Multimodal Large Models: Architecture & Challenges

• Multimodal Large Models are large-scale neural architectures that process and generate outputs across multiple data modalities, including text, images, video, and audio.
• They employ diverse fusion strategies—early, intermediate, and late fusion—to integrate heterogeneous signals and enable tasks such as visual question answering and image captioning.
• Advanced designs leverage self-attention, cross-attention, and state-space models to overcome computational bottlenecks and improve scalability, robustness, and cross-modal reasoning.

A Multimodal Large Model (MLM) is a large-scale neural model—commonly a transformer-based architecture or scalable variant—capable of ingesting, jointly representing, and generating across two or more data modalities, including text, images, video, audio, and various physiological or structured signals. By integrating these heterogeneous sources, MLMs enable capabilities fundamentally inaccessible to unimodal systems, such as cross-modal reasoning, multi-stream generation, and embodied perceptual understanding (Wang et al., 2 Aug 2024).

1. Definitions, Modalities, and Problem Scope

Multimodal Large Models are defined by the unified, joint processing of modalities such as:

• Text (token sequences, sentences)
• Vision (images, patch grids, image embeddings)
• Video (temporal frame streams, optionally with audio)
• Audio (speech, environmental sound, musical signals)
• Physiological/Scientific Signals (EEG, EKG, omics, etc.)

A model qualifies as “multimodal large” when it (i) possesses sufficiently high-capacity architectures (hundreds of millions to tens of billions of parameters), and (ii) supports cross-modal mapping, fusion, and reasoning—such as vision-language question answering, image captioning, audio–text retrieval, or video-based commonsense inference (Wang et al., 2 Aug 2024).

Modern MLMs extend beyond dual-modality to vision+language+audio, include explicit tool-use for integrating pretrained “expert” encoders, and address tasks including retrieval, free-form generation, active perception, and spatial reasoning.

2. Core Architectural Paradigms

MLMs are structured into compositionally layered pipelines, with precise data flow and modality interaction mechanisms.

2.1 Architectural Types

• Encoder-Only (Two-Stream):

Independent modality encoders (e.g., CLIP-style vision encoder, BERT-style text encoder) map each input to a latent space. Joint representations are obtained via contrastive learning, shallow cross-attention, or similarity scoring. Outputs are typically fused by joint embedding or late fusion (Wang et al., 2 Aug 2024).

• Encoder–Decoder:

A single transformer stack encodes concatenated multimodal sequences (text, image patches). Output tokens are produced by an autoregressive decoder that attends over the fused multimodal memory. Instruction-tuned LLMs, such as GPT derivatives, fall in this category (Wang et al., 2 Aug 2024).

• Co-Training / Tool Use (Agentic MLMs):

LLMs orchestrate calls to pretrained domain-specific tools (e.g., a vision model, an audio recognizer) via APIs. The LLM routes modality-specific data, fuses the results, and produces hybrid outputs (Wang et al., 2 Aug 2024).

2.2 Key Computational Operators

At the core, most architectures rely on multi-head self-attention and cross-attention:

$$\mathrm{Attention}(Q,K,V) = \mathrm{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V$$

where $Q$, $K$, $V \in \mathbb{R}^{n\times d}$ and $d_k$ is the key dimension.

• Cross-modal Attention: Typical implementation lets, e.g., text tokens as queries project onto visual keys and values (for text→image) or applies mutual co-attention in both directions (Wang et al., 2 Aug 2024).

State-space models (e.g., Mamba) have recently been substituted for self-attention to achieve linear scaling in sequence length, dramatically improving throughput and enabling efficient long-context processing. In these systems, sequential recurrence replaces attention’s quadratic activation map (Huang et al., 29 Jul 2024, Qiao et al., 20 Mar 2024).

3. Multimodal Fusion: Taxonomy and Mathematical Formulation

Fusion is the critical step for successful multimodal learning.

3.1 Fusion Paradigms

• Early Fusion: Concatenate raw/unified representations and jointly encode.
• Intermediate Fusion: Merge features from modality-specific encoders mid-network.
• Late Fusion: Merge independent predictions or representations post-encoding.
• Joint Embedding: Project all modalities into a contrastively aligned latent space (e.g., CLIP) (Wang et al., 2 Aug 2024).

3.2 Explicit Fusion Functions

Typical mathematical forms include:

• Linear Projections:

$$h = \sigma( W \cdot [x_\text{text}; x_\text{image}] + b )$$

• Gated Summation:

$$g = \sigma(W_g x_\text{text} + U_g x_\text{image} + b_g),\quad h = g \odot (W_t x_\text{text}) + (1-g)\odot (W_i x_\text{image})$$

• Bilinear/Tensor Fusion: Higher-order tensor contractions (e.g., MFB, MCB) for capturing multiplicative cross-model interactions (Wang et al., 2 Aug 2024).

In practical state-space-based MLLMs, 2D “vision selective scanning” mechanisms (e.g., bidirectional/cross scans over patch grids) recast visual tokens into a sequence compatible with 1D causal SSM processing, with subsequent MLP projections and gating (Huang et al., 29 Jul 2024).

