα^n_i_n interpolates the activation of the nnth selected neuron from zero to its observed value w~inn w^n_i_n; the implementation uses the paper’s Riemann approximation. For visual activations z and G⁡()=log⁡p⁡(Y∣I,T,)G(z)= p(Y I,T,z), inter-layer Fisher integration (IFI) replaces the gradient by its square; with m Riemann points, IFI⁡()≈∑n=1Nz~inn∑k=1m∑l=1N(∂G⁡((k/m)~)∂zill)2.IFI(z)≈ _n=1^N z^n_i_n _k=1^m _l=1^N ( ∂ G((k/m) z)∂ z^l_i_l )^2. (40) For a candidate path xP_x, define its modality-specific score and selected path as ⁡(x)=IGI⁡(x),x=T,IFI⁡(x),x=(I,T),x⋆=arg⁡maxx⁡(x). gatheredS(P_x)= casesIGI(P_x),&x=T,\\ IFI(P_x),&x=(I,T), cases\\ P_x = _P_xS(P_x). gathered (41) Denoting the resulting paths by T⋆=~ll=1LtP_T =\ w_l\_l=1^L_t and I,T⋆=~ll=1LvP_I,T =\ z_l\_l=1^L_v, the first editing step blocks their activations: ~l←(l=1,…,Lt),~l←(l=1,…,Lv). gathered w_l 0 (l=1,…,L_t),\\ z_l 0 (l=1,…,L_v). gathered (42) Direct pruning may also destroy retain knowledge carried by overlapping paths. MIP-Editor therefore freezes all off-path parameters and applies Representation Misdirection Unlearning (RMisU) only to the selected neurons. For forget input xfx_f, it samples ∼Unif⁡(d−1)u (S^d-1) and constructs the adaptive target f=λ‖hθ0(l)(xf)‖2.v_f=λ\|h^(l)_ _0(x_f)\|_2u. (43) The original RMisU editing loss combines forget-state misdirection and retain-state anchoring: ℒMIP=xf∼f‖hθ(l)(xf)−f‖22+γxr∼r‖hθ(l)(xr)−hθ0(l)(xr)‖22,with gradients restricted to T⋆∪I,T⋆. gatheredL_MIP=E_x_f _f\|h^(l)_θ(x_f)-v_f\|_2^2+ _x_r _r\|h^(l)_θ(x_r)-h^(l)_ _0(x_r)\|_2^2,\\ with gradients restricted to P_T _I,T . gathered (44) Thus, activation blocking (the pruning step in the original method) initializes forgetting, whereas localized RMisU restores general behavior without freely updating the rest of the model. Sculpted Memory Forgetting Adapter (SMFA) (Zeng et al., 2025). SMFA confines forgetting to low-rank adapter updates. Let fidkD_f^idk be the forget set relabeled with refusal responses and let rfewD_r^few be a few-shot retain subset. It first learns a Memory Forgetting Adapter (MFA) ΔWf W_f by minimizing cross-entropy on their union: ΔWf=arg⁡minΔW⁡ℒ⁡(fidk∪rfew,θ0+ΔW). W_f= _ WL\! (D_f^idk _r^few, _0+ W ). (45) It independently fine-tunes an adapter on a few-shot retain subset to obtain a retaining anchor ΔWa W_a. For entry (i,j)(i,j), two indicators identify a harmful forgetting update: directional conflict and dominance in magnitude, Cij=[ΔWa,ijΔWf,ij<0],Rij=[kρ|ΔWa,ij|<|ΔWf,ij|], gatheredC_ij= 1[ W_a,ij W_f,ij<0],\\ R_ij= 1[kρ| W_a,ij|<| W_f,ij|], gathered (46) where k≥0k≥ 0 is the masking strength and ρ=‖ΔWf‖F‖ΔWa‖F+ϵρ= \| W_f\|_F\| W_a\|_F+ε (47) compensates for the global scale difference between the two adapters. With =⊙M=C , SMFA removes entries satisfying both criteria: ΔWSMFA=[−(⊙)]⊙ΔWf,θu=θ0+ΔWSMFA. gathered W_SMFA= [1-(C ) ] W_f,\\ _u= _0+ W_SMFA. gathered (48) The conjunction is important: an oppositely directed but negligible forget update need not be removed. The backbone remains frozen, making the intervention modular and reversible while the retaining anchor protects unrelated memory and general visual understanding. Visual-Noise Guided In-Context Distillation (VGID) (Chen et al., 2026b). VGID constructs an unlearning-oriented teacher from the frozen original model by perturbing the image and prepending an in-context unlearning instruction to each forget query. For xf=(If,qf)x_f=(I_f,q_f), let ϕP_φ denote the visual perturbation and u the textual instruction. The teacher distribution and student objective are pT(⋅∣xf)=pθ0(⋅∣ϕ(If),u⊕qf),p_T(· x_f)=p_ _0\! (· _φ(I_f),u q_f ), (49) ℒVGID(θ)= _VGID(θ)= αxf∼f[DKL(pT(⋅∣xf)∥pθ(⋅∣xf))] α\,E_x_f _f [D_KL\! (p_T(· x_f)\,\|\,p_θ(· x_f) ) ] (50) +βxr∼r[DKL(pθ0(⋅∣xr)∥pθ(⋅∣xr))]. +β\,E_x_r _r [D_KL\! (p_ _0(· x_r)\,\|\,p_θ(· x_r) ) ]. The student receives the original forget input, while the intervention-induced teacher supplies its distillation target; the retain-set term preserves