Natively Multimodal Transformer Models: Architecture, Training, and Deployment

Multimodal transformer models are designed to process and produce multiple data modalities (text, images, audio, video, etc.) within a single unified system. This report provides a deep dive into state-of-the-art natively multimodal transformers, covering their architecture, training methodologies, and practical deployment considerations. It is intended for deep learning researchers interested in implementing such models from scratch, with detailed explanations and citations of recent work.

1. Architectural Design of Multimodal Transformers

Modern multimodal transformers integrate heterogeneous inputs through carefully designed architectures. Two broad architectural paradigms have emerged for natively multimodal models (Understanding Multimodal LLMs) (Understanding Multimodal LLMs):

• Unified Embedding (Single-Transformer) Architecture: All modalities are mapped into a shared token space and fed into one transformer (usually decoder-only) as a single sequence (Understanding Multimodal LLMs). For example, an image is converted into a sequence of patch embeddings with the same dimension as text token embeddings, then concatenated with text tokens so that a GPT-like model can attend to both uniformly (Understanding Multimodal LLMs). Microsoft’s Kosmos-1 follows this approach: it uses a causal Transformer LM backbone and simply inserts image embeddings (delimited by special <image> tokens) into the text stream (Language Is Not All You Need Aligning Perception with Language Models | Qiang Zhang) (Language Is Not All You Need Aligning Perception with Language Models | Qiang Zhang). This way, the model “perceives general modalities in a unified way” as a single sequence of tokens (Language Is Not All You Need Aligning Perception with Language Models | Qiang Zhang). Recent research even shows that a sufficiently large LLM can learn to encode image patches directly via a linear projection without a separate vision backbone (as in the Fuyu-8B model) (Understanding Multimodal LLMs), simplifying the architecture by letting the transformer itself learn visual features.

• Cross-Modality Attention (Two-Stream) Architecture: A transformer processes one modality (often text) while attending to encoded representations of another modality via cross-attention layers (Understanding Multimodal LLMs). Typically, modality-specific encoders (e.g. a Vision Transformer for images, a speech encoder for audio) produce latent features which are injected into the language model’s layers through cross-attention. DeepMind’s Flamingo is a prime example: it combines a frozen pretrained vision encoder and a frozen LLM, with learned gated cross-attention layers that allow the text decoder to attend to visual features (Tackling multiple tasks with a single visual language model - Google DeepMind). This insertion of cross-attention “bridges” the modalities inside the transformer’s attention blocks (analogous to encoder-decoder attention in the original Transformer for translation (Understanding Multimodal LLMs)). The result is a multi-modal model that can accept interleaved image/text inputs and generate language outputs conditioned on images (Tackling multiple tasks with a single visual language model - Google DeepMind) (Tackling multiple tasks with a single visual language model - Google DeepMind).

(Understanding Multimodal LLMs) Two core architecture paradigms for multimodal transformers: (A) unified embedding (all modalities as a single sequence of token embeddings into a GPT-like model) vs. (B) cross-modality attention (separate modality encoders with cross-attention integration in a GPT-like model) (Understanding Multimodal LLMs).

1.1 Cross-Modal Attention Mechanisms

In cross-modality architectures, attention mechanisms are extended to handle multiple inputs. In self-attention, the queries, keys, and values all come from the same sequence, whereas in cross-attention the queries come from one sequence and keys/values from another (Understanding Multimodal LLMs). Multimodal transformers leverage this by mixing sequences from different modalities: e.g. a text decoder’s attention heads query visual token embeddings as an additional source (Understanding Multimodal LLMs) (Understanding Multimodal LLMs). The design often mirrors the encoder-decoder setup in translation, except the “encoder” is replaced by a modality-specific module (like an image encoder) (Understanding Multimodal LLMs) (Understanding Multimodal LLMs). For instance, Flamingo uses a Perceiver Resampler to compress image features and then inserts multiple cross-attention layers throughout the LLM stack, enabling continuous conditioning on one or many images (Tackling multiple tasks with a single visual language model - Google DeepMind). Gating mechanisms can modulate these cross-attention inputs, preventing the language model from being overwhelmed by visual features early in training (Flamingo - Intuitively and Exhaustively Explained). Overall, cross-modal attention allows richly fusing modalities at intermediate layers instead of only at the input or output, which tends to improve fine-grained alignment between, say, image regions and words.

1.2 Shared Embedding Spaces and Tokenization Strategies

No matter the high-level architecture, modalities must be represented in a compatible form for a transformer. This typically means mapping each modality to a sequence of vector embeddings in a common dimensional space. Key techniques include:

• Modality-specific encoders: Each input type (text, image, audio, etc.) may have a dedicated encoder that produces features the transformer can ingest (A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks) (A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks). For text, this is just the standard tokenizer + embedding lookup (e.g. WordPiece or BPE embeddings) (Understanding Multimodal LLMs). Images are often split into patches (e.g. 16×16 pixels) which are flattened and passed through a Vision Transformer (ViT) to yield a sequence of patch embeddings (Understanding Multimodal LLMs). A linear projection then may adjust the patch embeddings to the model’s hidden size so that image tokens have the same dimension as text tokens (Understanding Multimodal LLMs). This is exactly how many image-text LLMs work: a frozen CLIP or ViT encodes the image, and a learned linear layer maps the image features into the LLM’s embedding space (Multimodal Large Language Models). Audio can be tokenized by converting waveforms to spectrogram “image” patches or by using a pretrained speech transformer (like HuBERT or Whisper) to produce embeddings (A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks). Video can be treated as a sequence of frame embeddings (with 2D patches per frame or 3D spatiotemporal tokens) often supplemented with an audio track encoder.

• Unified latent spaces: Some models learn a shared latent space for all modalities so that a single transformer can operate over them. For example, OpenAI’s CLIP model aligns images and text by training separate encoders whose outputs reside in the same vector space, such that matching image-text pairs have high cosine similarity (What is CLIP: Contrastive Language-Image Pre-Training? - Marqo). This contrastive alignment effectively creates a shared embedding space for vision and language. More recently, Meta’s ImageBind extended this idea to six modalities (images, text, audio, depth, thermal, and IMU data) – all are encoded into a common embedding space anchored to images (ImageBind: One Embedding Space To Bind Them All). ImageBind’s central idea is to “bind” all modalities via the image encoder: it trains each modality’s encoder to project its inputs into the same distributed representation as the image that was paired with it, using contrastive loss across modalities (ImageBind: One Embedding Space To Bind Them All). By unifying the representation space, such models enable direct comparison or fusion of different modalities’ tokens. In practice, even when modality-specific encoders exist, a projection module (linear layer or small MLP) is used to map each encoder’s output into a common size and space for the multimodal transformer (Understanding Multimodal LLMs). Keeping these encoders frozen while training only the projection (“connector”) is a common practice to integrate off-the-shelf models (Understanding Multimodal LLMs).

• Discrete tokenization: An alternative to continuous feature projection is to discretize modality signals into tokens from a finite vocabulary. For example, the VQ-VAE/VQ-GAN approach can turn images into sequences of codebook indices (as done in generative models like DALL·E). Similarly, audio waveforms can be tokenized into discrete codes (e.g. via SoundStream or EnCodec). The Unified-IO 2 model takes this approach for full generality: it tokenizes text, images, audio, video, bounding boxes, and even robot actions into a shared sequence of discrete tokens, then uses a single transformer encoder-decoder to model them (Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision Language Audio and Action) (Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision Language Audio and Action). Discrete tokens allow casting any modality as “language”; however, learning good codebooks or using pretrained tokenizers is crucial for performance.

1.3 Aligning Representations Across Modalities

A fundamental challenge is ensuring that representations from different modalities are semantically aligned so that the model can reason jointly over them. Several techniques address this:

• Contrastive learning: as mentioned, pairing modalities (like image and text) and training on a contrastive objective forces the model to bring related modalities’ embeddings together. Models like CLIP and ALIGN encode images and text separately but then train with a contrastive loss that maximizes the similarity of matching image-caption pairs while minimizing it for mismatched pairs (What is CLIP: Contrastive Language-Image Pre-Training? - Marqo). This yields a shared cross-modal feature space where, for instance, an image of a cat is near the text “cat” (What is CLIP: Contrastive Language-Image Pre-Training? - Marqo). Such pre-aligned encoders can then be used in a unified multimodal transformer with minimal further tuning. Contrastive alignment is also useful for video-language and audio-language pairs, especially when explicit supervision (e.g. captions) is available to indicate which modalities should match (Multimodal Large Language Models). For example, aligning video clips to their transcripts can be done by embedding each and using a similarity score to train matches vs. non-matches (Multimodal Large Language Models).

• Joint training with cross-modal objectives: Some transformer models are trained from scratch on multimodal self-supervised tasks that force cross-modal alignment. A notable strategy is masked multimodal modeling – extending the masked token prediction of BERT/ViT to a setting with mixed modality input. For instance, the recent 4M (Massively Multimodal Masked Modeling) approach trains a single Transformer on a combination of text, images, and other modality tokens by randomly masking a subset of tokens across all modalities and predicting them (4M: Massively Multimodal Masked Modeling | OpenReview). Because the model must use available modality information to fill in missing pieces of other modalities, it learns a shared cross-modal understanding. Similarly, models like FLAVA (Facebook AI, 2021) jointly optimize image masking, text masking, and image-text matching losses to align visual and textual representations. Another example is BLIP-2’s two-stage training: first a Q-Former (querying transformer) is trained to extract visual features relevant to text by using an image-text matching loss, aligning the vision encoder output with text semantics; then the model is trained to generate text from those features (BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models) (BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models). This staged approach was shown to bridge modality gaps effectively, outperforming earlier methods like Flamingo that relied only on image-to-text generation loss (BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models).

• Shared attention or co-attention layers: Some architectures explicitly couple modalities early on. LXMERT and ViLBERT (2019) used co-attentional transformer layers where vision and language features attend to each other, effectively intertwining the representations. By exchanging information in early layers, the model aligns modalities before the final prediction. More recent unified models often rely on the transformer’s flexibility to handle concatenated multimodal tokens and learn alignment implicitly, but careful loss balancing and design of training data (discussed next) are needed to avoid one modality dominating.

