UnifiedMLLM: Unifying Multimodal Tasks

• UnifiedMLLM is a unified multimodal large language model framework that represents heterogeneous tasks using structured task and grounding tokens.
• It employs a task router to dynamically dispatch requests to specialized expert models for operations like segmentation, editing, and media synthesis.
• The framework is trained via a three-stage strategy—modality pretraining, task adaptation, and LoRA MoE tuning—to enhance multi-task performance and generalization.

Searching arXiv for the specified paper and closely related work on UnifiedMLLM and unified multimodal MLLM frameworks. UnifiedMLLM is a multimodal LLM framework introduced to enable a unified representation for multi-modal multi-task processing with a LLM. It is designed to address the limitation that many MLLMs are trained for specific tasks and rely on task-specific input-output formats, which constrains broader applicability. The framework represents heterogeneous tasks through a consistent protocol based on task tokens and grounding tokens, uses a task router to dispatch requests to expert models, and is trained with a three-stage strategy intended to preserve generalization capacity and the model’s knowledge reservoir while strengthening reasoning and task processing capabilities (Li et al., 2024). Within the broader literature, UnifiedMLLM belongs to a line of work pursuing unified or omni-modal MLLMs, in which multiple modalities and tasks are mapped into a common LLM-centric representation and coordinated through shared reasoning machinery (Jiang et al., 2024).

1. Definition and conceptual scope

UnifiedMLLM is presented as “a comprehensive model designed to represent various tasks using a unified representation” (Li et al., 2024). Its central claim is that multimodal multi-task systems can be made more general by replacing task-specific interfaces with a single representational scheme. In this formulation, the model is expected to comprehend implicit user intent, perform reasoning, generate textual responses, and emit structured control tokens that specify both the intended task and the relevant spatial granularity (Li et al., 2024).

The unification mechanism is explicit. Instead of assigning each downstream capability its own prompt format, decoder head, or task-specific schema, UnifiedMLLM augments the LLM output with two types of special symbols: task tokens and grounding tokens. Task tokens indicate the type of downstream task to perform, while grounding tokens identify the relevant region or spatial target. These outputs are then interpreted by a task router, which forwards the request to a designated expert model for execution (Li et al., 2024).

This places UnifiedMLLM at the intersection of two research trajectories. One trajectory seeks unified modeling spaces for multiple modalities, as surveyed under the banner of Omni-MLLMs, where arbitrary non-linguistic modalities are aligned to an LLM embedding space through modality encoders, connectors, an interactive backbone, and generators (Jiang et al., 2024). The other trajectory studies unified handling of diverse domains or tasks inside a single multimodal model, often with parameter-efficient specialization mechanisms such as LoRA experts (Chen et al., 2024). UnifiedMLLM combines these themes through a modular architecture that separates global reasoning from specialized task execution (Li et al., 2024).

2. Unified representation: task tokens, grounding tokens, and routing

The defining representational device in UnifiedMLLM is the joint use of task tokens and grounding tokens. Task tokens are described as indicators of task types, while grounding tokens are indicators of task granularity (Li et al., 2024). The paper gives the example of task markup such as <Edit> ... </Edit> for image editing and grounding markup such as <Region> {x,y,w,h} </Region> for spatial specification (Li et al., 2024). These token pairs are embedded in the LLM’s output so that subsequent components can parse both what operation is intended and where it should be applied.

This design removes the need for ad hoc task formats. The same decoder can produce natural language alongside machine-interpretable control structure. The significance of this scheme lies in its attempt to make task recognition an internal generative act rather than an externally pre-specified interface requirement. The model is therefore not restricted to explicit, rigid prompt templates alone; it is trained to infer task type and grounding from open instructions and dialogue context (Li et al., 2024).

The task router is the execution mediator. After the LLM generates task and grounding tokens, the router parses them and dynamically invokes the corresponding expert model (Li et al., 2024). The architecture therefore decouples semantic interpretation from low-level execution. The LLM handles reasoning and intent understanding, while expert systems handle fine-grained downstream operations such as segmentation, editing, or media synthesis.