the original model’s behavior. Thus, VGID transfers the effect of an inference-time intervention into model parameters without requiring a separately trained teacher. A.3.2 Hyperparameter Search For the evaluated MLLMU forget setting, the reported experiments cover the following hyperparameter configurations for the ten unlearning methods in Table 2. Distinct parameter combinations are counted as configurations, whereas checkpoints saved at different training stages of the same configuration are counted separately only when reporting evaluated checkpoints. • GA: We evaluate three learning rates, 1,2,3×10−5\1,2,3\× 10^-5, yielding 33 configurations. • GradDiff: We evaluate three learning rates, 1,2,3×10−5\1,2,3\× 10^-5, and three retain coefficients, λr∈0.5,1,2 _r∈\0.5,1,2\, yielding 3×3=93× 3=9 configurations. • KL: We evaluate three learning rates, 1,2,3×10−5\1,2,3\× 10^-5, and three coefficients, α∈0.5,1,2α∈\0.5,1,2\, yielding 3×3=93× 3=9 configurations. • NPO: We evaluate three learning rates, 1,2,3×10−5\1,2,3\× 10^-5, three retain coefficients, λr∈0,0.5,1 _r∈\0,0.5,1\, and three preference temperatures, β∈0.6,0.9,1.2β∈\0.6,0.9,1.2\, yielding 3×3×3=273× 3× 3=27 configurations. • RMU: We evaluate three learning rates, 1,2,3×10−5\1,2,3\× 10^-5, three steering magnitudes, c∈1,10,100c∈\1,10,100\, and three intervention layers, l∈6,11,16l∈\6,11,16\, yielding 3×3×3=273× 3× 3=27 configurations. • UNDIAL: We evaluate three learning rates, 10−5,10−4,3×10−4\10^-5,10^-4,3× 10^-4\, three distillation weights, λu∈1,2,5 _u∈\1,2,5\, and three logit-adjustment coefficients, γ∈3,10,30γ∈\3,10,30\, yielding 3×3×3=273× 3× 3=27 configurations. • MMUnlearner: We evaluate three learning rates, 1,2,3×10−5\1,2,3\× 10^-5, and three localization thresholds, 0.5,1,2\0.5,1,2\, yielding 3×3=93× 3=9 configurations. • MANU: We evaluate three pruning parameters, 2,5,10\2,5,10\, yielding 33 configurations. • MIP-Editor: We evaluate three learning rates, 10−4,5×10−4,10−3\10^-4,5× 10^-4,10^-3\, three Top-k values, 3,5,10\3,5,10\, and three steering coefficients, 0.5,1,2\0.5,1,2\, yielding 3×3×3=273× 3× 3=27 configurations. • SMFA: We evaluate three learning rates, 5×10−5,10−4,2×10−4\5× 10^-5,10^-4,2× 10^-4\, and three multimodal scaling coefficients, k∈2.5,5,10k∈\2.5,5,10\, yielding 3×3=93× 3=9 configurations. In total, we trained 150150 distinct hyperparameter configurations across the ten unlearning methods. A.3.3 Metric Aggregation This section defines the aggregation used in Table 2. Component scores are mapped to [0,1][0,1] and direction-aligned so that larger values indicate better performance. All aggregates are computed from unrounded values; rounding is applied only for display. Valid zeros are retained, whereas an aggregate with a missing required component is reported as “–”. Forget Quality. For a forget evaluation split d, let adfilla_d^fill and adclsa_d^cls denote cloze and classification accuracy in percent, and let rdRL∈[0,1]r_d^RL∈[0,1] denote ROUGE-L. The task-based forgetting score is Fdtask=13[(1−adfill100)+(1−adcls100)+(1−rdRL)].F_d^task= 13 [ (1- a_d^fill100 )+ (1- a_d^cls100 )+ (1-r_d^RL ) ]. (51) We first average the scores on the multimodal forget set and the held-out forget-target test set: Fmultitask=Fdirect-multitask+Ftesttask2.F_multi^task= F_direct -multi^task+F_test^task2. (52) Let TTRT_TR be the normalized Truth Ratio, pansp_ans the answer probability, and DKSD_KS and DJSD_JS the KS-Test statistic and JS distance against the matched Retain-Only reference. Forget Quality is FQ=15[Fmultitask+TTR+(1−pans)+(1−DKS)+(1−DJS)].FQ= 15 [F_multi^task+T_TR+(1-p_ans)+(1-D_KS)+(1-D_JS) ]. (53) BLEU is reported as an auxiliary generation metric but is not included in this aggregate. Model Utility. For modality m∈uni,multim∈\uni,multi\ and retain split s∈retain-shared,retain-celebritys∈\ retain-shared, retain-celebrity\, we define Um,s=13(am,sfill100+am,scls100+rm,sRL).U_m,s= 13 ( a_m,s^fill100+ a_m,s^cls100+r_m,s^RL ). (54) The retain-set component averages the four modality–split combinations: Uretain=14∑m∈uni,multi∑s∈retain-shared,retain-celebrityUm,s.U_retain= 14 _m∈\uni,multi\ _s∈\ retain-shared, retain-celebrity\U_m,s. (55) Let ℬB contain MMBench, M-Vet, POPE, ScienceQA, VizWiz, GQA, and VQAv2, and let aba_b be the accuracy in percent on benchmark b. The general-capability component and Model Utility are Ugeneral=17∑b∈ℬab100,MU=Uretain+Ugeneral2.U_general= 17 _b a_b100, = U_retain+U_general2. (56) Model-side robustness. We apply the task-based forgetting function above to the multimodal evaluations after quantization, targeted relearning, and representation probing. Let the resulting scores be Q, L, P16P_16, and P24P_24, respectively. The two probe layers are averaged before assigning equal weight to the three intervention families: Model=13(Q+L+P16+P242).Model= 13 (Q+L+ P_16+P_242 ). (57) Input-side robustness. Cross-modal leakage is measured by evaluating the corresponding pure-text forget split without the image. Its direction-aligned score is ICrossModal=Fdirect-unitask.I_CrossModal=F_direct -uni^task. (58) For each other input-side attack a, we convert its target-recovery or attack-success rate ASRaASR_a, expressed in percent, to Ia=1−ASRa/100I_a=1-ASR_a/100. Input-Side Robustness averages five components: Input=15(ICrossModal+IFigStep+IJailbreak+IImageRephrase+ISUA).Input= 15 (I_CrossModal+I_FigStep+I_Jailbreak+I_ImageRephrase+I_SUA ). (59) The image–textual jailbreak variant is excluded from this aggregate. Output-side robustness. Output-Side Robustness uses only the multimodal evaluation of five membership-inference attacks. For attack j, its ROC-AUC is converted to a score measuring proximity to random guessing: Oj=clip⁡(1−2|AUCj,multi−0.5|,0,1).O_j=clip (1-2 |AUC_j,multi-0.5 |,0,1 ). (60) An AUC of 0.50.5 therefore receives a score of 11, while an AUC of 00 or 11 receives a score of 00. We then compute Output=15(OLOSS+OZLIB+OGradNorm+OMin-K%+OMin-K%++).Output= 15 (O_LOSS+O_ZLIB+O_GradNorm+O_Min -K\%+O_Min -K\%++ ). (61) Checkpoint selection and final aggregation. For each unlearning method, we select the checkpoint with the highest mean of Forget Quality and Model Utility: Sselect=FQ+MU2.S_select= FQ+MU2. (62) The selection scores are computed on the same evaluation splits used to report Table 2; no separate validation split is used. Robustness does not enter checkpoint selection. We reserve it for post-selection stress testing so that checkpoint selection is not optimized for the particular attacks and interventions used in the robustness audit. For the selected checkpoint, the three robustness dimensions and the final method-level score receive equal weight: Robustness=Model+Input+Output3,Overall=FQ+MU+Robustness3.Robustness= Model+Input+Output3, = FQ+MU+Robustness3. (63) Because Forget Quality and Model Utility are used both for selection and for reporting on the same splits, their reported values and the resulting Overall score are selection-conditioned and may be optimistic. We therefore interpret small differences between methods descriptively. A.3.4 Additional Results for Unlearning Method Evaluation To unpack the aggregate results in Table 2, the following tables report the individual measurements for Forget Quality and Model Utility, together with the model-, input-, and output-side Robustness scores. These breakdowns show how the individual scores contribute to the aggregate results. Table 5: Detailed Forget Quality results on the forget set and held-out forget-target test set. Cloze and Cls. report accuracy (%), while Gene. reports ROUGE-L. Lower scores indicate stronger forgetting. Finetune and Retain-Only serve as reference models; bold and underlined values indicate the best and second-best results among the unlearning methods, respectively. Methods Forget Set Test Set Cloze ↓ Cls. ↓ Gene. ↓ Cloze ↓ Cls. ↓ Gene. ↓ Reference Models Finetune 13.54 38.78 0.242 11.46 27.35 0.149 Retain-Only 12.50 