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Co-occurring Associated REtained concepts in Diffusion Unlearning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.6Download Report PDF
unlearningdiffusionconcept erasuresafety
Abstract:

Unlearning has emerged as a key technique to mitigate harmful content generation in diffusion models. However, existing methods often remove not only the target concept, but also benign co-occurring concepts. Unlearning nudity can unintentionally suppress the concept of person, preventing a model from generating images with person. We define these undesirably suppressed co-occurring concepts that must be preserved CARE\textbf{CARE} (C\textbf{C}o-occurring A\textbf{A}ssociated RE\textbf{RE}tained concepts). Then, we introduce the CARE score\textbf{CARE score}, a general metric that directly quantifies their preservation across unlearning tasks. With this foundation, we propose ReCARE\textbf{ReCARE} (R\textbf{R}obust e\textbf{e}rasure for CARE\textbf{CARE}), a framework that explicitly safeguards CARE while erasing only the target concept. ReCARE automatically constructs the CARE-set, a curated vocabulary of benign co-occurring tokens extracted from target images, and leverages this vocabulary during training for stable unlearning. Extensive experiments across various target concepts (Nudity\textit{Nudity}, Van Gogh\textit{Van Gogh} style, and Tench\textit{Tench} object) demonstrate that ReCARE achieves overall state-of-the-art performance in balancing robust concept erasure, overall utility, and CARE preservation.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces CARE (Co-occurring Associated REtained concepts) as a formalization of benign concepts that must be preserved during unlearning, along with a CARE score metric and the ReCARE framework for robust erasure. It resides in the 'Co-Occurring Concept Preservation' leaf, which contains four papers total, including the original work. This leaf sits within the broader 'Preserving Utility and Co-Occurring Concepts' branch, indicating a moderately populated research direction focused on preventing collateral damage during concept removal. The taxonomy shows this is an active but not overcrowded area, with sibling papers addressing similar preservation challenges.

The taxonomy reveals that this work is closely related to 'Semantic Relationship and Graph-Based Reasoning' (four papers) and 'General Utility Preservation and Regularization' (seven papers), both within the same parent branch. These neighboring directions explore related but distinct approaches: semantic graphs for relational guidance versus regularization for overall quality maintenance. The paper's focus on explicitly modeling co-occurring concepts distinguishes it from general utility methods that lack explicit co-occurrence handling. The broader 'Concept Erasure Methods and Architectures' branch (twenty-one papers across multiple leaves) addresses technical erasure mechanisms, while this work emphasizes what to preserve rather than how to erase.

Among twenty-four candidates examined, none clearly refute the three main contributions. The CARE definition and metric examined ten candidates with zero refutable matches, suggesting novelty in formalizing co-occurring concept preservation as a distinct problem. The ReCARE framework similarly examined ten candidates without refutation, indicating the two-stage construction procedure (four candidates examined) appears novel within this limited search scope. The sibling papers in the same taxonomy leaf address related preservation challenges but do not appear to provide the same formalization or automated CARE-set construction approach, based on the candidates reviewed.

Given the limited search scope of twenty-four semantically similar papers, this analysis captures the immediate research neighborhood but cannot claim exhaustive coverage. The absence of refutable candidates across all contributions suggests the work introduces distinct terminology and methodology within the co-occurrence preservation space. However, the taxonomy shows this is an established research direction with multiple related efforts, so the novelty lies more in the specific formalization and framework rather than identifying the co-occurrence problem itself, which prior work has recognized.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
24
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Preserving benign co-occurring concepts during diffusion model unlearning. The field addresses the challenge of selectively removing unwanted concepts (e.g., copyrighted content, explicit imagery, or harmful styles) from pretrained diffusion models while maintaining their ability to generate benign, related content. The taxonomy reveals several complementary research directions: Concept Erasure Methods and Architectures focuses on the technical mechanisms for removing concepts, ranging from fine-tuning approaches like Erasing Concepts[2] and Safe Latent Diffusion[3] to parameter-efficient techniques such as Ablating Concepts[5]. Preserving Utility and Co-Occurring Concepts tackles the critical problem of collateral damage—ensuring that erasure does not degrade model performance on legitimate tasks or inadvertently suppress benign concepts that frequently appear alongside targeted ones. Adversarial Robustness and Attack Resistance examines vulnerabilities where adversaries attempt to circumvent erasure through prompt engineering or adversarial inputs, as explored in works like Circumventing Erasure[20]. Evaluation Frameworks and Benchmarks establish standardized metrics and test suites for assessing erasure completeness and utility preservation, while Theoretical Foundations and Irreversibility investigates the fundamental limits and guarantees of unlearning, including whether erasure can be made provably irreversible. A particularly active line of work centers on the co-occurrence problem: when a target concept (e.g., a specific artist's style) frequently appears with benign concepts (e.g., common objects or scenes), naive erasure methods often damage the model's ability to generate those benign elements. CARED[0] directly addresses this challenge by introducing mechanisms to disentangle and preserve benign co-occurring concepts during the erasure process. This work sits within a dense cluster alongside CRCE[13] and Robust Concept Erasure[37], which similarly emphasize maintaining model utility while achieving robust removal. In contrast, earlier methods like Erasing Concepts[2] and Ablating Concepts[5] primarily focused on erasure effectiveness without explicit co-occurrence handling, often resulting in broader utility degradation. The tension between erasure completeness and utility preservation remains a central open question, with recent efforts exploring adversarial training, regularization strategies, and fine-grained concept decomposition to achieve better trade-offs across diverse erasure scenarios.

Claimed Contributions

CARE (Co-occurring Associated REtained concepts) definition and CARE score metric

The authors identify and formally define CARE as benign co-occurring concepts that should be preserved during unlearning (e.g., 'person' when erasing nudity). They introduce the CARE score metric to explicitly measure the retention of these concepts, providing an evaluation dimension orthogonal to existing robustness and utility metrics.

10 retrieved papers
ReCARE (Robust erasure for CARE) framework

The authors develop ReCARE, a method that automatically constructs a CARE-set through global clustering and intra-cluster refinement to identify benign co-occurring tokens. This CARE-set is then integrated into training objectives (Retain Loss and Erase Loss) to preserve benign concepts while robustly erasing harmful targets.

10 retrieved papers
Two-stage CARE-set construction procedure with global clustering and intra-cluster refinement

The authors propose a two-stage refinement process for constructing the CARE-set: global clustering removes clusters that are either too similar to or irrelevant to the target concept, while intra-cluster refinement prunes tokens within retained clusters that still subtly resemble the target, ensuring only genuinely benign co-occurring concepts remain.

4 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

CARE (Co-occurring Associated REtained concepts) definition and CARE score metric

The authors identify and formally define CARE as benign co-occurring concepts that should be preserved during unlearning (e.g., 'person' when erasing nudity). They introduce the CARE score metric to explicitly measure the retention of these concepts, providing an evaluation dimension orthogonal to existing robustness and utility metrics.

Contribution

ReCARE (Robust erasure for CARE) framework

The authors develop ReCARE, a method that automatically constructs a CARE-set through global clustering and intra-cluster refinement to identify benign co-occurring tokens. This CARE-set is then integrated into training objectives (Retain Loss and Erase Loss) to preserve benign concepts while robustly erasing harmful targets.

Contribution

Two-stage CARE-set construction procedure with global clustering and intra-cluster refinement

The authors propose a two-stage refinement process for constructing the CARE-set: global clustering removes clusters that are either too similar to or irrelevant to the target concept, while intra-cluster refinement prunes tokens within retained clusters that still subtly resemble the target, ensuring only genuinely benign co-occurring concepts remain.

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