Multimodal Foundation Model

• Multimodal foundation models are large-scale neural architectures that ingest and align diverse data types (text, images, speech, etc.) into semantically enriched representations.
• Key architectures include dual-encoders, single-backbone token models, branch-specific encoders with fusion, and modular adapters to efficiently handle varied modalities.
• Pretraining objectives such as contrastive alignment, masked autoencoding, and teacher feature regression enhance cross-modal convergence and drive performance in specialized domains.

A multimodal foundation model (MFM) is a large-scale, general-purpose neural architecture designed to ingest, align, and reason over multiple modalities—such as text, images, speech, video, or sensor data—using a unified or coordinated representation space. MFMs are characterized by their capacity for modality-agnostic transfer, strong zero/few-shot adaptation, and the ability to abstract away from input surface heterogeneities (e.g., language syntax, acoustic structure, or visual appearance) to produce task-robust, semantically enriched representations. MFMs are now central in high-impact application domains spanning general AI, clinical medicine, geospatial science, wireless communications, and neuroscience.

1. Core Architectures and Modality Integration

Contemporary MFM architectures generally follow one of several patterns: dual-encoder (CLIP-like), single-backbone with discrete tokenization (DIDO), branch-specific encoders followed by cross-modal fusion (transformer or GNN), or modular extension with adapters.

• Dual-encoder paradigm: Text and image/speech encoders are pretrained to align their outputs in a joint embedding space, most often using a symmetric InfoNCE (contrastive) or similar objective (Zhang et al., 2023, Fei et al., 2021, Farzanullah et al., 29 Dec 2025, Lee et al., 2024).
• Single-backbone discrete-token models: All modalities are discretized (e.g., via VQ-VAE or Q-Former), then encoded and decoded by a unified transformer supporting arbitrary input/output ordering, enabling true any-to-any generation and interleaved multimodal output (Wang et al., 2024).
• Branch-specific encoders plus fusion: Each input type is handled by a specialized encoder (e.g., ViT for images, BERT for text, CNN/Transformer for audio or sensor streams); these are merged via attention, alignment, or memory-based modules, with cross-modal dependencies captured through attention or graph reasoning (Matsuishi et al., 29 May 2025, Mohsin et al., 2 Oct 2025, Yang et al., 27 Oct 2025, Aboulfotouh et al., 19 Nov 2025).
• Modular/adapter-based expansion: Lightweight skill-specific or modality-specific adapters are grafted onto a frozen backbone, enabling efficient extension to new input types or tasks without retraining the full model (Qazi et al., 14 Nov 2025).

Processing long, variable-length modalities (e.g., speech versus text) often requires explicit length adaptation: pooling, learned attention, or convolutional adapters to map varying input sizes to fixed-dimension representations (Lee et al., 2024). More advanced models employ data-driven or task-adaptive compression strategies for robust cross-modal alignment, particularly in low-resource language/speech settings.

2. Pretraining Objectives, Representation Alignment, and Layerwise Convergence

The dominant pretraining paradigms for MFMs emphasize unsupervised or self-supervised objectives designed to enforce semantic convergence across modalities:

• Contrastive alignment: Symmetric InfoNCE loss aligns positive pairs (e.g., image-text, speech-text) and repels negatives in the joint space (Zhang et al., 2023, Fei et al., 2021, Lee et al., 2024, Farzanullah et al., 29 Dec 2025). Pooling, mean, or attention-based reduction is used to produce fixed-size embeddings per input unit.
• Masked autoencoding: Models trained to reconstruct masked image or signal patches given partial observations, often with a shared ViT backbone for all image-centric modalities (Shi et al., 2024, Ravirathinam et al., 2024, Zhou et al., 30 Jun 2025, Aboulfotouh et al., 19 Nov 2025, Yan et al., 2024).
• Regression to teacher features: Student encoders are regressed against semantically richer teachers (e.g., CLIP-Large), at both visible and masked patch positions, in the teacher’s latent space rather than pixels (Yan et al., 2024).
• Multistage fusion losses: Some frameworks combine contrastive, masked reconstruction, and supervised multi-task losses (e.g., classification, segmentation), updating branch encoders, fusion modules, or head adapters as appropriate (Qazi et al., 14 Nov 2025, Mohsin et al., 2 Oct 2025).
• Causal and retrieval-based objectives: Causally informed variable-step forecasting leverages explicit driver–response structure in domains such as geoscience to build robust embeddings that reflect physical causality (Ravirathinam et al., 2024).

Layerwise analyses (e.g., SVCCA) reveal that early layers retain modality-specific signals (speech acoustics, text syntax) while convergence in the joint semantic space occurs at higher layers (typically beyond the mid-point in deep transformers) (Lee et al., 2024). Explicit alignment objectives and stronger, data-adaptive length reduction mechanisms further promote cross-modal convergence, particularly in well-resourced input domains.