36.73 0.207 13.54 40.41 0.146 General Unlearning Methods GA (Maini et al., 2024) 0.00 0.00 0.003 0.00 0.00 0.003 GD (Maini et al., 2024) 8.33 4.08 0.155 10.42 7.76 0.130 KL (Maini et al., 2024) 14.58 37.14 0.233 9.38 40.82 0.144 RMU (Li et al., 2024b) 12.50 31.43 0.219 7.29 29.39 0.149 NPO (Zhang et al., 2024a) 12.50 30.61 0.209 6.25 37.96 0.130 UNDIAL (Dong et al., 2025) 9.38 42.45 0.252 6.25 35.51 0.168 MLLM-specific Unlearning Methods MMUnlearner (Huo et al., 2025) 8.33 37.55 0.186 7.29 33.06 0.124 MANU (Liu et al., 2025b) 12.50 41.22 0.402 10.42 40.41 0.248 MIP-Editor (Li et al., 2026) 14.58 40.82 0.217 10.42 35.51 0.152 SMFA (Zeng et al., 2025) 13.54 31.84 0.139 9.38 34.29 0.119 Table 6: Detailed Forget Quality results for normalized Truth Ratio, answer probability, KS-Test, and JS Distance. Higher Truth Ratio and lower values for the other metrics are better. Finetune and Retain-Only serve as reference models; bold and underlined values indicate the best and second-best results among the unlearning methods, respectively. Methods Truth Ratio ↑ Prob. ↓ KS-Test ↓ JS Dist. ↓ Reference Models Finetune 0.570 0.499 0.140 0.281 Retain-Only 0.452 0.254 0.150 0.337 General Unlearning Methods GA (Maini et al., 2024) 0.393 0.000 0.330 0.190 GD (Maini et al., 2024) 0.432 0.168 0.130 0.130 KL (Maini et al., 2024) 0.583 0.489 0.100 0.294 RMU (Li et al., 2024b) 0.586 0.493 0.080 0.280 NPO (Zhang et al., 2024a) 0.623 0.345 0.060 0.269 UNDIAL (Dong et al., 2025) 0.652 0.355 0.140 0.324 MLLM-specific Unlearning Methods MMUnlearner (Huo et al., 2025) 0.467 0.190 0.130 0.257 MANU (Liu et al., 2025b) 0.583 0.469 0.130 0.281 MIP-Editor (Li et al., 2026) 0.617 0.489 0.060 0.252 SMFA (Zeng et al., 2025) 0.570 0.488 0.070 0.262 Table 7: Detailed Model Utility results on the multimodal Retain-Shared and Retain-Celebrity sets. Cloze and Cls. report accuracy (%), while Gene. reports ROUGE-L. Higher scores are better. Finetune and Retain-Only serve as reference models; bold and underlined values indicate the best and second-best results among the unlearning methods, respectively. Methods Retain-Shared Retain-Celebrity Cloze ↑ Cls. ↑ Gene. ↑ Cloze ↑ Cls. ↑ Gene. ↑ Reference Models Finetune 8.04 36.74 0.231 12.75 29.90 0.171 Retain-Only 16.52 41.47 0.261 16.34 50.91 0.169 General Unlearning Methods GA (Maini et al., 2024) 0.11 0.00 0.003 0.00 0.00 0.000 GD (Maini et al., 2024) 15.63 0.85 0.215 9.48 14.75 0.145 KL (Maini et al., 2024) 19.53 31.70 0.234 12.42 32.38 0.168 RMU (Li et al., 2024b) 17.41 27.01 0.225 13.40 25.59 0.158 NPO (Zhang et al., 2024a) 18.75 23.66 0.211 6.86 19.32 0.167 UNDIAL (Dong et al., 2025) 15.74 35.31 0.267 11.44 49.22 0.230 MLLM-specific Unlearning Methods MMUnlearner (Huo et al., 2025) 16.85 28.13 0.184 14.05 19.19 0.148 MANU (Liu et al., 2025b) 19.08 33.30 0.430 13.40 33.03 0.305 MIP-Editor (Li et al., 2026) 17.86 25.27 0.214 13.73 45.56 0.162 SMFA (Zeng et al., 2025) 18.64 29.82 0.136 7.84 23.37 0.112 Table 8: Detailed Model Utility results on seven general capability benchmarks. All scores are reported as percentages, and higher scores are better. SQA denotes ScienceQA. Finetune and Retain-Only serve as reference models; bold and underlined values indicate the best and second-best results among the unlearning methods, respectively. Methods MMBench ↑ M-Vet ↑ POPE ↑ SQA ↑ VizWiz ↑ GQA ↑ VQAv2 ↑ Reference Models Finetune 80.00 16.00 82.00 56.00 2.60 52.00 79.20 Retain-Only 80.00 24.00 78.00 62.00 8.40 44.00 79.20 General Unlearning Methods GA (Maini et al., 2024) 0.00 0.00 0.00 36.00 0.00 0.00 0.00 GD (Maini et al., 2024) 76.00 16.00 82.00 56.00 5.20 56.00 81.20 KL (Maini et al., 2024) 78.00 16.00 82.00 56.00 0.60 34.00 53.60 RMU (Li et al., 2024b) 80.00 18.00 82.00 60.00 9.00 52.00 81.20 NPO (Zhang et al., 2024a) 72.00 20.00 82.00 56.00 0.60 38.00 63.20 UNDIAL (Dong et al., 2025) 80.00 20.00 88.00 60.00 11.00 56.00 83.20 MLLM-specific Unlearning Methods MMUnlearner (Huo et al., 2025) 78.00 22.00 78.00 