multimodal model integration

This work addresses the lack of principled understanding in existing literature regarding the choice between cross-attention and feature concatenation strategies for multimodal fusion, which has largely relied on empirical heuristics. Through controlled experiments and theoretical analysis, we demonstrate for the first time that feature alignment quality is the key determinant of fusion strategy performance: under pre-aligned features, concatenation consistently outperforms cross-attention by 4.1–5.1 percentage points across all data scales, with its advantage becoming more pronounced as alignment degrades. Building on this insight, we develop a theoretical decision framework grounded in sample complexity and validate our findings using features extracted from ResNet-18 and CLIP ViT-B/32 on controlled datasets.

This study investigates the impact of fusion timing on the accuracy–latency trade-off in multimodal vision–language systems. We propose and systematically evaluate three fusion strategies—early, middle, and late—within a unified architecture combining BERT for language and lightweight visual backbones (MobileNetV2 or ViT) on the CMU MOSI dataset; inference latency is empirically measured on an NVIDIA Jetson Orin AGX edge platform. Results show that late fusion achieves the highest accuracy (12.3% lower MAE), while early fusion incurs the lowest latency (41.7% reduction on average), with fusion stage exhibiting a strong negative correlation between accuracy and latency. To our knowledge, this is the first work to quantitatively and systematically validate the critical influence of fusion location under a consistent experimental framework. Our findings provide reproducible architectural guidelines and empirical evidence for designing efficient multimodal models tailored to resource-constrained edge devices.

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.

This work addresses the challenge that certain modalities may introduce interference under specific inputs in multimodal fusion, thereby degrading model performance. To mitigate this issue, the authors propose a plug-and-play pre-fusion calibration module that leverages cross-modal summary contrast to extract supportive and conflicting cues, generating instance-level and dimension-level modulation signals. These signals dynamically enhance beneficial features while suppressing misleading information. The method achieves, for the first time, fine-grained, conflict-aware modulation prior to fusion and is compatible with both sequential and convolutional architectures. It consistently improves performance across five benchmark tasks—including emotion understanding and action recognition—and demonstrates enhanced robustness and consistency under modality missingness and data perturbations.

RGB-D multimodal fusion mechanisms have long suffered from poor interpretability, and the fundamental nature of cross-modal complementarity remains unclear. Method: This paper establishes the first interpretability analysis framework specifically targeting the fusion process, introducing a joint metric of semantic variance and feature similarity to systematically characterize cross-modal representation consistency, intra-modal evolutionary patterns, and collaborative optimization logic. Through cross-layer feature comparison and quantitative semantic analysis, we identify a prevalent imbalance between consistency and specificity in mainstream fusion strategies. Contribution/Results: We formalize a “specificity-driven inference under consistency constraints” principle that explicates cross-modal complementarity. Our framework provides both theoretical foundations and a verifiable evaluation paradigm for designing trustworthy, generalizable multimodal fusion models.

This work addresses the unclear interaction between feature alignment and target fitting in cross-modal fine-tuning, which often leads to a mismatch between feature-label structures across source and target domains, thereby degrading generalization. For the first time, this study theoretically characterizes their relationship by introducing the notion of “feature-label distortion,” and establishes a provable generalization bound on target error. Based on this analysis, a principle for joint optimization of alignment and fitting is derived. The resulting framework offers interpretable and actionable design guidelines for cross-modal fine-tuning. Extensive experiments demonstrate that the proposed method significantly outperforms current state-of-the-art approaches across multiple benchmark datasets, confirming its effectiveness and broad applicability.

This work addresses the performance degradation in multimodal classification caused by modality imbalance by proposing deep ensembling as an alternative to explicit modality fusion, achieving effective multimodal classification through the combination of unimodal networks. The key contributions include the first demonstration that superior performance can be attained without explicit fusion, a heuristic strategy for allocating the number of ensemble models based on each modality’s predictive capability, and the construction of a controllable synthetic multimodal data framework with fitted scaling laws. Experiments show that, under identical parameter budgets, the proposed method significantly outperforms state-of-the-art late-fusion and intermediate-fusion approaches on both real-world and synthetic datasets, while the derived scaling laws reveal an asymptotic upper bound on ensemble performance.

This work addresses the challenge in multimodal sentiment analysis where modality-specific signal refinement and cross-modal interaction modeling often interfere with each other due to conflicting optimization objectives. To resolve this, the authors propose SeRIn, a novel architecture that decouples modality separation and cross-modal interaction into structured priors through a three-stage pipeline—separation, refinement, and integration—processing unimodal representations and cross-modal interactions via independent pathways before fusing them at the prediction stage. Notably, SeRIn adaptively adjusts modality weights without requiring explicit supervision. The method achieves state-of-the-art performance on the CH-SIMS and CMU-MOSEI benchmarks, yielding significant improvements across all evaluation metrics.

This work proposes AlignMamba-2, a novel framework addressing key challenges in multimodal sentiment analysis—namely, cross-modal alignment difficulty, modality heterogeneity, and computational inefficiency. AlignMamba-2 integrates a dual-alignment mechanism based on optimal transport and maximum mean discrepancy to enhance cross-modal consistency. It further introduces a modality-aware Mixture-of-Experts architecture that effectively combines modality-specific and shared experts to better model heterogeneous data. Leveraging the efficient Mamba backbone, the proposed method achieves state-of-the-art performance on four benchmark datasets—CMU-MOSI, CMU-MOSEI, NYU-Depth V2, and MVSA-Single—demonstrating significant improvements over existing approaches in both accuracy and inference efficiency.

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.