2. Training Process for Multimodal Models

Training a multimodal transformer from scratch is significantly more involved than training a text-only model. This section covers pretraining methodologies, dataset curation, multimodal fusion strategies, and avoiding modality collapse during training.

2.1 Pretraining Objectives and Methodologies

Self-supervised pretraining is the bedrock for multimodal models, as fully labeled multimodal datasets are scarce. Common objectives include:

• Masked token modeling: Generalizing BERT’s masked language modeling, the model is trained to predict masked-out tokens in a sequence that may include multiple modalities. The model must use the context from all modalities to fill in the blanks. For example, mask some image patch tokens and some text tokens and have the transformer predict both. This teaches the model to leverage cross-modal context (e.g. use the image to infer a masked word, use text to infer a masked region). Unified models like 4M adopt this approach at scale, mapping all inputs to discrete tokens and performing masked prediction across text and image tokens together (4M: Massively Multimodal Masked Modeling | OpenReview). This greatly increases the diversity of pretraining signals – the model learns vision-only patterns, language-only patterns, and crucially the correlations between them by having to solve mixed-modality gaps.

• Contrastive alignment: As discussed, many models include a contrastive loss during pretraining. This might be done on separate encoder outputs (à la CLIP), which is common when using dual encoders that later feed into a fusion model. It can also be done within a unified model by constraining that the model’s hidden representations treat the correct image-text (or audio-text) pairing as more similar than incorrect pairings. Some transformers implement this via special output heads that produce a similarity score between modality encodings, to be trained with InfoNCE loss. Contrastive pretraining on large-scale image–caption pairs (like billions of noisy alt-text images from the web) has proven extremely effective at injecting world knowledge about visual concepts into the model (What is CLIP: Contrastive Language-Image Pre-Training? - Marqo). The learned aligned embeddings then serve as a strong starting point for downstream multimodal tasks.

• Image-to-text (or speech-to-text) generation: Many multimodal transformers are additionally pretrained with a causal language modeling objective conditioned on other modalities. For instance, given an image encoding, train the model to generate a descriptive caption (similar to how GPT is trained on text, but now with an image in context). This is a multimodal extension of language modeling – the model learns to treat visual input as context for text generation. Flamingo was trained in this manner: it processed interleaved image-text sequences and learned to output the correct text for each image prompt (Tackling multiple tasks with a single visual language model - Google DeepMind). This teaches cross-modal response generation (e.g. answering visual questions, narrating videos) in a natural way. However, as noted in the BLIP-2 paper, image-to-text generation alone may be insufficient to fully align modalities (BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models). It often needs to be combined with representation-learning objectives (like contrastive or masked modeling) so the model doesn’t ignore subtle visual information.

• Multimodal instruction tuning (during pretraining): A newer trend is incorporating an instruction-following loss early on. Rather than pretraining only on generic tasks, some works inject a portion of multimodal instruction/QA data partway through pretraining (sometimes called “stage 2 pretraining”). For example, a model might be exposed to formatted prompts like “<image> [Image] <text> Question: … Answer: …” to simulate interactive use. This can help the model start aligning with the format and style needed for a conversational assistant. Typically, though, full instruction tuning is done after pretraining (covered in Section 3), since it requires high-level reasoning that benefits from a strong base model.

2.2 Dataset Curation for Multimodal Training

Obtaining large-scale multimodal data is crucial. Models like GPT-4 and others are believed to train on a combination of unimodal and multimodal corpora to reach web-scale token counts (Language Is Not All You Need: Aligning Perception with Language Models - Microsoft Research). Common data sources and curation strategies include:

• Image–Text pairs: There exist several billion-scale image caption datasets compiled from web data. LAION is one such public dataset (with subsets like LAION-400M, LAION-2B) containing images with alt-text captions. Conceptual Captions (3M, 12M) and YFCC-15M are other examples of web-scraped image descriptions. These are weakly supervised: the text wasn’t written specifically as a caption but is associated with the image, providing noisy but vast training signal. Models like CLIP, Flamingo, BLIP, and others rely heavily on such data (Tackling multiple tasks with a single visual language model - Google DeepMind). Research has shown that training on purely web data (with appropriate mixing) can yield state-of-the-art results without manual annotations (Tackling multiple tasks with a single visual language model - Google DeepMind). Additionally, curated sets like COCO Captions or Visual Genome QA are often used in smaller proportions to inject high-quality grounding.

• Interleaved multimodal documents: Efforts like Multimodal C4 (MMC4) expand the notion of “corpus” beyond pairs. MMC4 augments the text-only C4 dataset (extracted from CommonCrawl) with inline images, resulting in 103M documents containing both text and images in HTML order (An Open, Billion-scale Corpus of Images Interleaved with Text - arXiv). This allows training on longer context where images and text intermix (web pages, articles, etc.), which is important for models that need to handle sequences like “Here is a paragraph, then an image, then more text…”. Flamingo’s training data included interleaved image/text sequences from web sources, enabling its few-shot capabilities on arbitrary multimedia prompts (Tackling multiple tasks with a single visual language model - Google DeepMind). For video+text, datasets like WebVid-2M provide short web video clips with captions (often transcribed or descriptive audio). For audio, Audiocaps or automatic speech transcripts paired with audio can serve a similar role.

• Unimodal datasets as components: To reach the largest scale, multimodal training usually also leverages standard large text corpora (for pure text LM training) and image collections (for pure masked image modeling, etc.). For example, Kosmos-1 mixed “arbitrarily interleaved text and images, image-caption pairs, and text data” in its training mixture (Language Is Not All You Need: Aligning Perception with Language Models - Microsoft Research) – meaning it still used a huge amount of text-only data to not lose the general language understanding, while gradually introducing multimodal samples. The recently released PaLM-E (an embodied multimodal LM) similarly started from a giant text-only LM (PaLM) and added image/robotics data on top. Careful data balancing is needed so that the model sees enough multimodal examples to learn cross-modal skills, but not so many that it forgets or underperforms on language. Typically, a curriculum or mixing ratio is used (e.g. every training batch might contain a mix of pure text sequences and some image-text sequences).

• Pretraining task mixtures: The dataset is often annotated conceptually by task to help the model alternate between different objectives. For instance, one popular recipe is the combination of image-text contrastive data, image-text generative data, and masked multimodal data. Meta AI’s FLAVA used 70M image-text pairs for contrastive learning, 70M for image-text matching and MLM, plus image-only and text-only corpora for single-modality masked learning, all in one unified training loop. A joint training like this forces the model to avoid modality collapse by keeping each modality useful for some task – e.g. if the model were to ignore images, it would fail the image-specific masked patch prediction; if it ignored text, it would fail caption generation, etc.

2.3 Fusing Modalities During Training & Avoiding Modality Collapse

A critical aspect of training is how to feed multiple modalities into the model and ensure it learns to utilize them all:

• Early vs. late fusion: Multimodal models can fuse information at different stages (A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks). Early fusion means combining raw inputs or embeddings at the very start (as in unified token sequence models). Late fusion means processing modalities independently and merging their outputs at the final decision (e.g. ensembling predictions – not common for transformers). Intermediate fusion – such as cross-attention at some layer – is a middle ground (A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks). Empirically, joint fusion throughout (either via unified tokens or repeated cross-attention) works best for rich tasks, as it allows the model to build a truly integrated representation (A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks). Approaches like joint multimodal training with iterative fusion ensure that each modality’s features influence the other during representation learning, rather than only being combined at the end.

• Multimodal conditioning strategies: When training on multi-input tasks, the model may receive modalities simultaneously or sequentially. Some transformers simply concatenate modality tokens with markers (this requires the model to infer which token came from which modality – hence special tokens like <image> are used (Language Is Not All You Need Aligning Perception with Language Models | Qiang Zhang)). Others use parallel input streams with cross-attention to combine them. The design of prompts or input format can guide the model to attend to all inputs: for example, a prompt template “Image description: <image>. Question: <text>? Answer:” explicitly tells the model an image is provided and a question follows. During training, shuffling modality presence can also help – e.g. sometimes provide only text, sometimes text+image – so the model learns to fall back to text-only reasoning when image is absent, and use the image when it’s there.

• Preventing modality collapse: A known issue is that a powerful modality (like text, which provides a lot of prior knowledge) can dominate the model’s predictions, effectively ignoring the other modality. For example, a multimodal LM might answer a question using its parametric knowledge and completely overlook the image input – resulting in a plausible answer that doesn’t actually describe the given image (a form of hallucination). To combat this, researchers use balanced training and explicit grounding tasks. One solution is grounding supervision: e.g., train on visual-question answering data where the only way to get the answer right is to use the image, not just general knowledge. If the model tries to rely on text priors, it will often be wrong – the loss will drive it to actually look at the image. OpenAI noted that GPT-4’s vision model was trained on data that encourage identifying specific image details to avoid this failure mode. LLaVA (Large Language and Vision Assistant) addressed modality misuse by fine-tuning on detailed Q&A pairs about images, and reported that naive models often output text that is not grounded in the image until aligned properly (LLaVA-RLHF). They further introduced multimodal dialogue data so that the model must refer to the image in a conversation, reinforcing cross-modal interactions.

• Multi-task and curriculum training: One effective strategy is to train the model on a mix of multimodal and unimodal tasks, gradually increasing difficulty. Early in training, heavy use of contrastive alignment can ensure the modality representations are in sync. Later, generative tasks enforce joint reasoning. Some curricula start with simpler tasks (like describe an image) and move to harder ones (like use two images and a question). By staging the learning, the model is less likely to get stuck in a bad equilibrium where it prefers one input modality exclusively. The BLIP-2 two-stage training is a good illustration: Stage1 ensured the image encoder’s output is aligned to text by a contrastive/QA objective, before Stage2 allowed the model to freely generate text (BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models). This prevented the model from ignoring visual features when learning to generate text, solving the “modality gap” that otherwise exists with a frozen LLM (BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models).

