Multimodal Knowledge Editing (MKE)

• MKE is a framework that updates multimodal models by targeting corrections in both visual and textual data with a focus on reliability, generality, and locality.
• It employs methods such as parameter updates, memory-based techniques, and in-context interventions to propagate accurate edits across complex, entangled representations.
• Evaluation metrics like reliability, locality, and modality consistency are used to guide and benchmark the effectiveness of iterative, scenario-aware knowledge updates.

Multimodal Knowledge Editing (MKE) refers to the targeted update, correction, or injection of knowledge in models that integrate both textual and visual modalities—most notably, Multimodal LLMs (MLLMs). Unlike unimodal (text-only) knowledge editing, MKE operates on representations that bind visual facts (such as entities depicted in images) to their corresponding textual descriptions or relations. Techniques in this domain seek to balance three desiderata: reliability (ensuring the correction 'sticks'), generality (robustness to paraphrased or novel inputs), and locality (preservation of unrelated knowledge). Recent work has extended MKE from coarse, factoid edits to fine-grained, visually grounded, combinatorial, and even meta-cognitive knowledge updates.

1. Foundational Principles and Formalism

MKE generalizes the classic text-based knowledge editing paradigm to models $$f_\theta: (\mathbb{V}, \mathbb{X}) \rightarrow \mathbb{Y}$$ that accept both vision and language inputs. An MKE operation is formally an edit descriptor $(v_e, x_e, y_e)$ (image, text, desired output) or, in a more general quadruple formalism, $f = (x, v, o \to \tilde o)$, where a base fact $o$ is replaced with a correction $\tilde o$ under specified multimodal context. The main objectives after editing are:

• $f_\theta(v_e, x_e) = y_e$ (edit reliability),
• For any paraphrased $(v'_e, x'_e)$ in the “semantic neighborhood”, $f_\theta(v'_e, x'_e) = y_e$ (generality),
• For unrelated $(v_u, x_u)$, $f_\theta(v_u, x_u)$ remains unchanged (locality). Such models must propagate these edits throughout the entangled vision-language latent space and ensure multimodal alignment, which introduces challenges not present in unimodal editing (Li et al., 18 Feb 2024, Ma et al., 17 Dec 2024, Yuan et al., 30 Nov 2025).

2. Task Taxonomy and Benchmarks

Recent MKE research has defined a taxonomy of tasks, targeting progressively more challenging scenarios.

• Fine-Grained Multimodal Entity Knowledge Editing (FG-MKE): The MIKE benchmark (Li et al., 18 Feb 2024) focuses on editing knowledge about specific and visually grounded entities (e.g., identifying “President Joe Biden” rather than “a politician”). MIKE tasks include Vanilla Name Answering (VNA), Entity-Level Captioning (ELC), and Complex-Scenario Recognition (CSR), each probing a different aspect of the multimodal editing process.
• Editing Error Types and Modality Consistency: MC-MKE (Zhang et al., 19 Jun 2024) distinguishes between misrecognition (visual errors, e.g., entity extraction from images) and misreading (textual errors, e.g., attribute misclassification given the correct entity). Different editing formats (IE_edit, SRO_edit, IRO_edit) target updates to visual, textual, or combined components, respectively, and the benchmark emphasizes modality consistency—the requirement that post-edit, all interface modalities reflect the same correction.
• Diverse and Free-Form Knowledge: MMKE-Bench (Du et al., 27 Feb 2025) and ComprehendEdit (Ma et al., 17 Dec 2024) evaluate editing of diverse visual knowledge including free-form entity semantics, gestures, actions, and user-specific knowledge.
• Dynamic, Multihop, and Medical Scenarios: Hybrid-DMKG (Yuan et al., 30 Nov 2025) introduces MMQAKE for reasoning over edited dynamic multimodal KGs in 2–5-hop multihop chains. MedMKEB (Xu et al., 7 Aug 2025) and MultiMedEdit (Wen et al., 9 Aug 2025) extend these evaluations to the medical domain, probing not only single edits but also knowledge portability, adversarial robustness, and sequential (lifelong) editing.

3. Methodologies and Editor Frameworks

Editing in the MKE context utilizes several paradigms, summarized below with concrete instantiations from the literature.

Editor Type	Principle	Example Method / Paper
Parameter Update	Localized gradient or low-rank transformation of model parameters	MEND (Li et al., 18 Feb 2024), LoRA (Wen et al., 9 Aug 2025)
Memory-Based	External memory/explicit edit cache, possibly with learned scope detection	SERAC (Li et al., 18 Feb 2024, Zeng et al., 19 Nov 2024), MSCKE (Zeng et al., 19 Nov 2024)
In-Context (Demo)	Prompt-augmented inference (no parameter change)	IKE (Li et al., 18 Feb 2024, Du et al., 27 Feb 2025)
Hybrid / Modular	Unified key–value memory, combining internal/external updates	UniKE (Pan et al., 30 Sep 2024), MindBridge (Li et al., 4 Mar 2025), MemEIC (Seong et al., 29 Oct 2025)
Specialized Adapters	Mixture-of-Experts, gated adapters conditioned on scope	MolEdit (MoLMs) (Lei et al., 16 Nov 2025)
Meta-Cognitive	Layer-wise meta-memory with game-theoretic monitoring	MIND (Fan et al., 6 Sep 2025)

Notably, architectures such as MindBridge (Li et al., 4 Mar 2025) introduce a “memory modality” independent of any LLM backbone, enabling scalable, cross-model editing, while MemEIC (Seong et al., 29 Oct 2025) employs dual LoRA adapters per modality with a selective connector to support continual and compositional sequences of edits. MSCKE (Zeng et al., 19 Nov 2024) utilizes a multimodal scope classifier to tightly localize edits in fine-grained visual contexts.