A plausible implication is that UnifiedMLLM treats “task selection” as part of sequence generation rather than as a separate classification stage. This contrasts with many multimodal pipelines that hard-code the task family before inference. In the broader literature, related efforts toward unification also emphasize shared representational spaces and single-model handling of multiple modalities, but UnifiedMLLM’s specific emphasis is on structured output tokens that explicitly support task routing and grounding (Jiang et al., 2024).

3. Architecture and component design

UnifiedMLLM is built atop Vicuna-v1.5 as its LLM backbone, with dedicated modality encoders and lightweight adapters for image, video, and audio inputs (Li et al., 2024). Images are processed with CLIP ViT-L/14. Videos are represented by uniformly sampled frames, encoded via Q-Former with temporal position encoding. Audio is encoded with ImageBind’s audio encoder over sampled segments and aggregated with Q-Former (Li et al., 2024). Encoded features from each modality are mapped to the LLM embedding space using two-layer MLP adapters.

The architecture introduces special tokens for tasks and grounding directly into the LLM vocabulary (Li et al., 2024). During decoding, the LLM produces both natural language and structured control tokens. The task router then parses the structured portion and dispatches the request to expert models. This architecture separates the reasoning substrate from operational backends.

The expert layer is explicitly heterogeneous. The paper lists the following integrations (Li et al., 2024):

Function	Expert models
Image/Video Segmentation	SEEM
Image Editing	InstructPix2Pix, GLIGEN
Text-to-Image	Stable Diffusion, GLIGEN
Layout Generation	GLIGEN, LayoutGPT strategies
Video Editing	FRESCO
Text-to-Video/Image-to-Video	ModelScopeT2V, I2vgen-xl
Text-to-Audio	Auffusion

This modularity is a core architectural property. UnifiedMLLM does not attempt to internalize all generative or analytic capabilities inside a monolithic decoder. Instead, it uses the LLM as a global planner and interface interpreter, while external or attached experts provide specialized task competence (Li et al., 2024). This resembles other unified frameworks in which a common backbone coordinates diverse modalities and tasks, but UnifiedMLLM makes the separation between reasoning and execution especially explicit.

The paper also introduces LoRA mixture-of-experts modules within the LLM to avoid erosion of core language reasoning knowledge during downstream adaptation (Li et al., 2024). The model keeps the LLM and encoders frozen for stability and knowledge retention, while lightweight trainable adapters provide task-specific updates.

The formulation is given through the transformer FFN update:

$f(x) = x + \textrm{FNN}(x)$

with a linear layer decomposed as

$o = W x = W_0 x + \Delta W x$

where $W_0$ is the frozen backbone weight and $\Delta W$ is the trainable task-specific update (Li et al., 2024). In the MoE replacement,

$o = W_0 x + \sum_{i=1}^{N} G(x)_i E_i(x)$

with routing

$G(x) = \operatorname{Softmax}(x \cdot W_g)$

where $E_i(\cdot)$ is the $i$-th expert MLP and $W_g$ are the router weights (Li et al., 2024). The low-rank expert parameterization is

$\Delta W_E = B A$

with $o = W x = W_0 x + \Delta W x$0 and $o = W x = W_0 x + \Delta W x$1, and forward computation becomes

$o = W x = W_0 x + \Delta W x$2

(Li et al., 2024).

In comparative context, LLaVA-MoLE also extends LoRA with a sparse Mixture of LoRA Experts to mitigate data conflicts when mixing distinct domains in instruction finetuning MLLMs (Chen et al., 2024). That work routes each token to the top-1 expert inside transformer MLP layers and reports that sparse expert specialization can reduce interference between domains while keeping cost roughly constant relative to standard LoRA (Chen et al., 2024). This provides a relevant technical backdrop for UnifiedMLLM’s use of LoRA-MoE as a knowledge-preserving specialization strategy.

4. Training strategy and dataset construction

UnifiedMLLM is trained in three stages, each associated with a specific objective and parameter-update regime (Li et al., 2024).

Stage 1: Modality-perception Pretraining. The goal is to develop base cross-modal perception. The model is trained on large-scale, open-source multimodal data, while freezing the LLM and encoders and training only the adapters (Li et al., 2024). This stage is intended to establish generic multimodal input understanding.