In summary, the training process for multimodal transformers involves careful objective design and data mixing. By using large-scale self-supervised tasks (masked modeling, contrastive alignment, multimodal generation) on curated multimodal corpora, and by explicitly forcing the model to ground its understanding in each modality, state-of-the-art models learn robust joint representations.

3. Post-Pretraining: Instruction Tuning and Alignment

After core pretraining, multimodal models undergo post-training refinements to become useful assistants. These include instruction tuning across modalities, alignment via human feedback (RLHF), and specialization for efficient inference.

3.1 Multimodal Instruction Tuning

Instruction tuning trains the model to follow natural language instructions and perform specific tasks, analogous to how text-only LLMs (like GPT-3.5) are tuned on instruction-answer pairs. For multimodal models, this means fine-tuning on data where the input prompt and/or the output involve multiple modalities. Examples of instruction tuning data for multimodal assistants:

• Vision-Language Instructions: Datasets where each example is a dialogue or QA pair grounded in an image. For instance, LLaVA-Instruct contains prompts like “Look at this image [image]. What is the cat doing?” with an answer, or requests like “Describe the image in detail.” These can be harvested by converting existing VQA and caption datasets into an instruction format. LLaVA used 90k such image-instruction pairs, many generated by GPT-4 describing images (LLaVA-RLHF). They also turned visual QA datasets (VQA v2, OK-VQA) into multi-turn dialogues to simulate interactive questioning (LLaVA-RLHF). By fine-tuning on this, the model learns to parse user requests that reference an image and to respond with helpful, grounded answers.

• Audio-Language or Video-Language Instructions: Similarly, if the model handles audio, one could use datasets of spoken questions (transcribed) about an audio clip, or instructions like “Transcribe this audio” or “What is the sentiment of this voice sample?”. For video, instructions might be “Summarize the events in this video clip.” In practice, large multimodal instruction datasets are often bootstrapped: e.g., use a strong model to generate responses to prompts involving a modality (“synthetic” instruction following data). Microsoft’s Kosmos-2 and others are likely leveraging such techniques to create audio-visual instruction data.

• Format unification: During instruction tuning, the model typically is trained to produce a textual answer, even if the prompt includes other modalities. (Producing non-text outputs is a separate challenge, discussed later.) The tuning data therefore pairs multimodal inputs with text outputs (explanations, descriptions, etc.). This reinforces the model’s language generation capabilities while using multimodal understanding. By the end of this stage, the model should handle prompts like a user query that includes an image or audio snippet, and follow any textual instruction about it.

Crucially, multimodal instruction tuning is often done after a model has been partly aligned to each modality. For example, starting with a converged pretrained model (e.g. CLIP + LLM fused model) and then instruction-tuning it on a modest corpus (tens or hundreds of thousands of examples) of QA/caption data yields excellent results (as seen with LLaVA, MiniGPT-4, etc.). This stage imparts task-specific knowledge and conversational ability without needing to train on extremely large corpora. It is analogous to how InstructGPT is derived from GPT-3.

3.2 Reinforcement Learning with Human Feedback (RLHF) for Multimodal Alignment

To further align multimodal models with human preferences (making them helpful, correct, and harmless), RLHF can be applied, extending the approach used for text assistants:

• Preference data collection: Humans are shown model outputs in response to multimodal prompts and asked for preferences or ratings. For example, a user might ask a multimodal assistant “What is in this photo?” and the model produces an answer – human labelers would mark if the answer is correct and satisfies the question. Special care is needed to ensure the model’s answer is grounded in the input (not hallucinated) and is complete. One issue unique to multimodal models is visual hallucination – the model might include details not present in the image. To address this, researchers design preference tests focusing on grounding. The LLaVA-RLHF project created a benchmark called MMHal-Bench specifically to catch hallucinated image descriptions (LLaVA-RLHF).

• Reward modeling: A reward model is trained on this human preference data. Interestingly, in multimodal RLHF, the reward model itself must accept multimodal input (e.g. an image and an answer) to score it. LLaVA-RLHF introduced a “Factually Augmented” reward model that was given not just the model’s answer and the question, but also additional factual info like the image’s ground-truth caption or some reference text, to help judge correctness (LLaVA-RLHF). This was found to mitigate reward hacking and improve grounding by providing the reward model more context (LLaVA-RLHF).

• Policy optimization: The base model (policy) is then refined using RL (typically Proximal Policy Optimization or similar), using the reward model’s feedback as a signal. Through iterative updates, the model learns to prefer outputs that humans would rate higher. LLaVA-RLHF is one of the first open multimodal RLHF results, producing a model that “mimics the spirit of multimodal GPT-4” and is more helpful and less hallucinating than the base LLaVA (LLaVA-RLHF) (LLaVA-RLHF). Notably, they found that RLHF significantly reduced instances of the model mentioning things not actually in the image, and improved the correctness of visual reasoning.

• Challenges: Multimodal RLHF must ensure the model doesn’t sacrifice accuracy for safety in ways that conflict with the input modality. For example, a naive reward model might always penalize answers that say “there is nothing in the image” – but sometimes, an image might truly be blank or irrelevant. Balancing factual accuracy with politeness and refusal when appropriate is tricky. As multimodal assistants venture into open-world tasks (e.g. describing any image), RLHF helps calibrate their behavior (avoiding overly snarky captions, preventing guessing when unsure, etc.). OpenAI’s GPT-4 (vision) was likely RLHF-trained to refuse certain unsafe image requests (like identifying a person in a photo, which violates privacy).

In sum, RLHF for multimodal models aligns them more closely with human-expected responses, refining the output quality beyond what supervised data alone can do. Projects like LLaVA-RLHF (LLaVA-RLHF) and MM-RLHF are pioneering this area, demonstrating large gains in user satisfaction on image-based queries.

3.3 Fine-Tuning for Efficiency and Inference Optimization

After a multimodal model is trained and aligned, practical deployment may require optimizing it for latency, memory, and throughput. Techniques include:

• Model compression: Large multimodal transformers (tens of billions of parameters) are hard to deploy, so quantization and distillation are actively explored. Quantization reduces model size by using lower precision for weights/activations (e.g. 8-bit or 4-bit integers). However, multimodal models pose extra challenges – e.g. the vision encoder parts might be sensitive to quantization. Recent work Q-VLM showed successful post-training quantization of a 13B-parameter vision-language model (LLaVA) to 8-bit, achieving a 2.78× memory reduction and 1.44× speedup with no performance loss (Q-VLM: Post-training Quantization for Large Vision-Language Models). They did so by carefully handling cross-modal layers and using block-wise optimization to account for layer dependencies (Q-VLM: Post-training Quantization for Large Vision-Language Models) (Q-VLM: Post-training Quantization for Large Vision-Language Models). This indicates that even heavy multimodal models can be compressed for faster inference. Additionally, knowledge distillation can train a smaller model to replicate a larger model’s outputs. For instance, you might distill a 10B multimodal model into a 1B model by generating a large synthetic multimodal QA dataset and training the small model on it. This is an open research area, but analogous successes in language (Alpaca, DistilGPT) suggest feasibility.

• Latency reduction strategies: Beyond quantization, efficient transformer architectures help meet real-time constraints. Techniques like FlashAttention (optimizing attention computation) and streaming decoding (outputting tokens as they are generated) are directly applicable. A multimodal assistant can also cache encoder outputs – e.g. if an image is processed, the visual features can be reused for multiple queries about that image without re-encoding every time. In cross-attention models, one can encode the image once and keep those key/value vectors fixed while the decoder runs through its layers for each output token. This cache idea is similar to how an encoder-decoder caches encoder keys/values. It substantially cuts down repeated computation for scenarios like “interactive dialogue about a single image.” For audio, if using an encoder-decoder, the audio encoder output can be cached for subsequent decoding steps as well. Another approach is multimodal caching: if a user references the same modality content frequently (like a user’s profile picture in a chat), the system could reuse previous embeddings.

• Architecture tuning for deployment: Some research focuses on making the architecture itself more efficient for multimodal tasks. For example, Low-Rank Adapters (LoRA) have been used to add multimodal capability to LLMs with minimal overhead by injecting small learned layers instead of finetuning the whole model. This means at inference, the overhead is tiny. Also, models like MAGMA and BLIP-2 connect a frozen LLM with minimal extra parameters (Q-Former in BLIP-2 has only ~29M parameters) (BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models), which can be faster than training a giant unified model. BLIP-2 in particular is noted to be more compute-efficient, outperforming Flamingo on VQA while using 54× fewer trainable parameters (BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models). From a deployment perspective, fewer trainable (and thus active) parameters can also mean faster inference if parts of the model (like the large LLM) remain frozen or use optimized implementations.

• Edge deployment considerations: On resource-limited devices, one might not fit a full multimodal transformer. A common compromise is a hybrid pipeline: do heavy modality processing on-device with smaller models, and send intermediate results to a server-based large model. For instance, a smartphone could run a small CNN to detect objects in an image (or a small speech recognizer to get text from audio), then send the summarized information (detected objects, transcribed text) to a cloud LLM which produces the final answer. While not “end-to-end multimodal” in one model, this approach leverages multimodal AI in a practical way. When aiming to truly deploy on-edge, quantized smaller multimodal models (like 7B parameter range or below, possibly distilled) might run on high-end mobile NPUs. Techniques like Neural Architecture Search have even been explored to tailor multimodal models for efficiency, choosing lighter attention patterns or merging encoder stages.

In conclusion, after training a capable multimodal model, significant effort goes into tuning it for real-world use: aligning it with human intents (via instruction tuning and RLHF) and making it efficient enough to serve (via compression and engineering optimizations). The next section considers how these models are put into action in interactive settings.

4. Real-World Implementation Considerations

Building a multimodal assistant that can see, hear, and respond in real time raises additional challenges beyond core model training. Here we discuss practical considerations for deployment, real-time interaction, and multimodal output generation.