3. Multimodal Foundation Models in Specialized Domains

Applications of MFMs span a spectrum of high-impact verticals, each with distinct modalities, data challenges, and adaptation requirements:

• Biomedical and clinical imaging: The integration of text (reports, EHR) and medical images (CT, X-ray, ultrasound, pathology, fundus) guides diagnosis and cross-modal retrieval, with models like BiomedCLIP (Zhang et al., 2023), EyeFound (Shi et al., 2024), PanDerm (Yan et al., 2024), and MerMED-FM (Zhou et al., 30 Jun 2025) unifying multiple medical domains under a common representation. Masked autoencoding and memory-based regularization are common techniques for achieving data-efficient adaptation.
• Human behavior and activity understanding: AURA-MFM aligns IMU, video, motion capture, and text into a shared space, dramatically improving zero-shot and transfer learning for detailed human activity recognition (Matsuishi et al., 29 May 2025). Cross-modal contrastive objectives and transformer-based sequence encoders are key.
• Geospatial and remote sensing: Geospatial FMs merge multispectral, SAR, hyperspectral, LiDAR, and vision-language modalities, often with pretraining strategies targeting multi-granularity contrastive alignment and cross-modal attention for robust change detection, segmentation, captioning, and VQA (Yang et al., 27 Oct 2025). Causally informed pretraining (e.g., CI-VSF) boosts robustness under distribution shift by internalizing driver–response physical relationships (Ravirathinam et al., 2024).
• Wireless communications and ISAC: Multimodal wireless FMs harmonize IQ streams, image-like spectrograms, and other raw radio modalities via unified ViT backbones and masked modeling objectives, supporting joint localization, signal classification, and activity inference (Aboulfotouh et al., 19 Nov 2025, Farzanullah et al., 29 Dec 2025).
• Civil aviation: AviationLMM demonstrates scale, robustness, and privacy-preserving fusion across voice, radar, telemetry, video, and structured text for mission-critical situational awareness and incident reconstruction (Li et al., 14 Jan 2026).
• Recommender systems and personalization: Architectures such as VIP5 wrap CLIP-like features, text, and user/item IDs into a single seq2seq interface, with parameter-efficient adapters enabling rapid modularity across tasks (Geng et al., 2023).
• Generalist and AGI-oriented systems: Foundation models such as BriVL (Fei et al., 2021, Lu et al., 2022), MIO (Wang et al., 2024), and CommGPT (Jiang et al., 26 Feb 2025) pursue tool-free any-to-any understanding and generation, leveraging large-scale weakly correlated internet data, multimodal tokenization, and flexible instruction-following for open-ended reasoning and generation.

4. Representation Analysis, Cross-Modal Gaps, and Generalization Limits

MFMs are evaluated not only on downstream supervised tasks but also via intrinsic analyses of their internal representations:

• Modality and language gaps: Empirical SVCCA analyses expose that text encoders achieve greater cross-lingual agnosticism than speech encoders, due to speaker and recording variabilities (Lee et al., 2024). The modality gap (text–speech) often exceeds the language gap (cross-language, fixed modality), unless models are explicitly trained for modality-agnostic convergence. SONAR, with explicit cross-modal losses, is able to shrink the modality gap below the language gap—a desirable property for robust transfer across both axes.
• Low-resource and domain-shift performance: The effectiveness of cross-modal convergence and adaptation is typically reduced in low-resource settings. The benefits of pooling or length adaptation (e.g., speech–text alignment) observed in high-resource languages largely vanish in low-resource cases (Lee et al., 2024). Data scale and diversity, along with effective domain-specific adaptation modules, are critical levers.
• Neural encoding and brain similarity: Multimodal pretraining yields representations more predictive of neural activations in multisensory/multimodal integration areas of the human brain, suggesting improved model “brain-likeness” relative to unimodal backbone controls (Lu et al., 2022).
• Interpretability and diagnostic tools: Tools such as SVCCA, attention heatmaps, memory module statistics, and probe-based feature attribution are increasingly standard for interrogating model convergence and abstraction properties (Lee et al., 2024, Zhou et al., 30 Jun 2025).

5. Downstream Adaptation, Parameter Efficiency, and Training Protocols

MFMs are typically pretrained once using massive unlabeled or weakly labeled data and then adapted either via lightweight task-specific heads, parameter-efficient adapters (e.g., LoRA, bottleneck MLPs), or supervised fine-tuning of selected layers:

• Adapter modularity: Lightweight adapters provide fine-grained control for adding new domains, tasks, or input types with minimal overhead (<5% extra parameters per extension), while maintaining the integrity of pretrained representations (Qazi et al., 14 Nov 2025, Geng et al., 2023).
• Late fusion and projection heads: Linear heads or “heads” comprised of simple MLPs are commonly appended to pretrained joint representations for classification, retrieval, or regression tasks, with fine-tuning restricted to the head and, occasionally, the final backbone layers (Matsuishi et al., 29 May 2025, Shi et al., 2024, Zhou et al., 30 Jun 2025).
• Memory and batch balancing: When aggregating diverse modalities or domains in training, balanced sampling and explicit memory modules are deployed to avoid dominance by well-represented classes or modalities (Zhou et al., 30 Jun 2025).
• Evaluation strategies: Models are assessed via metrics such as mean AUROC, sensitivity, and specificity in clinical imaging (Zhou et al., 30 Jun 2025); recall, CIDEr, BLEU in vision-language retrieval and captioning (Zhang et al., 2023, Wang et al., 2024); and alignment metrics (SVCCA, t-SNE clustering, cosine similarity) for joint space convergence (Lee et al., 2024, Farzanullah et al., 29 Dec 2025).