58.00 8.20 54.00 81.00 MANU (Liu et al., 2025b) 80.00 16.00 82.00 56.00 4.60 54.00 81.20 MIP-Editor (Li et al., 2026) 80.00 20.00 84.00 64.00 11.00 56.00 83.20 SMFA (Zeng et al., 2025) 0.00 0.00 0.00 6.00 0.00 0.00 0.00 Table 9: Detailed Model-Side Robustness results for quantization, targeted relearning, and probing at layers 16 and 24. Higher scores are better. Finetune and Retain-Only serve as reference models; bold and underlined values indicate the best and second-best results among the unlearning methods, respectively. Methods Quant. ↑ Relearn. ↑ Probe-L16 ↑ Probe-L24 ↑ Reference Models Finetune 0.752 0.759 0.993 0.996 Retain-Only 0.768 0.760 0.984 0.993 General Unlearning Methods GA (Maini et al., 2024) 0.999 0.997 0.992 0.991 GD (Maini et al., 2024) 0.915 0.772 0.994 0.993 KL (Maini et al., 2024) 0.727 0.780 0.984 0.997 RMU (Li et al., 2024b) 0.748 0.800 0.989 0.990 NPO (Zhang et al., 2024a) 0.838 0.786 0.994 0.996 UNDIAL (Dong et al., 2025) 0.759 0.722 0.996 0.999 MLLM-specific Unlearning Methods MMUnlearner (Huo et al., 2025) 0.906 0.794 0.965 0.971 MANU (Liu et al., 2025b) 0.866 0.666 0.976 0.998 MIP-Editor (Li et al., 2026) 0.761 0.752 0.946 0.995 SMFA (Zeng et al., 2025) 0.866 0.806 0.994 0.997 Table 10: Detailed Input-Side Robustness results for cross-modal queries, FigStep, jailbreak prompts, image rephrasing, and SUA. Higher scores indicate stronger resistance to these inputs. Finetune and Retain-Only serve as reference models; bold and underlined values indicate the best and second-best results among the unlearning methods, respectively. Methods Cross-Modal FigStep,↑ Jailbreak,↑ Image SUA,↑ Leakage,↑ Rephrasing,↑ Reference Models Finetune 0.574 0.860 0.740 0.688 0.292 Retain-Only 0.611 0.930 0.797 0.667 0.438 General Unlearning Methods GA (Maini et al., 2024) 0.853 1.000 1.000 1.000 1.000 GD (Maini et al., 2024) 0.594 0.900 0.860 0.833 0.646 KL (Maini et al., 2024) 0.582 0.880 0.763 0.780 0.333 RMU (Li et al., 2024b) 0.596 0.890 0.767 0.777 0.292 NPO (Zhang et al., 2024a) 0.625 0.790 0.807 0.747 0.458 UNDIAL (Dong et al., 2025) 0.574 0.920 0.853 0.830 0.479 MLLM-specific Unlearning Methods MMUnlearner (Huo et al., 2025) 0.656 0.830 0.847 0.863 0.583 MANU (Liu et al., 2025b) 0.588 0.850 0.783 0.777 0.292 MIP-Editor (Li et al., 2026) 0.562 0.890 0.787 0.810 0.333 SMFA (Zeng et al., 2025) 0.598 0.850 0.723 0.800 0.333 Table 11: Detailed Output-Side Robustness results based on five membership inference attacks. Each score measures how close the attack’s ROC-AUC is to random guessing; higher scores are better. Finetune and Retain-Only serve as reference models; bold and underlined values indicate the best and second-best results among the unlearning methods, respectively. Methods LOSS ↑ ZLIB ↑ GradNorm ↑ Min-K% ↑ Min-K%++ ↑ Reference Models Finetune 0.840 0.748 0.536 0.969 0.965 Retain-Only 0.954 0.856 0.456 0.884 0.846 General Unlearning Methods GA (Maini et al., 2024) 0.874 0.823 0.033 0.828 0.828 GD (Maini et al., 2024) 0.872 0.969 0.000 0.665 0.590 KL (Maini et al., 2024) 0.855 0.762 0.444 0.971 0.969 RMU (Li et al., 2024b) 0.804 0.709 0.487 0.924 0.928 NPO (Zhang et al., 2024a) 0.907 0.798 0.000 0.961 0.902 UNDIAL (Dong et al., 2025) 0.831 0.637 0.000 0.940 0.977 MLLM-specific Unlearning Methods MMUnlearner (Huo et al., 2025) 0.712 0.800 0.280 0.594 0.562 MANU (Liu et al., 2025b) 0.806 0.720 0.423 0.940 0.997 MIP-Editor (Li et al., 2026) 0.853 0.759 0.494 0.972 0.995 SMFA (Zeng et al., 2025) 0.847 0.738 0.399 0.972 0.968 A.3.5 Case Study These four paired case studies illustrate the tradeoff between suppressing a target answer and preserving useful responses. GA omits the target answer in all four forget cases, but also omits it in all four retain cases and often produces repetitive text. MIP-Editor and UNDIAL preserve the target answer in every retain case and suppress it in three of four forget cases; both still reveal it in the Eva Lindberg case. GD and RMU preserve all four retain