4.1 Synchronous Multimodal Understanding and Interaction

A multimodal assistant like OpenAI’s voice-enabled ChatGPT involves multiple simultaneous input streams – e.g. the user could be speaking (audio) while also showing an image. Achieving synchronous understanding means the system must handle and fuse these inputs in real-time:

• Pipeline vs. unified model: Currently, most deployed systems use a pipeline of specialized models rather than one unified model for everything. For example, ChatGPT with voice operates by first using a speech-to-text model (like Whisper) to transcribe audio into text, feeding that text (plus any image input) into the language model, then using a text-to-speech model to output voice. This division of labor is practical because each component can be optimized independently (Whisper is highly optimized for audio, GPT-4 for text reasoning, etc.). The downside is potential latency and loss of nuanced audio information (tone, prosody) that isn’t captured in the transcript.

• Toward unified multimodal agents: In research, there is interest in models that directly consume raw modalities. For instance, a single transformer could take the audio waveform frames and image pixels along with text, and reason jointly. Microsoft’s Kosmos-1 was a step in this direction (audio wasn’t in v1, but the vision-text integration sets the stage). Google’s Gemini is rumored to integrate vision, speech, and text in one system. The challenge is that audio and video are temporal streams – a naive approach of converting everything to tokens and concatenating would result in extremely long sequences (e.g. 5 seconds of audio at 16 kHz might be thousands of tokens if not carefully encoded). For real-time interaction, the model needs to be able to process streaming inputs and perhaps emit streaming outputs (like start speaking an answer before the user finishes talking). This is an active area of research, combining ideas from streaming speech recognition and incremental language generation.

• Multimodal context and memory: In an interactive setting, the model has to maintain context across turns that may include different modalities. For example, a user might show an image in one turn, then refer to it in a subsequent voice query (“Can you tell me more about that?”). The system must have a mechanism to remember the image (perhaps by storing its embedding or some identifier in the conversation state). Current solutions store the image in memory and prepend a description of it to the prompt in later turns (e.g. “User uploaded an image of a cat on a skateboard.”). A more native solution is to carry over the raw visual embeddings in the model’s recurrence (if it’s a transformer with attention, it might include previous turn’s image tokens as context for the next turn). Research prototypes have demonstrated transformers that handle dialog with persistent visual context, but memory length becomes a factor. Techniques like state caching or external memory modules could help scale to long multimodal conversations.

• Temporal alignment: In truly synchronous multi-sensor input (like a robot with cameras and microphones), there’s a question of how to align modalities in time. If someone is describing a part of a live video feed, the model must correlate the audio reference with the right video frame. This may require time-stamping tokens or using techniques from audio-visual speech recognition, where the model learns to match spoken words to visual events. Some solutions use cross-modal attention with alignment – e.g., the model can attend to video frames around the moment a question was asked. Another approach is to have separate subsystems that each produce a timestamped stream of semantic tokens (words from speech, object labels from vision) and then fuse those streams. Ensuring the assistant responds promptly also means it might need to begin formulating an answer before the user has completely finished providing inputs (much like how humans interject in conversation). This remains a complex systems design problem for multimodal AI.

4.2 Efficient Inference Pipelines

When deploying a multimodal model in a production environment (e.g. a cloud service or on-device app), efficiency and reliability are paramount:

• Parallelizing modality processing: Unlike a single-modal model, a multimodal system can do some work in parallel. For example, if a user query has both an image and text, the image encoder and text tokenizer can run concurrently on different hardware (GPU/TPU) threads. Only when it comes to joint inference (in an integrated model’s attention layers) do the threads synchronize. By designing the system to handle different modality inputs on separate compute streams, one can reduce end-to-end latency. In a pipeline scenario, the speech recognizer, visual analyzer, and LLM can be seen as stages that ideally overlap in time (e.g., start transcribing speech while the user is still talking, start generating answer tokens while not all image analysis is done, etc.).

• Hardware acceleration: Multimodal models benefit from specialized hardware features. For instance, GPU tensor cores or TPU can accelerate large matrix ops for transformers. Additionally, some SOCs have computer vision accelerators that can run CNNs faster than a general GPU – offloading the image encoding to those could free up GPU memory for the LLM. New accelerators that support audio processing (like spectrogram convolution) or even transformer blocks directly in hardware are emerging, which could make end-to-end multimodal inference more feasible. In edge devices, one might use the DSP for audio, NPU for vision, GPU for the transformer, leveraging each for what it does best.

• System-level optimization: Techniques such as batching and caching are also relevant. If deploying at scale, one could batch multiple multimodal queries together for the transformer forward pass (though this is complicated if each query has different images). For caching, as mentioned, the visual features of common images can be cached. Also, if using a two-stage model (encode then decode), one can pre-compute encodings. Some assistants incorporate an explicit vision encoder server – essentially a microservice that takes images and returns embedding vectors, which the LLM server then consumes. This way the heavy vision model is managed separately and can be scaled independently (e.g. many small CNN instances vs. fewer big LLM instances), improving overall throughput.

• Robustness and fallbacks: Real-world inputs can be noisy – e.g. blurry images, accents or background noise in audio. A practical system might have to detect when one modality’s data is unusable and not let that confuse the whole model. For example, if OCR on an image fails, the system should handle it gracefully (perhaps ask the user for clarification or try an alternative strategy). Implementing confidence measures for each modality (like an image captioning confidence, speech recognition confidence) and feeding those into the decision process can improve reliability. Some multimodal models are being trained to self-diagnose modality quality – e.g., if an image is just a blank square, the model’s vision-attention outputs might be almost zero, which could trigger the model to respond “I’m not sure what’s in the image.” Designing the model or surrounding system to handle such cases is critical for a polished assistant.

4.3 Model Distillation and Edge Deployment

When aiming to deploy multimodal models on edge devices (phones, AR glasses, robots), size and efficiency constraints become extreme:

• Distillation to smaller architectures: A large multimodal model (say 10B parameters) might be distilled into a Mixture-of-Experts or multi-stage small model on edge. For example, instead of one big model that does everything, an edge assistant could use a 100M-parameter text-audio model for voice chat and only invoke a heavier cloud vision model when needed. There is research on training unified small models (~hundreds of millions of params) that can do basic vision+language tasks. The MobileViL and other efficient VL models attempt to prune and compress vision transformers and use smaller LMs (like DistilBERT) for language. Such models can handle simple multimodal instructions offline (e.g. “take a photo and read the text on it” on a phone).

• Federated and distributed inference: In some scenarios, part of the model can run locally and part in the cloud, as mentioned. For instance, privacy might dictate that images never leave the user’s device – so the device runs a vision model to extract features, and only those features (which ideally cannot reconstruct the image) are sent to the cloud LLM. This split computation requires careful calibration (the feature should be rich enough for the LLM to answer questions). Some projects use feature-sharing approaches where both device and cloud run parts of the same transformer (splitting layers between device and server). This is tricky with latency but has been explored in split neural networks.

• Real-time outputs (e.g. speech): On edge, generating speech from the assistant’s text output is an additional step. Modern TTS models (like Tacotron, WaveGAN) can be large, but there are efficient ones that run in real-time on phones. Integrating TTS with the multimodal model means ensuring the text output is delivered as it’s generated to the TTS system. If the multimodal model streams its text output token by token, a low-latency TTS can start speaking before the sentence is complete, reducing perceived delay. This requires the model to produce fluent, incremental responses (avoid long pauses or revisions mid-sentence). Techniques like incremental decoding or having the model predict an end-of-sentence token to signal when to stop can assist in making the voice response feel natural.

• Safety and privacy: Real-world deployment must handle sensitive content. For example, a multimodal model could inadvertently identify a person in an image or describe protected attributes – this is often disallowed (OpenAI’s vision model refuses to identify real people or guess sensitive traits). So the model or an external filter must detect and sanitize such outputs. This might involve a combination of computer vision detectors (for faces, license plates, etc.) and policy rules on the language model’s outputs. There is ongoing work on embedding safety constraints into the model itself via fine-tuning so that it “knows” not to do certain things with certain inputs. Similarly, user privacy on device might require that no images/audio are stored or that the model runs locally when dealing with personal data.

4.4 Generating Seamless Multimodal Outputs

Finally, an advanced multimodal assistant might not be limited to text (or spoken text) as output – it could potentially generate images, video, or other modality responses:

• Image generation and editing: A multimodal model could take a text or image query and return a generated image (for example, “Draw me a picture of a sunset on Mars”). Current systems often integrate with separate diffusion models (like DALL·E) to achieve this. However, research like Unified-IO 2 shows models that can output images in token form: Unified-IO 2 is trained to produce images by generating a sequence of discrete visual tokens (via a VQGAN) as output, in addition to text or audio outputs (Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision Language Audio and Action) (Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision Language Audio and Action). This means a single transformer can potentially decide to answer with a paragraph or with an image, depending on the prompt. In practice, this capability is nascent – such models exist in research but not yet in mainstream assistant products. Nonetheless, we see the trend: models like Google’s Gemini are expected to handle both visual generation and understanding, enabling dynamic multimodal dialogues (e.g., the user sketches something, the assistant refines it into a nicer image, etc.).

• Audio and speech outputs: Text-to-speech as a separate module is the norm, but there are efforts to have transformers generate audio waveforms directly. For example, Meta’s Voicebox and Google’s speech models can produce audio from text in one model. A unified multimodal model could in theory take text and a desired speaker identity and output audio tokens (e.g. generate a waveform or a sequence of audio codec tokens). Unified-IO 2 also included audio output in its training (predicting spectrogram tokens) (Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision Language Audio and Action). The main advantage of a single model doing this is tighter integration – the model can modulate prosody or decide to play a sound effect, etc., as part of its reasoning. However, the quality of specialized TTS models currently exceeds any joint model, so most systems stick to a pipeline where the LLM produces text and a separate high-quality TTS voices it.