Stage 2: Task Adaptation Tuning. The goal is to teach the model to understand diverse tasks, grounding, and intent. Training uses task-specific datasets formatted with the unified representation as well as general instruction datasets (Li et al., 2024). The summary mentions reasoning datasets such as LISA, SmartEdit, and LayoutGPT. In this stage, the LLM and adapters are jointly trained while modality encoders remain frozen (Li et al., 2024).

Stage 3: Multi-Task LoRA MoE Tuning. The goal is to cement reasoning, intent understanding, and multi-task ability while preventing catastrophic forgetting. This stage uses a 100k instance multi-turn, multi-task instruction dataset created via GPT-3.5 and GroundingGPT. Only the LoRA-MoE parameters are updated; all else is frozen (Li et al., 2024).

The paper also describes a task-specific dataset and an 100k multi-task dataset encompassing complex scenarios (Li et al., 2024). For region-based tasks, GroundingGPT is used to generate captions with bounding boxes, while GPT-3.5 produces multi-turn dialogs that are filtered for correct adherence to the unified representation (Li et al., 2024). This suggests that a substantial part of UnifiedMLLM’s training recipe is the construction of synthetic or programmatically structured instruction data aligned to its output protocol.

The three-stage strategy has close analogues in the unified MLLM literature. InfMLLM, for example, also uses a three-stage training scheme consisting of alignment pretraining, multitask hybrid training, and instruction fine-tuning, progressively increasing GPU memory requirements and broadening task coverage (Zhou et al., 2023). The Omni-MLLM survey similarly identifies two-stage alignment pre-training and instruction fine-tuning as a common pattern for unified multimodal models (Jiang et al., 2024). UnifiedMLLM extends this logic by adding a final LoRA-MoE phase dedicated to multi-task consolidation and knowledge preservation (Li et al., 2024).

5. Empirical performance and evaluated capabilities

UnifiedMLLM is reported to achieve state-of-the-art or highly competitive results across a diverse set of tasks (Li et al., 2024). The paper organizes evaluation across segmentation, reasoning-based editing, layout-guided generation, and multimodal generation tasks.

For referring image segmentation on RefCOCO, RefCOCO+, and RefCOCOg, the metric reported is cIoU. UnifiedMLLM records 76.3 on RefCOCO Val, 66.4 on RefCOCO+ Val, and 68.0 on RefCOCOg Val, compared with 74.9, 65.1, and 67.9 for LISA, and 74.7, 65.1, and 67.0 for NExT-Chat (Li et al., 2024).

For reasoning editing on the Reason-Edit dataset, the paper reports PSNR, SSIM, and CLIP Score. UnifiedMLLM obtains 26.67 PSNR, 0.81 SSIM, and 21.10 CLIP, compared with SmartEdit at 25.26, 0.74, and 20.95 (Li et al., 2024).

For layout-guided generation and reasoning on NSR-1K, the summary states that UnifiedMLLM approaches or exceeds LayoutGPT (GPT-4) in both layout accuracy and image correctness (Li et al., 2024).

For multimodal generation, the summary reports the following best results (Li et al., 2024):

Task	Benchmark	Reported result
Text-to-Image	COCO-Captions	FID 10.84
Text-to-Video	MSR-VTT	FID 11.15, CLIPSIM 0.3120
Text-to-Audio	AudioCaps	FD 22.42, IS 9.95

These evaluations support the paper’s claim that a single representational interface can support a wide range of downstream capabilities when combined with a routing-and-expert execution design (Li et al., 2024).

The paper also emphasizes scalability and generalization. It states that new tasks can be added by defining new task and grounding tokens and using the router, without retraining the backbone (Li et al., 2024). It further reports robust generalization to unseen instructions, attributing this to training on diverse and synthetic multi-turn, multi-task dialog data (Li et al., 2024). Qualitative analysis is said to show successful execution of complex tasks, including generating videos from images plus audio, unseen during training (Li et al., 2024). Since the description characterizes these as qualitative findings, a cautious reading is that the framework exhibits compositional behavior beyond narrowly supervised settings.

6. Relation to adjacent unified MLLM paradigms

UnifiedMLLM should be situated among several neighboring efforts that pursue “unification” in different senses.