4. Evaluation Methodologies and Metrics

Benchmarks and practical studies have converged on a set of key axiomatic metrics:

• Reliability: Fraction of edit cases in which the model returns the new, desired output.
• Locality: Fraction of unrelated (out-of-domain) queries where the output does not change post-edit. Includes both text-locality and image-locality.
• Generality: Fraction of paraphrased or novel (in-domain) queries where the edit “sticks”—captures the robustness of the update.
• Consistency: Modality consistency between text-only and image+text edit routes (e.g., MC-MKE (Zhang et al., 19 Jun 2024)).
• Knowledge Generalization Index (KGI) / Knowledge Preservation Index (KPI): In ComprehendEdit (Ma et al., 17 Dec 2024), these measure the effect of editing on neighboring in-domain samples (those that were previously wrong or right, respectively), explicitly excluding AI-generated paraphrases, which addresses bias.
• Portability and Robustness: The ability of the edit to transfer to reasoning chains (MedMKEB (Xu et al., 7 Aug 2025)) or to resist adversarial prompt perturbations.

5. Key Findings, Practical Insights, and Limitations

Evaluations across multiple benchmarks reveal several consistent phenomena:

• No existing method achieves uniformly high reliability, locality, and generality across all edit formats and task types (Li et al., 18 Feb 2024, Zhang et al., 19 Jun 2024, Du et al., 27 Feb 2025).
• Memory-based methods (e.g., SERAC) and scope classifiers (e.g., in MSCKE (Zeng et al., 19 Nov 2024)) excel at locality and specificity, critical for fine-grained, entity-targeted edits.
• In-context methods (e.g., IKE) tend to achieve higher reliability on single-step edits but may generalize poorly or degrade on locality, especially in complex or sequential editing.
• Adapters and gating mechanisms (e.g., MolEdit’s MEKA+EAES (Lei et al., 16 Nov 2025), MemEIC’s dual LoRA (Seong et al., 29 Oct 2025)) outperform monolithic updates by containing changes to targeted submodules.
• Multi-step (K-shot) editing and approaches leveraging multi-view augmentation enable more robust entity and scenario modeling but lead to diminishing returns beyond 3–4 cues (Li et al., 18 Feb 2024).
• Cross-modal and sequential edits face compounding side effects, with the locality and generality degrading non-linearly as the number or compositional depth of edits increases (Wen et al., 9 Aug 2025, Seong et al., 29 Oct 2025).
• Editing only the LLM head or Q-former is often more effective for text-rich or entity-focused edits, while vision encoder edits are required for correcting misrecognitions or visual concepts (Zhang et al., 19 Jun 2024, Zeng et al., 19 Nov 2024).
• Domain brittleness: general editors (SERAC, IKE) may fail in medical or scientific domains, motivating new hybrid or KG-anchored approaches (Xu et al., 7 Aug 2025).
• Meta-cognitive supervision (reliance on Shapley-value monitoring and label prototypes) enables editing modules to learn both when to apply specific knowledge and under what boundary or noise conditions they should abstain (Fan et al., 6 Sep 2025).

6. Domain-Specific and Advanced Scenarios

Specialized applications push MKE research into new regimes:

• Medical and Scientific Domains: MultiMedEdit (Wen et al., 9 Aug 2025) and MedMKEB (Xu et al., 7 Aug 2025) characterize knowledge editing in clinical VQA, requiring generalization to multi-frame reasoning, strict locality, and robustness against adversarial queries. Medical-domain MLLMs often require models to maintain reliability and generality under sequential, multi-hop knowledge transfer conditions.
• Molecular LLMs: MolEdit (Lei et al., 16 Nov 2025) develops Mixture-of-Experts adapters and facet-based gating for updating molecular structure–caption mappings, crucial to ensure updates remain isolated and do not degrade chemically unrelated knowledge.
• Multihop and Dynamic KG Reasoning: Hybrid-DMKG (Yuan et al., 30 Nov 2025) and MemEIC (Seong et al., 29 Oct 2025) advance towards continual, compositional, and interpretable editing supported by dynamic multimodal knowledge graphs and explicit retrieval modules for step-wise, chain-based reasoning.

7. Future Directions and Open Problems

Emergent gaps and possibilities identified include:

• Designing editors that adaptively fuse or partition the vision and language backbones, supporting both entity-aware and modality-consistent edits.
• Developing retrieval-augmented, hybrid editors that dynamically schedule or select between memory-based and parameter-efficient updates, conditioned on task and context.
• Increasing the scalability of MKE methods to tens or hundreds of thousands of edits while preserving catastrophic forgetting without overfitting to edit sets (Li et al., 4 Mar 2025).
• Extending meta-cognitive editing and reflective monitoring into lifelong, continuous knowledge update pipelines.
• Integrating temporal, event-based, or structured knowledge representations to support higher-order reasoning, transfer, and robustness.

The trajectory of multimodal knowledge editing research demonstrates a move from coarse, triplet-based edits toward scenario-aware, lifespan-robust, and meta-cognitive frameworks. These advances underpin next-generation multimodal AI systems that safely, efficiently, and flexibly adapt their internal multimodal world models to a rapidly changing world (Li et al., 18 Feb 2024, Ma et al., 17 Dec 2024, Yuan et al., 30 Nov 2025, Xu et al., 7 Aug 2025, Zeng et al., 19 Nov 2024, Fan et al., 6 Sep 2025, Seong et al., 29 Oct 2025, Pan et al., 30 Sep 2024, Li et al., 4 Mar 2025, Lei et al., 16 Nov 2025).