• Embodied actions: In robotics or AR, the “output” might be an action (move a robot arm, navigate a cursor). Multimodal transformers like PaLM-E have been trained to output embodied action sequences (like continuous controls or discrete commands) conditioned on vision and text (What you need to know about multimodal language models - TechTalks) (What you need to know about multimodal language models - TechTalks). In deployment, this means the model’s output is fed to an actuator or another system. Such outputs must be tightly constrained for safety (you’d have a verification layer so the robot doesn’t do something unexpected). This is a niche application but demonstrates the versatility of these models.

• Mixed modality responses: A truly seamless assistant could respond with a combination of modalities – e.g. “Let me show you – [generated image] – here’s a diagram, and I’ll also explain it in words: … [spoken explanation]”. Achieving this requires coordinating multiple output heads. One approach is a multi-pass generation: first have the model generate any non-text modalities (images) internally, then include them in the prompt and generate the text description referencing them. Another approach is architecting the model with multiple output tokens types (some tokens represent image pixels, some represent text). Unified-IO 2’s encoder-decoder can produce interleaved outputs (like an image with an audio caption). The field hasn’t standardized this yet, but research is quickly moving towards multimodal output. In fact, the authors of Unified-IO 2 report state-of-the-art results on tasks spanning image generation, captioning, audio understanding, and even robotic manipulation with one model (Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision Language Audio and Action) (Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision Language Audio and Action).

In summary, real-world multimodal assistants currently rely on complex pipelines and careful engineering to meet latency and quality requirements. But ongoing advances in unified architectures and training (e.g. models like Kosmos-2, Gemini, Unified-IO 2) are rapidly pushing the boundary of end-to-end multimodal intelligence. We can envision a near future where a single transformer-based agent can fluidly converse with a user, seeing what they see, hearing what they say, and responding with words, images, or sounds as needed. Achieving this at scale will require combining the architectural innovations, training techniques, and deployment strategies discussed above.

References: The content above synthesizes findings from numerous recent works. Key references include Sebastian Raschka’s overview of multimodal LLM architectures (Understanding Multimodal LLMs) (Understanding Multimodal LLMs), the BLIP-2 approach with Q-Former for vision-language alignment (BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models) (BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models), OpenAI’s CLIP for shared image-text embeddings (What is CLIP: Contrastive Language-Image Pre-Training? - Marqo), the LLaVA and LLaVA-RLHF studies on instruction-tuning and RLHF for vision-language models (LLaVA-RLHF) (LLaVA-RLHF), and the Unified-IO 2 model illustrating scaling to diverse modalities (Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision Language Audio and Action) (Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision Language Audio and Action), among others. These demonstrate the state of the art in designing and training multimodal transformers, as well as the emerging best practices for aligning and deploying them as truly multimodal assistants. Each advancement brings us closer to AI systems with more human-like perceptual and communicative abilities across domains.

4.1 Synchronous Multimodal Understanding and Interaction

A multimodal assistant like OpenAI’s voice-enabled ChatGPT involves multiple simultaneous input streams – e.g. the user could be speaking (audio) while also showing an image. Achieving synchronous understanding means the system must handle and fuse these inputs in real-time:

• Pipeline vs. unified model: Currently, most deployed systems use a pipeline of specialized models rather than one unified model for everything. For example, ChatGPT with voice operates by first using a speech-to-text model (like Whisper) to transcribe audio into text, feeding that text (plus any image input) into the language model, then using a text-to-speech model to output voice. This division of labor is practical because each component can be optimized independently (Whisper is highly optimized for audio, GPT-4 for text reasoning, etc.). The downside is potential latency and loss of nuanced audio information (tone, prosody) that isn’t captured in the transcript.

• Toward unified multimodal agents: In research, there is interest in models that directly consume raw modalities. For instance, a single transformer could take the audio waveform frames and image pixels along with text, and reason jointly. Microsoft’s Kosmos-1 was a step in this direction (audio wasn’t in v1, but the vision-text integration sets the stage). Google’s Gemini is rumored to integrate vision, speech, and text in one system. The challenge is that audio and video are temporal streams – a naive approach of converting everything to tokens and concatenating would result in extremely long sequences (e.g. 5 seconds of audio at 16 kHz might be thousands of tokens if not carefully encoded). For real-time interaction, the model needs to be able to process streaming inputs and perhaps emit streaming outputs (like start speaking an answer before the user finishes talking). This is an active area of research, combining ideas from streaming speech recognition and incremental language generation.

• Multimodal context and memory: In an interactive setting, the model has to maintain context across turns that may include different modalities. For example, a user might show an image in one turn, then refer to it in a subsequent voice query (“Can you tell me more about that?”). The system must have a mechanism to remember the image (perhaps by storing its embedding or some identifier in the conversation state). Current solutions store the image in memory and prepend a description of it to the prompt in later turns (e.g. “User uploaded an image of a cat on a skateboard.”). A more native solution is to carry over the raw visual embeddings in the model’s recurrence (if it’s a transformer with attention, it might include previous turn’s image tokens as context for the next turn). Research prototypes have demonstrated transformers that handle dialog with persistent visual context, but memory length becomes a factor. Techniques like state caching or external memory modules could help scale to long multimodal conversations.

• Temporal alignment: In truly synchronous multi-sensor input (like a robot with cameras and microphones), there’s a question of how to align modalities in time. If someone is describing a part of a live video feed, the model must correlate the audio reference with the right video frame. This may require time-stamping tokens or using techniques from audio-visual speech recognition, where the model learns to match spoken words to visual events. Some solutions use cross-modal attention with alignment – e.g., the model can attend to video frames around the moment a question was asked. Another approach is to have separate subsystems that each produce a timestamped stream of semantic tokens (words from speech, object labels from vision) and then fuse those streams. Ensuring the assistant responds promptly also means it might need to begin formulating an answer before the user has completely finished providing inputs (much like how humans interject in conversation). This remains a complex systems design problem for multimodal AI.

I’d like to follow up on several specific implementation details mentioned in your report that weren’t linked to published research. For each of these techniques, could you provide:

1. Any specific published research that documents these approaches (if available)
2. Whether these techniques are primarily found in proprietary systems vs. open research
3. Any technical alternatives or workarounds that might be available to the open research community

Specifically, I’m interested in:

A. Multimodal Memory Management:

• You mentioned systems that “store the image in memory and prepend a description of it to the prompt in later turns.” Are there published examples of this approach?
• The concept of “carrying over raw visual embeddings in the model’s recurrence” - is this documented in any research papers?
• What are the most promising approaches for maintaining multimodal context over long conversations?

B. Temporal Alignment:

• You described “time-stamping tokens” and “cross-modal attention with alignment” for synchronizing multiple input streams. Are these theoretical approaches or implemented in current systems?
• Are there documented techniques for how models “begin formulating an answer before the user has completely finished providing inputs”?
• How do current multimodal assistants like OpenAI’s Advanced Voice Mode handle simultaneous visual and audio inputs?

C. Deployment Optimizations:

• The “vision encoder server” microservice architecture you mentioned - is this a common pattern documented anywhere?
• How do production systems implement “confidence measures for each modality” in their fusion processes?
• What specific hardware acceleration strategies are most effective for multimodal inference?

D. Multimodal Output Generation:

• You noted “the field hasn’t standardized” approaches for generating mixed modality responses - what are the leading approaches currently?
• Are there any public examples of models that effectively coordinate timing between spoken explanations and visual aids?
• How do systems like GPT-4 with vision decide when to generate images versus text explanations?

This information would help clarify where the frontier of public knowledge currently lies relative to industry implementation.

Multimodal AI Systems: Technical Report

This report examines four key areas of multimodal AI systems – (A) Multimodal Memory Management, (B) Temporal Alignment, (C) Deployment Optimizations, and (D) Multimodal Output Generation. Each section discusses state-of-the-art approaches, citing published research where available, and clarifies whether techniques are from open research or proprietary implementations. Technical alternatives and workarounds for the open research community are also highlighted.

A. Multimodal Memory Management

Maintaining Visual Context Across Dialogue Turns: In multimodal dialogue, a core challenge is preserving information from images across multiple turns. A straightforward approach (often used in practical systems) is to generate a text description of an image and include that description in subsequent prompts, effectively “carrying” the image context forward. For example, Visual ChatGPT (Microsoft, 2023) introduced a Prompt Manager that injects visual information into ChatGPT’s prompt, enabling the model to handle follow-up questions about an image (Microsoft Open-Sources Multimodal Chatbot Visual ChatGPT - InfoQ) (Microsoft Open-Sources Multimodal Chatbot Visual ChatGPT - InfoQ). This approach of storing images as descriptions is a pragmatic workaround frequently seen in applications (and open-source demos) rather than a deeply studied academic method. It leverages existing language-only memory by encoding images as text, making it accessible to a language model that cannot otherwise “remember” visual content natively.

Research on Persistent Visual Memory: Academic research has explored more principled ways to retain image information across turns. The Visual Dialog task introduced by Das et al. (2017) is a classic example (Visual Dialog) (Visual Dialog). In Visual Dialog, an AI is given an image, a dialogue history, and a new question about the image; the model must answer while grounding the answer in both the image and the conversation history (Visual Dialog). One of the models proposed is a Memory Network encoder, which explicitly stores each previous Q&A pair as a “fact” in a memory bank. At each turn, the model retrieves relevant facts from this memory and attends to the image features to inform its answer (Visual Dialog). This method effectively treats the image as a constant piece of context and uses a learned memory retrieval mechanism for the dialogue – a technique documented in open research (Visual Dialog). Similarly, Zhang et al. (2020) proposed a Cross-Modal Memory Network for an embodied dialogue task, with separate memory modules for language and vision that collaboratively retrieve past visual observations and dialogue context (Vision-Dialog Navigation by Exploring Cross-Modal Memory) (Vision-Dialog Navigation by Exploring Cross-Modal Memory). These research models go beyond simply prepending text; they carry over raw visual features or embeddings in a learned memory, allowing the model’s recurrence (state) to include visual information from previous turns. In open literature, this idea is usually implemented with explicit memory modules or by feeding the image’s feature vector into the model at each turn (often concatenated with textual inputs), rather than the model literally propagating an unmodified pixel embedding through time.