targets but reveal the forget target in two cases each. The examples highlight distinct failure modes across methods and serve as qualitative illustrations rather than estimates of overall performance. Figure 3: Four paired case studies of MLLM unlearning. Green Forgotten indicates that the target answer is absent; red Retained indicates that it is present. Figure 4: Four paired case studies of MLLM unlearning. Green Forgotten indicates that the target answer is absent; red Retained indicates that it is present. Figure 5: Four paired case studies of MLLM unlearning. Green Forgotten indicates that the target answer is absent; red Retained indicates that it is present. Figure 6: Four paired case studies of MLLM unlearning. Green Forgotten indicates that the target answer is absent; red Retained indicates that it is present. A.4 Details of Unlearning Metric Evaluation This section provides the formal definitions used to evaluate the faithfulness and robustness of MLLM unlearning metrics. For every evaluated metric, we normalize its range to [0,1][0,1] and align its direction according to the corresponding evaluation objective. A.4.1 Faithfulness Let m⁡(M,F)m(M;D_F) denote the score produced by metric m for model M on the forget set FD_F, with a larger value indicating stronger evidence that the target knowledge is present. Applying the metric to the positive model pool P and the negative model pool N produces two score distributions: +m=m⁡(M,F)∣M∈,−m=m⁡(M,F)∣M∈.S_+^m= \m(M;D_F) M \, _-^m= \m(M;D_F) M \. (64) We compute the faithfulness score as Fm=AUC-ROC⁡(+m,−m).F_m=AUC -ROC (S_+^m,S_-^m ). (65) A higher FmF_m indicates that metric m more reliably distinguishes models containing the target knowledge from models that have not learned it. A.4.2 Robustness Robustness to Quantization Let MuM_u denote an unlearned model and b(Mu)Q_b(M_u) its b-bit quantized counterpart. Let m~(M) m(M) denote the normalized unlearning score produced by metric m, where a larger value indicates stronger evidence of successful unlearning. Quantization robustness is defined as Qm=min⁡(m~(b(Mu))m~(Mu)+ϵ,1),Q_m= ( m\! (Q_b(M_u) ) m(M_u)+ε,1 ), (66) where ϵε is a small constant introduced for numerical stability. A higher QmQ_m indicates that the metric preserves its assessment after quantization. Robustness to Relearning Let MuM_u be an unlearned model and MrM_r a retain-only reference model. Both models are exposed to the same forget-set examples under an identical relearning schedule. We use Mu(t)M_u^(t) and Mr(t)M_r^(t) to denote the corresponding models after t relearning steps. The change in normalized unlearning score is defined as Δm(M,t)=m~(M)−m~(M(t)). _m(M,t)= m(M)- m\! (M^(t) ). (67) Relearning robustness is then computed as Rm=min⁡(Δm(Mr,t)Δm(Mu,t)+ϵ,1).R_m= ( _m(M_r,t) _m(M_u,t)+ε,1 ). (68) A higher RmR_m indicates that the metric characterizes knowledge recovery in the unlearned model similarly to that in the retain-only reference model. Probing For a frozen model M, we extract hidden states hℓ(x,M)h_ (x;M) at a selected layer ℓ and fit a linear probe to predict the target label z: Wℓ∗(M)=arg⁡minW1|probetrain|∑(x,z)∈probetrainℒprobe(Whℓ(x,M),z).W_ ^*(M)= _W 1|D_probe^train| _(x,z) _probe^trainL_probe\! (Wh_ (x;M),z ). (69) The fitted probe is evaluated on an entity-disjoint test set, with random-label and matched retain-only controls, following the protocol in Section A.2.1. We use probing as a model-side audit of residual target information, but exclude it from the metric-robustness aggregate. Unlike quantization and relearning, probing introduces an auxiliary readout whose predictions are not directly comparable to the model outputs on which the original generation- and likelihood-based metrics are defined. Moreover, the method-level results in Table 9 show probing-based forgetting scores of 0.993 and 0.996 for the Finetune reference at layers 16 and 24, close to the Retain-Only scores of 0.984 and 0.993. These near-ceiling