arXiv AI June 12, 2026 By Md Raisul Kibria, S\'ebastien Lafond, Janan Arslan

Decoding the Multimodal Maze: A Systematic Review on the Adoption of Explainability in Multimodal Attention-based Models

arXiv:2508. 04427v2 Announce Type: replace-cross Abstract: Multimodal learning has witnessed remarkable advancements in recent years, particularly with the integration of attention-based models, leading to significant performance gains across a variety of tasks.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

multimodal

arXiv AI

Jun 6

Mechanistic Insights into Functional Sparsity in Multimodal LLMs via CoRe Heads

arXiv:2606. 05843v1 Announce Type: cross Abstract: While Multimodal Large Language Models (MLLMs) demonstrate remarkable proficiency on complex vision-language tasks, the mechanisms by which they extract query-relevant visual features from complex, noisy contexts remain opaque.

By Ruoxi Sun, Quantong Qiu, Juntao Li, Zecheng Tang, Yihang Lou, Min Zhang

llms efficiency multimodal safety

arXiv AI

Jun 26

What We are Missing in Multimodal LLM Evaluation?

arXiv:2606. 26348v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can process diverse inputs, e.

By Po-han Li, Shenghui Chen, Sandeep Chinchali, Ufuk Topcu

llms multimodal benchmarks

arXiv AI

Aug 7

C$^3$PO: Evaluating Cross-Modal Composition and Counterfactual Performance in Omnimodal Models

arXiv:2608. 05381v1 Announce Type: new Abstract: Current Multimodal Large Language Models (MLLMs) can process diverse sensory inputs, yet their reasoning remains heavily biased toward a dominant modality, resulting in brittle cross-modal reasoning.

By Swapnanil Mukherjee, Agyeya Negi, Tanuja Ganu, Ponnurangam Kumaraguru

llms multimodal benchmarks

arXiv AI

Jul 13

Explaining is Harder Than Predicting Alone: Evaluating Concept-based Explanations of MLLMs as ICL Visual Classifiers

arXiv:2605. 28215v2 Announce Type: replace Abstract: In-context learning (ICL) enables multimodal large language models (MLLMs) to classify images from a few labelled examples.

By Carmen Quiles-Ram\'irez, Leticia L. Rodr\'iguez, Nicol\'as Martorell, Natalia D\'iaz-Rodr\'iguez

llms multimodal benchmarks

arXiv AI

Jun 26

From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models

arXiv:2606. 26196v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have recently made remarkable progress in unifying vision-language understanding and reasoning, especially following the introduction of models such as OpenAI's O-series and DeepSeek's R-series, which have driven a paradigm shift toward perception-centric intelligence.

By Haoxiang Sun, Tao Wang, Li Yuan, Jian Zhao, Jiancheng Lv

llms multimodal

arXiv AI

Jun 30

Does Role Specialization Matter for Explanation Faithfulness in Mixture-of-Experts?

arXiv:2606. 29613v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures have recently been extended with role-based mechanisms for interpretability.

By Yeji Kim, Housam Babiker, Mi-Young Kim, Randy Goebel

diffusion multimodal benchmarks safety

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Mechanistic Insights into Functional Sparsity in Multimodal LLMs via CoRe Heads

What We are Missing in Multimodal LLM Evaluation?

C$^3$PO: Evaluating Cross-Modal Composition and Counterfactual Performance in Omnimodal Models

Explaining is Harder Than Predicting Alone: Evaluating Concept-based Explanations of MLLMs as ICL Visual Classifiers

From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models

Does Role Specialization Matter for Explanation Faithfulness in Mixture-of-Experts?