Raw Visual Embeddings in Stateful Models: The concept of carrying over raw visual embeddings in a model’s recurrence (i.e. the model’s hidden state) is not widely documented in standalone form, because modern transformer-based systems typically handle context by re-encoding all relevant information each turn. Instead, transformers rely on an extended context window that can include image tokens or embeddings alongside text tokens. For instance, recent open-source vision-language models like Qwen-VL or LLaVA allow the conversation history plus image embeddings to be passed in one sequence so that the model attends to both modalities in context (VLM Support — DashInfer 2.0.0 documentation). In such cases, the image embedding is inserted as a special token in the sequence, and the model doesn’t need to re-embed the image from scratch if the embedding is cached. This is analogous to “carrying” the visual data forward, though implemented via the transformer’s self-attention on the full context. Notably, an engineering pattern in some proprietary systems is to cache the image embedding server-side and prepend a reference or description in later turns – OpenAI’s GPT-4 with vision likely retains an internal representation of the image so the user can ask follow-ups without re-uploading the image (details are not public, but the behavior suggests such a mechanism). The Alexa AI team at Amazon documented a similar idea in a production voice assistant: they augment a text-based dialogue system with a vision module that computes CLIP embeddings for on-screen images, and carry these embeddings into the dialogue state when the user refers to those images (). This added visual context in the “memory” of the system improved their assistant’s ability to handle follow-up image references, demonstrating a practical, proprietary implementation of visual memory carryover.

Long Multimodal Context in Open Systems: Open research communities, facing limited context lengths of models, have explored summarization or retrieval to maintain long conversations with images. A common workaround is to summarize earlier dialogue turns (or compress the visual input into a shorter description) once the context gets too long, thus freeing space for new inputs. Another approach is to use an external retrieval augmentation: store embeddings of past images in a vector database, and when the conversation references old images, retrieve the relevant description or features and feed them back to the model. While not yet standardized, these techniques mirror those used in purely textual long conversations. In summary, academic literature provides mechanisms like memory networks and hierarchical encoders to maintain multimodal context (Visual Dialog), whereas production systems often employ heuristic strategies (e.g. caching a caption or embedding). The open research community can approximate proprietary capabilities by using these published memory modules or by orchestrating external tools (for example, using a vision API to re-supply image context when needed). The gap between open and closed systems here mainly lies in integration and scale – the fundamental ideas (storing image features or descriptions as context) are documented in research, even if fine details of GPT-4’s approach are not public.

B. Temporal Alignment

Synchronized Multi-Stream Inputs (Time-Stamping and Cross-Modal Attention): Multimodal systems that process time-based inputs (like audio and video) need to align those streams in time. A proposed solution is to time-stamp tokens or features from each modality and use cross-modal attention to align them. In theory, each token (or frame) could carry a temporal tag so that the model learns correspondences (e.g. matching spoken words with video frames that occurred at the same time). This approach has indeed been explored in research on video understanding. For instance, the MERLOT and MERLOT Reserve models (Zellers et al. 2021, 2022) ingest videos by encoding video frames, audio, and transcript text with a notion of time. These models learn to “match images to temporally corresponding words” in transcripts and audio (Rowan Zellers’ Homepage). Concretely, they use positional encodings or segment embeddings to denote time steps, and a transformer then performs cross-modal attention, effectively learning alignment between modalities over time. Such cross-modal alignment techniques are implemented in research systems for tasks like audio-visual scene understanding and video captioning. They are not just theoretical: for example, models for audio-visual speech recognition align mouth movements (video) with spoken phonemes (audio) via cross-modal attention, and often time-aligning embeddings is part of their architecture in practice.

However, in many deployed assistants (like voice-based chatbots with optional visual input), true low-level time alignment is rudimentary. These systems often handle modalities sequentially rather than processing a synchronized stream. Time-stamping every token for alignment remains mostly a research idea for specialized tasks. In current mainstream multimodal models (e.g. GPT-4’s vision or Bard with images), the image and text are not continuous streams but rather static inputs combined at one time. So, while cross-modal transformers with temporal alignment exist in literature (and a few specialized industrial systems), general user-facing AI assistants have not exposed explicit time-aligning of, say, a live video and a paragraph of text.

Incremental / Streaming Response Generation: An intriguing capability in multimodal (especially conversational) systems is to begin formulating an answer before the user has finished providing input. This is analogous to simultaneous interpretation or streaming speech recognition + response. In research, this is well studied under Simultaneous Machine Translation and Speech Translation. Techniques like the wait-$k$ policy allow a model to start generating output after reading only a few tokens of input, then alternate between reading more input and producing more output ([2005.08595] Efficient Wait-k Models for Simultaneous Machine Translation). For example, a wait-$k$ decoder might read the first $k$ words of a user’s speech transcript and then start responding, continuing to process incoming words as they arrive. This concept has been implemented in academic prototypes: researchers have shown transformer-based translation models that can operate with a fixed or learned lag, enabling real-time translation or dialogue.

In practical assistants, some degree of streaming is used in speech interfaces. Many speech-to-text systems will stream partial transcriptions to the language model so it can start computing a response. For instance, Google Assistant and Amazon Alexa have “barge-in” capabilities where they can guess the user’s intent and perhaps prepare an answer before the user stops speaking – though finalizing the response usually waits until the user finishes (to avoid errors). OpenAI’s voice-enabled ChatGPT currently does not seem to do true incremental generation; it uses a pipeline: the app records audio and detects end-of-speech, then sends the full transcription to the model, which then responds (as confirmed by documentation: the voice workflow is record -> transcribe -> LLM -> TTS (Everything you need to know about OpenAI’s Advanced Voice Mode: how it works, key features, and use cases) (Everything you need to know about OpenAI’s Advanced Voice Mode: how it works, key features, and use cases)). The observed behavior is that ChatGPT waits for you to finish speaking completely. Any impression of it formulating an answer mid-speech is likely due to fast turnaround after you stop, rather than overlapping input processing. So, in proprietary systems, streaming strategies are used mainly in limited forms (e.g. Whisper streaming transcription). The full theoretical capability (models truly listening and talking simultaneously) is not yet deployed in mainstream assistants. “Fully duplex” interaction – where the AI could listen and speak at the same time – is on the roadmap for some companies (anecdotally mentioned as “developing fully duplex models” by AI labs), but current public systems have not reached that level. In summary, simultaneous processing is implemented in research and specialized domains (real-time translation, live captioning), and is beginning to trickle into products as latency optimizations, but a conversational AI that genuinely responds on the fly (without waiting for input completion) remains mostly a proprietary frontier.

Handling Simultaneous Visual and Audio Inputs: Modern multimodal assistants like OpenAI’s ChatGPT with Advanced Voice Mode can accept both spoken input and images, but how do they handle them together? As of now, these systems process each modality with dedicated components and then combine the results at a higher level. In Advanced Voice Mode, for example, the audio is first transcribed to text (using a model like Whisper), and that text is what the language model actually sees (Advanced Voice Mode isn’t actually multimodal (no audio input)? - ChatGPT - OpenAI Developer Community). If the user also provides an image (say, by snapping a photo during the voice chat), the system will encode that image via a vision encoder (e.g. a CNN or ViT feeding into GPT-4’s multimodal interface). The LLM ultimately receives textual tokens (from the ASR) and image embeddings together in its input context. GPT-4’s vision model is designed to handle “one or more images” interleaved with text (ChatGPT can now see, hear, and speak | OpenAI), so in theory the assistant can take an image and a spoken question about it in the same query. In practice, this looks like the system doing two things in parallel: transcribing the audio into text and encoding the image into embedding vectors. Once both are ready, they are concatenated (the image is represented as special tokens or a hidden state in the prompt) and fed to the model for processing. There is typically an implicit alignment via the conversation semantics (e.g. the user’s spoken question might say “What is in this picture?” referring to the image). The assistant doesn’t truly synchronize audio waveforms with video frames in real-time – it handles the modalities as separate inputs that are combined at the prompt level.

It’s worth noting that OpenAI’s Advanced Voice Mode is not yet a fully multimodal end-to-end model; it’s a system of components. The community discovered that the GPT-4 model only gets the transcribed text and perhaps a description of audio, not raw audio (Advanced Voice Mode isn’t actually multimodal (no audio input)? - ChatGPT - OpenAI Developer Community). For example, the voice mode can’t yet detect the user’s tone or actual voice input beyond words – it treats it as text. Similarly, if an image is in play, the model gets the image content through the vision encoder. So, current systems handle simultaneous inputs by serially or concurrently pre-processing each modality and feeding the results to the LLM. There isn’t evidence that the model itself is doing dynamic temporal fusion of, say, an ongoing audio stream with a video – instead, it waits for each input to be encoded. In proprietary assistants, this is by design for reliability: voice activity detection (VAD) is used to decide when the user’s turn ends (Everything you need to know about OpenAI’s Advanced Voice Mode: how it works, key features, and use cases) (Everything you need to know about OpenAI’s Advanced Voice Mode: how it works, key features, and use cases), then the image and text are taken as a complete query.

Bottom line: Synchronizing multiple input streams with time alignment is an active research area and partially implemented in specialized systems (e.g. video models). Mainstream assistants currently treat multimodal inputs in a batched or turn-based manner, not truly streaming together. Techniques like time-stamped tokens and cross-modal attention are researched (theoretically sound and shown in papers) but not explicitly advertised in current consumer AI systems. The open research community can experiment with these by using architectures from video-language models (like MERLOT Reserve) or frameworks that support streaming (some libraries allow incremental decoding with transformers). Meanwhile, companies likely have internal prototypes for full-duplex multimodal interactions, but those remain proprietary for now.