4. Benchmarks, Quantitative Performance, and Task Diversity

MLMs are routinely evaluated on multi-modal datasets representing vision, language, audio, and increasingly, video and physiological data.

4.1 Common Benchmarks

Task	Example Datasets
Visual Question Answering	VQAv2, OK-VQA, GQA
Image Captioning	COCO Captions, Conceptual Captions
Video QA/Captioning	MSR-VTT, ActivityNet-QA, NExT-QA
Audio–Text Retrieval	AudioCaps, WavCaps, Clotho
Speech Recognition	LibriSpeech, GigaSpeech, MuST-C

4.2 Example Performance Table

Model	VQA Acc (%)	COCO BLEU-4	MSR-VTT CIDEr	AudioCaps BLEU
MiniGPT-4	72.5	33.1	—	—
InstructBLIP	74.2	34.7	—	—
LLaVA	75.8	36.2	—	—
NeXT-GPT	—	—	43.5	22.1
Video-LLaMA-2	68.0	—	45.0	—
SALMONN	—	—	—	28.4

Most recent MLMs (e.g., LLaVA, InstructBLIP, NeXT-GPT, Video-LLaMA, SALMONN) achieve state-of-the-art accuracy on text–vision–audio tasks and exhibit strong generalization to novel multi-modal combinations (Wang et al., 2 Aug 2024).

5. Identified Challenges and Bottlenecks

Several technical obstacles limit current MLMs’ scalability and robustness:

• Data Alignment and Calibration: Web-scale image-text/video-text pairs are often coarsely or noisily aligned, promoting spurious correlations.
• Computational and Memory Scaling: Quadratic attention in transformers becomes prohibitive with long sequences and high-res inputs; large model size and multi-modal token streams strain memory and latency budgets (Wang et al., 2 Aug 2024).
• Robustness to Noise and Domain Shift: Models pretrained on web data underperform in specialized domains (medical, satellite, low-resource languages).
• Redundancy in Visual Tokens: There is significant redundancy among visual tokens, evidenced by minimal VQA accuracy losses when dropping up to 70% of tokens via average pooling or advanced compression modules (Chen et al., 28 Jun 2024, Wang et al., 5 Jan 2025).
• Perceptual/Cognitive Gaps: MLMs display a gap between surface-level perceptual mastery (e.g., image captioning) and genuine higher-order cognition, such as sarcasm or pragmatic inference (Zhang et al., 29 May 2025).

6. Future Directions and Research Frontiers

Key open problems and research thrusts include:

• Enhanced Fusion Mechanisms: Learnable, dynamic cross-modal adapters, routing networks, and submodality-aware attention are critical for deep, context-aware fusion (Wang et al., 2 Aug 2024).
• Efficient Scaling: Hybrid transformer–state-space backbones, model pruning, quantization, knowledge distillation, and mixture-of-experts LLMs aim to reduce complexity while maintaining expressivity (Zhou et al., 13 Nov 2024, Huang et al., 29 Jul 2024).
• Token Compression: Plug-and-play token reduction methods (e.g., FOLDER, LLaVolta) remove redundant visual tokens, accelerating inference and training while preserving or improving accuracy (Wang et al., 5 Jan 2025, Chen et al., 28 Jun 2024).
• Unified Multi-Modal Representation: Unified token schemas (including tasks and region grounding) allow generic, scalable architectures to support an open-ended task set, decoupling core LLM knowledge from modality-specific expert modules (Li et al., 5 Aug 2024).
• Distributed and Resource-Constrained Deployment: Paradigms such as token-based communication for edge networks and modular distributed frameworks (e.g., Cornstarch) facilitate scalable, bandwidth-efficient, and parallelized MLM training (Zhang et al., 6 May 2025, Jang et al., 14 Mar 2025).
• Evaluation and Trustworthiness: New evaluation metrics are needed for non-text modalities; human-aligned, self-supervised, and learned perceptual metrics must be developed for comprehensive benchmarking (Han et al., 29 May 2025).
• Domain and Graph Integration: MLMs are being extended to handle graph-structured multimodal data, exploiting node-level, edge-level, and relational reasoning across text, vision, and other modalities (Liu et al., 12 Jun 2025).
• Ethics and Fairness: Algorithmic bias, privacy (especially in medical/physiological deployments), and robust generalization to previously underrepresented signals (e.g., haptics, EEG) are vital open issues (Wang et al., 2 Aug 2024).

7. Outlook

Multimodal Large Models constitute a foundational technology for real-world AI, underpinning progress in embodied intelligence, human–AI collaboration, and general-purpose reasoning. The field is converging on architectures that harmonize self-supervised joint representation learning, dynamic modularity, efficient scaling, and robust alignment across modalities. Overcoming longstanding challenges in data alignment, scaling, cognitive grounding, and evaluation will be decisive in realizing trustworthy, adaptive, and scientifically grounded multimodal intelligence (Wang et al., 2 Aug 2024).