6. Open Challenges, Evaluation, and Design Implications

Despite marked progress, several key challenges and actionable design implications remain:

• Cross-modal abstraction under data scarcity: Achieving true modality-invariant and language-invariant semantics for underrepresented languages or rare modalities demands new approaches to length-adaptive pooling, tokenizer coverage extension, and joint pretraining (rather than freezing branches) (Lee et al., 2024).
• Causal and domain-aware representations: Integrating explicit domain knowledge (e.g., driver–response dynamics) via causally informed objectives improves downstream robustness, particularly under distribution shift and inter-domain transfer (Ravirathinam et al., 2024).
• Scalability and computational constraints: Model size, pretraining duration, and tokenization strategies are strong determinants of generalization, but require careful management to avoid inefficacies and overfitting (Yan et al., 2024, Yang et al., 27 Oct 2025).
• Trustworthiness, privacy, and interpretability: Application in safety-critical domains necessitates robust calibrations, privacy-preserving training (e.g., DP-SGD, federated split learning), and evidence-tracing for model outputs (Li et al., 14 Jan 2026). Governance tools maintain auditability for clinical/professional deployment (Mohsin et al., 2 Oct 2025).
• Ethical and regulatory considerations: Particularly for modalities intersecting with human data (medical, aviation, geospatial), federated learning, data sharing protocols, and regulatory frameworks are emerging as required adjuncts to technical developments (Li et al., 14 Jan 2026, Yang et al., 27 Oct 2025).

Adoption of intrinsic analysis tools such as SVCCA, robust evaluation across low-resource and extreme domain-shift settings, and the integration of causal, adaptive, and privacy-preserving modules are converging as best practices.

7. Representative Models and Quantitative Performance

Model	Modalities	Pretraining Obj.	Notable Results	Reference
BiomedCLIP	Image, Text	Dual-encoder contrastive	SOTA on biomedical retrieval/classification/VQA	(Zhang et al., 2023)
AURA-MFM	Video, MoCap, IMU, Text	Multibranch contrastive	0.62 F1 / 0.73 Acc zero-shot HAR	(Matsuishi et al., 29 May 2025)
SONAR	Speech, Text	Cross-modal/lingual constr.	SVCCA cross-modal ≥0.93 after pooling/alignment	(Lee et al., 2024)
EyeFound	Ophthalmic images	Masked autoencoding	0.955 AUROC (glaucoma fundus), SOTA zero-shot VQA	(Shi et al., 2024)
PanDerm	Skin images (4)	Latent reg. to CLIP	Exceeds best models with 10% labeled data	(Yan et al., 2024)
MerMED-FM	Med images (7)	Memory-based SSL	AUROC 0.988 (OCT), 0.943 (CT), 0.951 (US)	(Zhou et al., 30 Jun 2025)
MIO	Text, Image, Speech, Video	Autoregressive DIDO	65.5 VQAv2, 120.4 CIDEr, 6.3% WER ASR	(Wang et al., 2024)
Multimodal WFM	Spectrograms, IQ	Masked wireless model	90.6% bal. acc. (classification); 48.5% loc. err.	(Aboulfotouh et al., 19 Nov 2025)
AviationLMM	Audio, Video, Tracks, Text	Masked modeling, contrastive, fusion	Multimodal reasoning, privacy, synthetic generation	(Li et al., 14 Jan 2026)

These models exemplify the core principles, architectural trends, and performance benchmarks that now define the state-of-the-art in multimodal foundation models.

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In summary, MFMs advance unified, parameter-efficient abstraction across diverse data sources, leveraging self- and contrastive learning, adaptable fusion, and increasingly rigorous evaluation strategies. Persistent challenges focus on optimization for low-resource and asynchronous domains, causal structure learning, privacy-preserving collaboration, and trustworthiness in real-world deployment (Lee et al., 2024, Zhang et al., 2023, Aboulfotouh et al., 19 Nov 2025, Yan et al., 2024, Zhou et al., 30 Jun 2025, Ravirathinam et al., 2024, Mohsin et al., 2 Oct 2025, Li et al., 14 Jan 2026, Jiang et al., 26 Feb 2025, Yang et al., 27 Oct 2025, Wang et al., 2024).