scores suggest limited discrimination in the present setting. We therefore report probing within the model-side audit and use quantization and relearning for the metric-robustness aggregate. A.4.3 Metric Aggregation We aggregate quantization and relearning robustness using their harmonic mean: Bm=HM⁡(Qm,Rm).B_m=HM (Q_m,R_m ). (70) The overall reliability score is computed as Am=HM⁡(Fm,Bm).A_m=HM (F_m,B_m ). (71) For two non-negative values x and y, the harmonic mean is defined as HM⁡(x,y)=2xyx+y+ϵ.HM(x,y)= 2xyx+y+ε. (72) A.4.4 AI-Assisted Construction of MLLMU Variants We used GPT-4o to construct text variants from MLLMU (Liu et al., 2025a). For question–answer pairs in the 10% forget split, we generated question paraphrases and answer expansions for constructing the faithfulness model pools. The original images and sample IDs were retained. Separately, we used GPT-4o-mini to generate one fact-preserving answer paraphrase and three plausible answers prompted to alter one central fact for each Generation Task question–answer pair in the MLLMU full set. The resulting 2,000-record file supplies evaluation answers for Truth Ratio, KS-Test, and JS Distance without modifying the original dataset. Only text questions and answers, not images, were submitted to the models. Question paraphrasing. The system prompt was: You rewrite exactly one question for a multimodal QA dataset. Return JSON only with one key: rewritten_question. Keep the meaning, entity, requested attribute, and answer unchanged. Do not add facts, change the task, or mention this instruction. The user prompt template was: Source QA (rewrite only the question): "question": "<question>", "answer": "<answer>" Answer expansion. The system prompt was: You expand exactly one answer for a multimodal QA dataset. Return JSON only with one key: expanded_answer. Make the answer more detailed and natural, preferably in two or three sentences, but use only facts explicitly present in the source QA. Do not invent names, dates, locations, occupations, or other details. The answer must still directly answer the original question. If the source contains only one short fact, explain that same fact without adding any new information. The user prompt template was: Source QA (expand only the answer): "question": "<question>", "answer": "<answer>" Evaluation answer generation. The system prompt was: You construct evaluation answers for a multimodal factual QA benchmark. Return JSON only with exactly these keys: attribute, paraphrased_answer, perturbed_answers. The paraphrased answer must preserve every fact in the ground-truth answer and directly answer the question. Preserve the exact profession, place, person name, date, number, institution, object, or other answer-bearing phrase from the ground truth; change only its grammatical framing. Do not replace that phrase with a broader, narrower, or approximate synonym. Each perturbed answer must be a complete, independently generated answer that is plausible and fluent but factually false for the subject. Change exactly one central fact and preserve every other fact from the ground truth. When the answer contains multiple facts, select only one of them to alter. Keep the wording, specificity, language, and approximate length close to the ground truth. Do not add explanations, uncertainty, refusals, or formatting. All perturbed answers must be mutually distinct and distinct from the ground truth. The user prompt template was: Create exactly perturbations perturbed answers for this single QA: "question": "<question>", "ground_truth": "<ground_truth>". The attribute value should be a short semantic label such as "profession", "birthplace", "residence", "education", or "hobby". We set perturbations to 3. The saved evaluation-data manifest records gpt-4o-mini and prompt version mllmu_generation_truth_ratio_v4. Outputs were checked for required fields and nonempty text; evaluation answers were additionally checked for the