Why flattening multimodal inputs is a game-changer
Flattening means converting different types of data—like text, images, and sensor readings—into a single, unified representation that a model can process together. This is crucial because traditional models often treat each modality separately, leading to misalignment and lost information. A 2025 study [2] showed that combining two flattened views of a sentence—a sequential token sequence and a semantic plane—achieved state-of-the-art performance on relation extraction across four datasets. This suggests that flattening isn't just about convenience; it can actually capture richer semantic information than using a single representation alone.
Another key benefit is noise reduction. A 2025 paper [3] introduced a method that compresses multimodal input to filter out irrelevant information while preserving essential semantics, then decomposes the remaining information into unique, redundant, and synergistic components. This approach improved multimodal recommendation performance across three benchmark datasets, indicating that flattening can help models focus on what matters and ignore distractions.
What this means for real-world applications
The most dramatic evidence comes from a 2025 study [4] that built a multimodal foundation model for human activity recognition. By aligning four modalities—IMU (inertial measurement unit) data, third-person video, motion capture, and text—into a shared representation, they achieved a zero-shot classification accuracy of 0.7320, compared to 0.1961 for existing methods. That's a massive jump, showing that flattening can unlock capabilities that were previously out of reach, especially in domains like healthcare and robotics where understanding complex human actions is critical.
However, flattening isn't without challenges. A 2025 paper [5] found that when fine-tuning large language models for multimodal representation, the models often neglected image content. They had to design special prompts and fusion techniques to ensure both text and images were properly represented. This highlights that simply flattening inputs isn't enough—you need careful alignment to balance the influence of each modality. Over the next two years, we can expect more sophisticated alignment methods that address these issues, making flattened representations more robust and reliable.
Where the field is heading in the next two years
The research points toward a future where flattened representations become the standard for multimodal foundation models. A 2026 paper [1] outlines a research agenda focused on improving visual representations, aligning them with language models, and enhancing reasoning. This includes techniques like language-driven augmentations and multi-view fusion, which are all steps toward more seamless integration of modalities. As these methods mature, we'll likely see models that can handle a wider range of inputs—from text and images to sensor data—with greater accuracy and efficiency.
Another trend is the move toward disentangled representations, as seen in [3], where information is separated into unique, redundant, and synergistic parts. This allows models to leverage the strengths of each modality while minimizing overlap and noise. Over the next two years, we can expect more work on making these representations not just flattened, but also more interpretable and controllable. This will be key for applications like recommendation systems and personalized assistants, where understanding user preferences across multiple modalities is essential.
About These Sources
This answer is built on 5 studies (3 peer-reviewed, 2 preprints) — published from 2025 to 2026, 5 from 2024 or later, 1 in Q1 journals — selected as the most relevant from 5 studies that passed quality screening, drawn from 42 papers retrieved from a database of over 500 million.
Sources used in this answer
On the Alignment of Multimodal Foundation Models
This 2026 research agenda identifies three key components for robust multimodal reasoning—better visual representations, effective alignment with LLMs, and stronger reasoning—and proposes methods like TEAM, E2, TUNA, and Video Contrastive Decoding to address them, though no quantitative results are provided.
A homogeneous multimodality sentence representation for relation extraction
This 2025 study fused sequential and plane representations of sentences into a homogeneous multimodality representation, achieving state-of-the-art performance on relation extraction across four public datasets, demonstrating the benefit of combining complementary flattened views.
Multimodal Representation-disentangled Information Bottleneck for Multimodal Recommendation
This 2025 paper introduced MRdIB, a framework that compresses multimodal input and decomposes it into unique, redundant, and synergistic components, improving multimodal recommendation performance across three benchmark datasets by filtering noise and capturing emergent information.
AURA-MFM: Activity Understanding and Representations Alignment - Multimodal Foundation Model
This 2025 study proposed AURA-MFM, a multimodal foundation model aligning IMU, third-person video, motion capture, and text, achieving a zero-shot action recognition accuracy of 0.7320 compared to 0.1961 for existing methods, a substantial improvement.
NoteLLM-2: Multimodal Large Representation Models for Recommendation
This 2025 paper introduced NoteLLM-2, a framework for efficient multimodal representation in recommendations, finding that fine-tuned LLMs often neglect image content and proposing prompt-based and late fusion techniques to balance modality focus, validated in online and offline experiments.