C. Deployment Optimizations

Decoupled Vision Encoder Microservice: Due to the heavy compute demands of image processing, a common deployment optimization is to separate the vision encoder from the language model, possibly running it as a microservice. While not always called a “microservice” in literature, the pattern is well-known in engineering practice and is now appearing in documentation for open frameworks. For example, DashInfer (2024), an open-source LLM serving framework, explicitly supports a separate vision encoder that runs concurrently with the LLM (VLM Support — DashInfer 2.0.0 documentation). In their architecture for Qwen-VL (a multimodal model), the image is processed by the vision encoder in its own process, and the resulting embedding is then injected into the LLM’s token stream at the appropriate place (VLM Support — DashInfer 2.0.0 documentation) (VLM Support — DashInfer 2.0.0 documentation). This design allows the image encoder to be scaled or optimized independently and even hosted on a different machine, while the LLM focuses on text generation. The decoupled approach is indeed how many production systems are built: for instance, a cloud service might have a fleet of GPU servers handling CNN image feature extraction, and a separate fleet handling the language model inference. OpenAI hasn’t published details of GPT-4’s architecture, but speculation (and efficiency considerations) suggest that its vision component could be a separate module that the main model calls. Microsoft’s HuggingGPT system (2023) went as far as orchestrating multiple specialized models: the language model would decide which vision model to query for a given task (Microsoft Open-Sources Multimodal Chatbot Visual ChatGPT - InfoQ). That is essentially a microservices approach at the model level (albeit orchestrated via prompts). So yes, this “vision encoder as a service” pattern is documented in technical blogs and open-source projects, if not always in academic papers. It’s a practical solution embraced by both the open community and industry. The benefit is illustrated by DashInfer’s benchmarking: by running the vision encoder in parallel with text generation, they achieved nearly 2× throughput improvement (Qwen2-VL serving QPS increased by 1.9×–2.7×) compared to a serial pipeline (VLM Support — DashInfer 2.0.0 documentation). This confirms that splitting modalities into separate pipelines can yield significant performance gains – a crucial factor for real-time systems.

Confidence Measures in Modal Fusion: In a deployed multimodal system, it’s important to know how much to trust each modality’s data. Academic research on sensor fusion has long emphasized weighting modalities by confidence or uncertainty (Multimodal Dynamics: Dynamical Fusion for Trustworthy Multimodal …). For example, in a multimodal classification, one can use a gating network that dynamically weighs the contribution of each modality’s features based on their estimated reliability. Han et al. (2022) describe decision-level fusion where the system considers the uncertainty of each modality’s prediction and adjusts the fusion accordingly (Multimodal Dynamics: Dynamical Fusion for Trustworthy Multimodal …). In practice, production systems do implement confidence measures – though often with simpler heuristics. A voice assistant, for instance, will use the speech recognizer’s confidence score: if the transcription confidence is low, the system might ignore the audio input or ask the user to repeat (text modality taking precedence). In a vision+language assistant, if the vision encoder produces a very low probability for all recognized objects in an image, the system might default to a generic response rather than something specific (implicitly down-weighting the vision input). Amazon’s Alexa AI paper on Multimodal Context Carryover hinted at this: they added vision features to a dialogue state only if a visual referent was detected with sufficient confidence (). Similarly, research prototypes may train a small network to output a fusion confidence. Some approaches use an auxiliary loss to predict modality reliability (e.g. if one camera is blurry, the model learns to rely on other sensors) () (). In summary, while not often singled out as a feature in papers on GPT-4 or similar, confidence-based gating is a documented technique in multimodal research and is likely employed in proprietary systems to prevent errors. Open-source multimodal frameworks haven’t universally implemented this out-of-the-box, but it’s feasible: developers can use, say, the entropy of the vision module’s output distribution as a proxy for confidence and condition the LLM’s behavior on it. One challenge is that end-to-end LLMs (like a single big transformer that ingests image and text) don’t naturally provide per-modality confidence – this information has to come from separate models or probes. Therefore, many open implementations still treat fusion in a simplistic way (concatenating features), whereas cutting-edge deployed systems add rules or learned gates to modulate modality influence.

Hardware Acceleration Strategies: Multimodal inference at real-time speeds demands careful hardware optimization. The state-of-the-art strategy is to leverage specialized accelerators for each modality and maximize parallelism. For instance, an efficient pipeline might use GPUs (or TPUs) for both vision and language components, but load-balance them: while the GPU is busy with the transformer’s text decoding, the next image can be preprocessed on another GPU. Alternatively, edge deployments might use an ASIC or DSP for audio processing and a GPU for vision. A comprehensive survey (Li et al., 2024) on LLM inference acceleration highlights multimodality as one of the major trends driving hardware developments ([2410.04466] Large Language Model Inference Acceleration: A Comprehensive Hardware Perspective). This means new hardware and optimizations are being designed with multimodal tasks in mind – for example, unified memory to avoid copying image data between a vision processor and the CPU/GPU, or scheduling algorithms to handle asynchronous inputs. On the software side, techniques like model quantization and batching are vital. If many requests are independent, a server might batch multiple image encodings together to fully utilize GPU cores, then feed results to the language model in sequence. There are also efforts to compile multimodal models with frameworks like TensorRT or OpenVINO to optimize latency.

In production, companies like NVIDIA propose splitting the workload: one inference server for the vision backbone (possibly running a reduced precision model) and another for the language model, communicating via high-speed interconnect. This microservice approach we discussed not only aids modularity but also allows hardware-specific acceleration (e.g. run the CNN on a GPU that has CNN cores, run the LLM on a tensor-core GPU with lots of memory). Real-time processing can be further aided by streaming architectures – for example, using a streaming encoder for audio that produces partial outputs that can be fed to the text model in chunks. Such pipeline parallelism reduces idle time. Another effective strategy is to use multi-threading or multi-core CPUs for lighter tasks (like audio feature extraction or small image pre-processing) in parallel with GPU tasks.

For the open research community, replicating production-level performance means using these same tricks: quantize models (there are open tools to 4-bit or 8-bit quantize LLMs), use model parallelism if the model is too large for one GPU, and utilize libraries like DeepSpeed or vLLM for optimized inference. The hardware acceleration literature indicates that achieving 1000 tokens per second generation with low latency is extremely challenging and usually requires cluster-level solutions (Large Language Model Inference Acceleration: A Comprehensive Hardware …). Indeed, one analysis pointed out that to reach human-like real-time speeds (e.g. responding within a fraction of a second), current hardware designs often fall short unless heavily optimized or using very large throughput (batching) which isn’t applicable to single interactive sessions (Large Language Model Inference Acceleration: A Comprehensive Hardware …). As an alternative, some open projects choose to distill or fine-tune smaller multimodal models that can run on edge devices (e.g. Meta’s smaller Vision-Transformer hybrids) – sacrificing some accuracy for speed.

In summary, production systems combine architectural choices (like vision encoder microservices) with hardware optimizations (GPU parallelism, quantization, pipeline concurrency) to meet real-time requirements. This pattern is documented in industry whitepapers and some academic system descriptions, though one often has to piece it together (e.g. an Intel workshop on multimodal optimization covers how to improve throughput on CPU/GPU for such models (Optimizing Multimodal AI Inference - Intel Communities)). The most effective known strategies for open implementations are to use separate threads or devices per modality, use batching where possible, and reduce model sizes via compression. These methods are openly discussed and increasingly supported by open-source inference engines, meaning the research community can adopt many of the same optimizations seen in proprietary deployments.

D. Multimodal Output Generation

Generating Mixed-Modality Responses: Enabling AI systems to produce responses that include multiple modalities (e.g. both text and images, or spoken text with accompanying graphics) is a frontier with no single standardized approach yet. Current leading approaches tend to orchestrate multiple models or use structured output that an external program can interpret. One prominent strategy from research is to have the language model output special tokens or a markup that indicates an image should be generated at a certain point, then use an image generation model to produce the actual visual. For example, Visual ChatGPT (open-sourced by Microsoft) uses ChatGPT to decide the sequence of operations involving visual models (Microsoft Open-Sources Multimodal Chatbot Visual ChatGPT - InfoQ). If a user asks for an image, the system’s prompt manager will guide ChatGPT to output a command (in text) that triggers a diffusion model to create an image. ChatGPT might then continue with explanatory text. The final response presented to the user can thus contain an image followed by text. This kind of tool augmentation approach is available to the open community (Visual ChatGPT’s code, HuggingGPT, etc.), and it effectively coordinates distinct modalities by delegating to specialist models. Another example: in the Bing Chat integration of DALL-E 3, the system detects when the user prompt is asking for an image (e.g. “create an image of …”) and then calls the image generator, returning the image file alongside a textual description or caption (Bing Image Creator comes to Bing Chat and Microsoft Edge). The coordination logic (whether rule-based or learned) is proprietary in that case, but it is conceptually similar to the open solutions.

A more unified (but still experimental) approach is training a single model that can directly generate multiple kinds of outputs. For instance, SpeechGPT (Huang et al., 2023) attempts to give an LLM the ability to output speech waveforms in addition to text (SpeechGPT: Empowering Large Language Models with Intrinsic … - ar5iv). It does so by representing audio with discrete tokens (using a compression codec) and extending the model’s vocabulary to include those tokens. This allows the model to produce a sequence that, when decoded, becomes audible speech (so the model can “speak” with a specific voice or intonation) (SpeechGPT: Empowering Large Language Models with Intrinsic … - ar5iv). SpeechGPT is reported as “the first multimodal LLM capable of perceiving and generating multimodal content” with a focus on text and speech output (SpeechGPT) (SpeechGPT: Empowering Large Language Models with Intrinsic … - ar5iv). Likewise, one could imagine a model that outputs an image in a serialized form (like a sequence of pixels or an image token stream); however, generating high-resolution images purely via an LLM’s output tokens is not yet practical, so the two-model solution (LLM + image generator) is more common.

In proprietary systems, GPT-4 with vision doesn’t itself generate images – it only outputs textual descriptions or analysis of images. If we consider a scenario where GPT-4 could either describe something in words or produce an actual image as an answer, the decision is typically made outside the model. For example, Bing Chat explicitly requires the user to invoke image creation; GPT-4 won’t spontaneously decide to replace a textual answer with an image. This is likely a policy choice (to avoid unintended imagery) and a limitation (since GPT-4’s training was on text tokens, not image generation). In research, there are some models aiming for more integrated behavior: e.g., Unified-IO (2022) and similar frameworks treat various outputs (captions, detected objects, even images in a limited form) as different “tasks” the model can perform, chosen by a prompt. These are early steps toward a single model doing both interpretation and generation across modalities, but they are not yet at the level of generating rich mixed outputs in free-form dialogue.