required count and uniqueness, with failed requests retried. These automated checks do not establish the factual correctness of every generated answer. A.4.5 Hyperparameter Search For faithfulness, the search varies target-knowledge exposure rather than selecting a single best learning rate. We cross six training datasets with five learning rates, 1,2,3,4,5×10−5\1,2,3,4,5\× 10^-5. The positive pool comprises full, full_paraphrased, and full_bio, all of which contain the correct target facts. The negative pool comprises retain90, retain90_forget10_perturbed, and retain90_celeb_bio, none of which contains the correct target facts. The perturbed-answer variant pairs target prompts with incorrect answers, while the unrelated-biography variant controls for biographical format. A.4.6 Additional Results for Unlearning Metric Evaluation Figure 7: Faithfulness of 13 MLLM unlearning metrics on the MLLMU 10% forget setting. Blue and orange histograms show score distributions for matched models trained with and without the target knowledge, respectively. Dashed lines mark metric-specific thresholds, and AUC values summarize how well each metric separates the two model pools Figure 8: Relearning robustness of 11 MLLM unlearning metrics. Blue points show eligible unlearned checkpoints before and after targeted relearning; the red star marks the mean Retain-Only reference. The dashed line indicates y=xy=x, while the shaded region marks apparent recovery of target knowledge beyond the Retain-Only reference. Figure 9: Quantization robustness of 11 MLLM unlearning metrics. Blue points show eligible unlearned checkpoints before and after quantization; the red star marks the mean Retain-Only reference. The dashed line indicates y=xy=x, while the shaded region marks quantization-induced shifts toward greater apparent target knowledge. A.5 Limitations Although Open-MMUnlearning supports five benchmarks, eight MLLMs, and twelve unlearning methods, our controlled method comparison covers ten methods on the MLLMU 10% forget setting, while the metric meta-evaluation uses LLaVA-1.5-7B in the same setting. The observed rankings may change across models, datasets, forget ratios, and evaluation protocols. Our robustness evaluations cover selected interventions and attack settings, so they cannot establish that target knowledge is irrecoverable under every possible attack or later model update. Finally, aggregate scores depend on the chosen normalization and aggregation rules and should be interpreted alongside their component results. Broader controlled evaluations are needed to assess how consistently these findings generalize. A.6 Reproducibility Statement The main paper describes the experimental setup for the controlled method comparison and metric meta-evaluation. Appendices A.3.1, A.3.2, and A.3.3 specify the unlearning objectives, hyperparameter search spaces, checkpoint selection, and score aggregation procedures. Appendix A.4 defines the metric meta-evaluation protocol, while Appendix A.4.4 documents the construction procedure and prompts for the AI-generated MLLMU variants. Experiments use structured configuration files to specify models, datasets, methods, and evaluation settings. A.7 Ethics Statement MLLM unlearning concerns information that may raise privacy, safety, and copyright issues, and our benchmark suite includes tasks in each of these settings. Our robustness evaluations probe for residual target knowledge through model interventions, adversarial inputs, and membership inference. These evaluations are intended to expose potential failures; favorable scores or apparent refusals should not be interpreted as guarantees of complete deletion, privacy protection, legal compliance, or deployment safety. Users of Open-MMUnlearning should follow the terms of the underlying datasets and assess potential information leakage before releasing unlearned models or their outputs.

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