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When Large Multimodal Models Confront Evolving Knowledge: Challenges and Explorations

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Evolving Knowledge Injection; Large multimodal model; Benchmark and Dataset
Abstract:

Large Multimodal Models (LMMs) store vast amounts of pretrained knowledge but struggle to remain aligned with real-world updates, making it difficult to avoid capability degradation when acquiring evolving knowledge. Furthermore, most current work focuses on exploring static textual knowledge injection, neglecting dynamic multimodal evolving knowledge injection, leaving the potential of LMMs for multimodal knowledge injection as an open question. To address this, we first propose a pipeline to construct MMEVOKE, a benchmark for evaluating LMMs' ability in multimodal evolving knowledge injection. MMEVOKE contains 9,422 samples spanning 159 subtypes. Then, based on extensive experiments with MMEVOKE, we reveal challenges such as poor injection performance and capability degradation in existing knowledge injection methods through knowledge injection tests and general capability tests. Finally, to tackle these challenges, we introduce knowledge augmentation and knowledge retention methods, finding that knowledge-aware augmentation strengthens knowledge injection performance, and that Data Replay and MoE methods effectively mitigate capability degradation.

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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 MMEVOKE, a benchmark for evaluating multimodal evolving knowledge injection in large multimodal models, alongside knowledge augmentation and retention methods. It resides in the 'Evolving Knowledge Benchmarking and Evaluation' leaf, which contains four papers total, including the original work. This leaf sits within the broader 'Continual and Evolving Knowledge Learning' branch, indicating a moderately populated research direction focused on temporal knowledge updates and evaluation frameworks rather than architectural innovation.

The taxonomy reveals that neighboring leaves address complementary challenges: 'Continual Learning with Knowledge Retention' explores methods to mitigate catastrophic forgetting, while sibling papers like KORE and MINED focus on temporal reasoning benchmarks and dynamic knowledge rectification. The paper's emphasis on comprehensive evaluation distinguishes it from branches like 'Knowledge Injection Mechanisms and Architectures,' which prioritize structural modifications, and 'Domain-Specific Knowledge Injection,' which targets specialized applications. This positioning suggests the work bridges benchmarking and methodological contributions within the evolving knowledge subfield.

Among thirty candidates examined, the benchmark contribution (Contribution A) shows one refutable candidate from ten examined, while the challenge identification (Contribution B) similarly finds one overlapping work among ten. The knowledge augmentation and retention methods (Contribution C) appear more novel, with zero refutable candidates across ten examined papers. These statistics indicate that while the benchmark and challenge analysis have some prior overlap within the limited search scope, the proposed mitigation strategies show less direct precedent among the top-ranked semantic matches.

Based on the limited search scope of thirty candidates, the work appears to occupy a moderately explored niche within evolving knowledge evaluation. The benchmark and challenge identification face some prior work overlap, whereas the retention methods show stronger novelty signals. However, this assessment reflects top-K semantic matches and does not constitute an exhaustive literature review, leaving open the possibility of additional relevant work beyond the examined candidates.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: multimodal evolving knowledge injection in large multimodal models. The field addresses how to continuously update and integrate new knowledge into models that process both vision and language, ensuring they remain accurate as information evolves. The taxonomy reveals five main branches: Knowledge Injection Mechanisms and Architectures explores foundational designs for embedding knowledge into model structures, often through adapters or specialized modules (e.g., GraphAdapter[28], Cross Modal Adapters[21]); Prompt-Based and Retrieval-Augmented Knowledge Integration focuses on dynamic prompting strategies and external retrieval to supply contextual knowledge (e.g., Conditional Prompt[3], Dynamic Prompt Routing[13]); Continual and Evolving Knowledge Learning tackles the challenge of updating models over time without catastrophic forgetting, including benchmarking efforts; Domain-Specific Knowledge Injection targets specialized areas like medicine (KPL Medical[1], HuatuoGPT Vision[10]) or robotics (RT-2[16]); and General Domain Adaptation and Transfer examines broader techniques for cross-domain knowledge transfer and model merging (Model Merging Initialization[50]). Several active lines of work highlight key trade-offs between static versus dynamic knowledge integration and the balance between architectural modifications and prompt-based flexibility. Within Continual and Evolving Knowledge Learning, benchmarking efforts such as Evolving Knowledge[0] and KORE[35] assess how well models handle temporal knowledge shifts, while MINED[38] and Evolving Knowledge Pathways[19] explore mechanisms for incremental updates. Evolving Knowledge[0] sits squarely in this benchmarking cluster, emphasizing evaluation frameworks for evolving multimodal knowledge rather than proposing new injection architectures. Compared to neighbors like KORE[35], which also focuses on temporal reasoning benchmarks, and MINED[38], which addresses dynamic knowledge rectification, Evolving Knowledge[0] appears to prioritize comprehensive assessment of knowledge evolution capabilities. This contrasts with works in other branches that emphasize architectural innovation (e.g., DIM[11]) or domain-specific tuning (Remedy[2]), underscoring an ongoing tension between developing new methods and rigorously measuring their effectiveness on evolving knowledge tasks.

Claimed Contributions

MMEvoke benchmark for multimodal evolving knowledge injection

The authors introduce MMEvoke, a comprehensive benchmark designed to systematically evaluate large multimodal models' capabilities in injecting evolving knowledge. The benchmark comprises 9,422 multimodal samples across 159 fine-grained subfields, covering both news and entity evolving knowledge from 2024 onwards, with a reproducible construction pipeline.

10 retrieved papers
Can Refute
Identification of challenges in existing knowledge injection methods

Through systematic experiments on MMEvoke, the authors identify and characterize two critical challenges: poor knowledge adaptation performance in existing injection methods (even with sufficient context), and significant capability degradation across multiple dimensions after knowledge injection, with a consistent severity ranking and cascading effects.

10 retrieved papers
Can Refute
Knowledge augmentation and retention methods for evolving knowledge injection

The authors propose and evaluate knowledge augmentation strategies (distinguishing knowledge-aware from knowledge-agnostic approaches) and knowledge retention methods (including Data Replay and MoELoRA). They demonstrate that knowledge-aware augmentation improves knowledge adaptation while partially mitigating degradation, and that direct rehearsal and structured separation methods effectively preserve model capabilities.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

MMEvoke benchmark for multimodal evolving knowledge injection

The authors introduce MMEvoke, a comprehensive benchmark designed to systematically evaluate large multimodal models' capabilities in injecting evolving knowledge. The benchmark comprises 9,422 multimodal samples across 159 fine-grained subfields, covering both news and entity evolving knowledge from 2024 onwards, with a reproducible construction pipeline.

Contribution

Identification of challenges in existing knowledge injection methods

Through systematic experiments on MMEvoke, the authors identify and characterize two critical challenges: poor knowledge adaptation performance in existing injection methods (even with sufficient context), and significant capability degradation across multiple dimensions after knowledge injection, with a consistent severity ranking and cascading effects.

Contribution

Knowledge augmentation and retention methods for evolving knowledge injection

The authors propose and evaluate knowledge augmentation strategies (distinguishing knowledge-aware from knowledge-agnostic approaches) and knowledge retention methods (including Data Replay and MoELoRA). They demonstrate that knowledge-aware augmentation improves knowledge adaptation while partially mitigating degradation, and that direct rehearsal and structured separation methods effectively preserve model capabilities.

Table of Contents