multimodal foundation models

This work systematically investigates the core mechanisms of modality interaction in multimodal pretraining, focusing on knowledge transfer, synergistic effects, and fusion timing. Through experiments on both synthetic and large-scale real-world datasets, it provides the first empirical evidence of asymmetric cross-modal knowledge flow and demonstrates that data complexity governs whether modalities exhibit synergy or competition. The study further validates that early unified fusion consistently outperforms late alignment. Leveraging an architecture featuring shared attention and normalization layers with modality-specific feedforward components, the proposed approach is evaluated on a 13.5B mixture-of-experts model trained on 2 trillion tokens, confirming its effectiveness. Additionally, the paper introduces a highly efficient pretraining strategy that achieves strong generative performance using only 5% of the typical computational budget.

Traditional multimodal approaches rely on modality-specific encoders and late fusion, limiting scalability and cross-modal generalization. This work proposes a unified paradigm that reformulates diverse multimodal tasks—including text, image, audio, and video processing—as “next-frame prediction,” with all inputs and outputs represented as serialized video frames, enabling end-to-end, single-model inference. It pioneers the task-redefinition strategy—previously confined to NLP—within multimodal learning, eliminating modality-specific design in favor of a fully modality-agnostic architecture. The framework integrates cross-modal tokenization, sequential frame representation, autoregressive modeling, and a shared Transformer decoder, supporting joint multi-task pretraining. Empirical evaluation demonstrates strong zero-shot and few-shot generalization across text-to-text, image-to-text, video-to-video, video-to-text, and audio-to-text tasks. Crucially, adaptation requires only lightweight task-specific heads, underscoring its flexibility and efficiency.

Existing surveys predominantly examine isolated components of multimodal pipelines and lack empirically grounded, pedagogically oriented integration frameworks for teaching and learning contexts. Method: This study introduces the first taxonomy and analytical framework covering five core modalities—natural language, video, sensor data, human-centered signals, and environmental logs—and proposes a novel “mid-fusion” paradigm for multimodal data integration. It further innovates by applying citation graph pruning to achieve structured, high-precision literature synthesis. Contribution/Results: Through systematic review, taxonomic modeling, and multimodal fusion design, we demonstrate that multimodal synergy enables detection of fine-grained learning behaviors imperceptible to unimodal analysis. While prediction accuracy remains largely unchanged, interpretability improves significantly, yielding deeper insights into learners’ cognitive-affective states and training outcomes.

This paper systematically analyzes four core challenges in multimodal alignment and fusion—cross-modal misalignment, semantic modality gap, computational bottlenecks, and data noise/heterogeneity—that hinder model robustness, generalizability, and scalability. To address them, the authors propose a unified taxonomy covering 200+ studies, revealing an integrated “alignment–fusion” co-design paradigm. They further introduce three principled solution pathways: (1) noise-robust learning, (2) heterogeneous representation modeling, and (3) few-shot cross-modal transfer—unifying contrastive learning, cross-modal attention, latent-space alignment, graph neural networks, and differentiable architecture search. Extensive experiments on social media analysis, medical imaging, and sentiment recognition validate the efficacy of the framework. The work distills actionable design principles for scalable, robust, and generalizable multimodal learning, establishing a new benchmark for both theoretical research and industrial deployment.

This paper addresses key challenges in multimodal large language models (MLLMs)—namely limited scalability, insufficient robustness, and difficulties in cross-modal alignment—within vision-language tasks. To tackle these, it proposes a comprehensive technical taxonomy covering the full MLLM stack and introduces a tripartite evaluation framework centered on scalability, robustness, and ethical alignment. Methodologically, the work conducts a systematic comparative analysis of core techniques, including Transformer-based fusion architectures, visual encoders (e.g., ViT, CLIP), cross-modal alignment learning, instruction tuning, and multi-stage training. It further synthesizes structured best practices for modeling across text, image, video, and audio modalities. The study delivers 12 representative application cases, empirically validating its framework and guidelines. Collectively, this work advances trustworthy deployment and cross-domain adoption of MLLMs through rigorous architectural analysis, principled evaluation, and actionable implementation insights.

This study addresses the unclear trade-offs among diverse capabilities—such as general visual understanding, structured reasoning, and fine-grained OCR—in multimodal instruction tuning under mixed data regimes, particularly the lack of systematic investigation into how data organization influences these trade-offs. Treating data scheduling as a first-order design variable while holding model architecture and optimization settings fixed, the work compares four strategies: direct mixing, curriculum learning, balanced sampling, and reverse curriculum. Results demonstrate that curriculum-based training—sequencing tasks from general comprehension to specialized skills—achieves superior overall performance and structured reasoning while accelerating convergence. Balanced sampling improves OCR accuracy at the cost of capability imbalance, whereas reverse curriculum degrades performance and induces optimization instability. The findings highlight the critical role of training sequence in shaping the capability distribution of multimodal models.

This work addresses the limitations of existing approaches that predominantly rely on non-native late-fusion architectures and lack a systematic definition or unified framework for native multimodal modeling. The paper proposes a formal roadmap for Native Multimodal Modeling (NMM), introducing, for the first time, a rigorous formulation of “architectural nativeness.” Leveraging an input–output duality perspective, it categorizes models into three types: Multi-to-Text, Multi-to-Target, and Multi-to-Multi. A full-stack, industrial-grade NMM framework is developed, encompassing data governance, early/mid-stage fusion, end-to-end training, and deployment, all realized through a unified Transformer architecture that enables symbiotic cross-modal understanding and generation. This paradigm demonstrates superior performance and strong scalability across multiple tasks, offering a clear pathway toward truly native multimodal models.

This work addresses the challenge of efficiently equipping vision-language models with continuously evolving domain-specific skills, a task hindered by the high cost of conventional fine-tuning. The authors propose injecting capabilities from domain-specialized large language models into vision-language models through model fusion, enabling cross-modal skill transfer without additional training data or substantial computational resources. The study presents the first systematic analysis of the method’s applicability, fusion strategies, and hyperparameter sensitivity, offering quantitative evaluations of techniques such as Task Arithmetic (TA) and DARE across heterogeneous architectures. Experimental results demonstrate strong performance in instruction-following and cross-lingual tasks, while revealing limitations in mathematical reasoning, thereby delineating the effective boundaries and critical tuning factors for cross-modal skill injection.

本文通过构建从模型到系统的分类法，系统化地解决了多模态学习中的计算、内存和部署瓶颈问题，并探讨了效率在不同层面的体现及优化方法。

This study addresses the limitation of existing multimodal models in adapting to arbitrary modality combinations and prediction tasks by proposing a universal multimodal foundation model. The proposed method introduces a structural multimodal causal model to generate large-scale synthetic data, enabling the model to learn transferable cross-modal correlation patterns rather than relying on modality-specific representations. Furthermore, it incorporates in-context exemplar reasoning to achieve zero-shot generalization and fusion. Experimental evaluations across 18 datasets demonstrate that the proposed model attains competitive performance comparable to task-specific models without requiring task-specific fine-tuning. This work establishes a novel paradigm for constructing modality-agnostic, general-purpose multimodal systems.