Coordinating Timing of Modal Outputs (e.g. spoken explanation with visual aids): Delivering a synchronized multimodal response – like narrating while displaying an image or highlighting parts of it – is a complex capability that goes beyond generation of static outputs. There aren’t widely known public models that inherently handle this coordination. Instead, such behavior has been achieved through system design. For instance, a virtual lecturer system might use a script: first have the LLM generate the text of an explanation and identify points where a diagram would help, then generate or retrieve diagrams for those points, and finally present them in a timed slideshow with a TTS reading the script. This involves a lot of orchestrating outside the model. On the research side, there are works on text-to-video or audio-visual storytelling (e.g., generating a video with narration from a prompt), but those typically treat the problem as generating a single synchronized modality (video with audio) rather than dynamically coordinating multiple outputs in real time. An interesting research example is AV-Flow (2023), which aims to generate talking-head video and speech jointly from text (AV-Flow: Transforming Text to Audio-Visual Human-like Interactions). It uses one model to produce both the spoken words and the matching lip movements. While that is about an avatar speaking (rather than an AI assistant explaining something with a separate image), it shows that joint generation of audio and visual streams is being explored (AV-Flow: Transforming Text to Audio-Visual Human-like Interactions). Another line of work is on using tags for emotion or emphasis in TTS output – OpenAI’s voice can include expressions like laughter or a change in tone, presumably controlled by special notations or model instructions (OpenAI mentioned “emotion tags” as an upcoming concept for voice mode (Everything you need to know about OpenAI’s Advanced Voice Mode: how it works, key features, and use cases) (Everything you need to know about OpenAI’s Advanced Voice Mode: how it works, key features, and use cases)). These tags could be seen as a way to align non-verbal audio output (like laughter) with the spoken content.

For a system like an AI tutor that speaks while showing images, a practical approach today is to generate the textual explanation and the image separately and then use a simple timing mechanism (e.g., show image after X seconds or when a certain sentence is spoken). There is no end-to-end model known publicly that, for instance, generates a timed script with integrated “display image now” commands in one go. However, one could engineer this with an LLM by prompting it to produce output in a structured way. For example, prompt: “Explain this concept and include an image. First, say you will show an image, then describe the image while it’s on screen.” The LLM might output: “I’ll show you a diagram now… [image: diagram_of_concept]. As you can see in the diagram, …”. The system could parse this and know to display diagram_of_concept while reading out the rest. This kind of coordination is largely proprietary/experimental – custom systems might do it, but it’s not an off-the-shelf capability of open models. OpenAI’s own vision+voice chat doesn’t automatically narrate and point; it either describes what it sees in an image or answers verbally, but not a choreographed combination.

Deciding Modality of Response (Text vs Image) in GPT-4 with Vision: In current multimodal GPT-4, the model itself always responds in text. The decision to include an image (in systems like Bing) is actually outside the core model – it’s triggered by the user’s request or a separate logic layer. So, if the question is “How do models like GPT-4 decide when to output an image vs text?”, the answer is: GPT-4 (as available publicly) does not autonomously make that decision because it isn’t trained to generate image outputs. It is constrained to textual output. In contrast, consider an AI system that has both a language model and an image generator; a decision module or the language model’s chain-of-thought would be needed to decide, for example, “the user would benefit from a diagram, so I should provide an image.” In the open research community, we see attempts at this through planner models. HuggingGPT (Xu et al., 2023) is an example where the language model analyzes the user’s request and, if it detects something like “draw” or “show” in the prompt, it will invoke an image-generation model instead of just answering in text. This is done by prompt engineering ChatGPT to output a plan that includes calling a “DrawingTool” etc. – effectively the LLM decides modality via the plan (Microsoft Open-Sources Multimodal Chatbot Visual ChatGPT - InfoQ). Microsoft’s private Bing Chat likely uses a similar approach with hard-coded trigger phrases (as suggested by their blog: typing “create an image of X” routes to the Image Creator (Bing Image Creator comes to Bing Chat and Microsoft Edge)).

From a research perspective, one could train a model with a reward to choose the correct modality (for instance, output an image when asked for one, otherwise text). But since images aren’t part of the native output space of most LLMs, the near-term solution is an external decision policy. GPT-4 with vision in the API returns only text, so developers must interpret if that text is describing an image or if an image output is desired by context. Proprietary systems (like some multi-turn customer service bots) might have a predefined rule: “If the answer contains a location and the user’s device supports maps, show a map image instead of a long description.” Those rules are not learned by the model but are design decisions.

In the absence of a standard, the open community relies on multi-step pipelines to achieve mixed outputs. For example, one “leading approach” is:

• Step 1: LLM generates a textual outline of the answer and placeholders for images (or other media).
• Step 2: For each placeholder, call an appropriate model (image generator, database, etc.) to fill it.
• Step 3: Compose the final answer with both text and generated media.

This approach is flexible and has been demonstrated (as in Visual ChatGPT’s ability to insert generated images into the chat flow (Microsoft Open-Sources Multimodal Chatbot Visual ChatGPT - InfoQ)). It is open and accessible, but admittedly clunky compared to an ideal unified model.

Public Examples of Coordinated Modal Responses: We do have some niche examples that can be cited as public demonstrations:

• Virtual avatar products (like Synthesia or AI Studios) where an avatar speaks (TTS) while showing slides or images – here the timing is orchestrated by a script, not by an AI deciding on the fly, but it shows the concept of mixed output.
• Robotics demonstrations where an AI verbally guides someone while highlighting objects with a laser pointer or on a GUI (research from HRI – Human-Robot Interaction – sometimes does this, with the robot’s vision identifying an object and the robot saying “this one” while indicating it). Those are usually rule-based synchronizations though.
• The GALACTICA (Meta, 2022) model and others could output $\LaTeX$ formulas and diagrams in textual form, which is a limited form of multimodal output (just text that renders as math or tables). Not truly separate modalities, but a step toward varied outputs.
• For timing specifically: some multimedia summary systems take a video and generate subtitles and keyframes. If we invert that, an AI could generate a video with narration given a prompt – essentially what a video generator does (like Gen-2 by Runway, which takes text and makes a video with implicit timing). But again, those are specialized generative models, not interactive assistants coordinating on the fly.

In conclusion, multimodal output generation is in its infancy. Open research is actively trying approaches like interleaving discrete tokens for different modalities in one sequence (as SpeechGPT does for audio (SpeechGPT: Empowering Large Language Models with Intrinsic … - ar5iv)), or using one model to control others (as Visual ChatGPT and HuggingGPT do (Microsoft Open-Sources Multimodal Chatbot Visual ChatGPT - InfoQ)). Proprietary systems currently implement multimodal output in a piecemeal way – for example, ChatGPT’s voice mode outputs audio (via TTS) but that’s a fixed conversion of text to speech, not a separate content modality chosen by the model. When it comes to the model “deciding” to use an image, it’s usually because the user explicitly requested it. The lack of standardization means researchers are exploring various avenues: from pipeline orchestration (most practical) to training jointly multimodal decoders (very cutting-edge). Open-source projects that combine GPT with image generation are providing a valuable testbed for what coordinated outputs might look like. Until end-to-end multimodal generation models mature, the open community will rely on creative combinations of existing tools to emulate the rich outputs that one day these AI assistants might produce naturally.

References: The approaches and examples above draw on a mix of academic research and observations from system implementations. Key references include Visual Dialog’s memory networks (Visual Dialog), Alexa AI’s context carryover for vision (), MERLOT Reserve’s multimodal time alignment (Rowan Zellers’ Homepage), wait-$k$ simultaneous decoding ([2005.08595] Efficient Wait-k Models for Simultaneous Machine Translation), OpenAI’s voice mode design (via help documentation) (Advanced Voice Mode isn’t actually multimodal (no audio input)? - ChatGPT - OpenAI Developer Community) (Everything you need to know about OpenAI’s Advanced Voice Mode: how it works, key features, and use cases), DashInfer’s vision encoder serving design (VLM Support — DashInfer 2.0.0 documentation), multimodal fusion uncertainty in research (Multimodal Dynamics: Dynamical Fusion for Trustworthy Multimodal …), and Visual ChatGPT’s tool-integrated generation method (Microsoft Open-Sources Multimodal Chatbot Visual ChatGPT - InfoQ), among others. These illustrate how both open research and proprietary systems are tackling the challenges in multimodal AI, often converging on similar ideas but with different levels of transparency and generality. Each technique discussed has roots in published literature or documented implementations, though truly integrated solutions (especially for output generation) remain largely proprietary or experimental. By combining insights from these sources, the open community can approximate many of the capabilities, even if some polish and efficiency of commercial systems are hard to replicate exactly.

The approaches and examples above draw on a mix of academic research and observations from system implementations. Key references include Visual Dialog’s memory networks​ (OPENACCESS.THECVF.COM), Alexa AI’s context carryover for vision​ (ASSETS.AMAZON.SCIENCE), MERLOT Reserve’s multimodal time alignment​ (ROWANZELLERS.COM), wait-$k$ simultaneous decoding​ (ARXIV.ORG), OpenAI’s voice mode design (via help documentation)​ (COMMUNITY.OPENAI.COM) (CASES.MEDIA), DashInfer’s vision encoder serving design (DASHINFER.READTHEDOCS.IO), multimodal fusion uncertainty in research​, (OPENACCESS.THECVF.COM), and Visual ChatGPT’s tool-integrated generation method (INFOQ.COM), among others. These illustrate how both open research and proprietary systems are tackling the challenges in multimodal AI, often converging on similar ideas but with different levels of transparency and generality. Each technique discussed has roots in published literature or documented implementations, though truly integrated solutions (especially for output generation) remain largely proprietary or experimental. By combining insights from these sources, the open community can approximate many of the capabilities, even if some polish and efficiency of commercial systems are hard to replicate exactly.