First, the Omni-MLLM literature studies unification across modalities. The survey “From Specific-MLLMs to Omni-MLLMs” describes a general architecture with four core components: multi-modalities encoding, connector, interactive backbone, and generator. Omni-MLLMs map multiple non-linguistic modalities into the embedding space of LLMs and support interaction and understanding of arbitrary combinations of modalities within a single model (Jiang et al., 2024). UnifiedMLLM shares this LLM-centric alignment principle, but its distinctive contribution is to unify not only modalities but also tasks through explicit control tokens and expert routing (Li et al., 2024).

Second, some frameworks unify tasks within a single visual-LLM through shared prompting and staged training, but without explicit expert routing. InfMLLM is a unified framework for image captioning, visual question answering, and visual grounding. It uses a pool-adapter to preserve positional information in visual embeddings and applies a three-stage training scheme (Zhou et al., 2023). Compared with InfMLLM, UnifiedMLLM places more emphasis on downstream expert invocation, explicit grounding tokens, and multimodal generation beyond core vision-language understanding (Li et al., 2024, Zhou et al., 2023).

Third, domain unification and conflict mitigation form another adjacent line of work. LLaVA-MoLE argues that data conflicts are inevitable when instruction finetuning MLLMs on mixed datasets from distinct domains, and proposes sparse Mixture of LoRA Experts with top-1 routing to reduce negative interference (Chen et al., 2024). UnifiedMLLM’s LoRA-MoE mechanism addresses a related concern—preserving reasoning knowledge while extending task breadth—though its primary motivation is modular multi-task specialization rather than explicit mixed-domain conflict mitigation (Li et al., 2024).

Fourth, unified multimodal generation appears in specialized application areas. CAD-MLLM aims to generate parametric CAD models from textual descriptions, images, point clouds, or combinations thereof through a unified multimodal LLM pipeline (Xu et al., 2024). UML code generation from diagram images has likewise been framed as multimodal translation using CLIP-based visual encoding, Vicuna-based code generation, and LoRA or full fine-tuning (Bates et al., 15 Mar 2025). These systems are application-specific rather than general-purpose task routers, but they illustrate how unified multimodal representations can be coupled to domain-specific output languages or expert modules.

Finally, unification can also be interpreted at the software-architectural level. LLM-Agent-UMF proposes a unified modeling framework that separates LLMs, tools, and a “core-agent” containing planning, memory, profile, action, and security modules (Hassouna et al., 2024). Although this is an agent architecture rather than an MLLM training framework, it offers a useful conceptual analogy: both UnifiedMLLM and LLM-Agent-UMF separate high-level reasoning from specialized action backends, and both stress modularity as a means of extensibility (Li et al., 2024, Hassouna et al., 2024).

7. Significance, limitations, and open questions

The significance of UnifiedMLLM lies in its attempt to solve three problems simultaneously: heterogeneous task interfaces, multimodal reasoning, and extensible execution (Li et al., 2024). By allowing the LLM to output task tokens and grounding tokens, the framework makes task semantics part of the generative sequence. By using a task router and expert models, it avoids forcing a single backbone to internalize all low-level competencies. By employing three-stage training with a final LoRA-MoE phase, it aims to preserve generalization capacity and the knowledge reservoir while scaling to many tasks (Li et al., 2024).

The framework’s modularity is repeatedly emphasized. The paper states that UnifiedMLLM can seamlessly add new tasks by defining new task and grounding tokens and using the router, without retraining the backbone (Li et al., 2024). This suggests an extensible interface layer in which the LLM serves as a stable semantic coordinator and experts can evolve independently. In the broader Omni-MLLM literature, such extensibility is frequently described as a central aspiration of unified multimodal systems (Jiang et al., 2024).

At the same time, several limitations or unresolved questions are implicit in the surrounding literature. One recurrent issue is catastrophic forgetting and interference when new domains or tasks are added. UnifiedMLLM addresses this through frozen backbones and LoRA-MoE tuning (Li et al., 2024), while LLaVA-MoLE addresses related interference through sparse token-level expert routing (Chen et al., 2024). This suggests that scaling unified systems may depend as much on specialization mechanisms as on unified representation alone.

Another challenge concerns the dependence on synthetic or automatically constructed instruction data. UnifiedMLLM’s 100k multi-turn, multi-task dataset is created via GPT-3.5 and GroundingGPT, and filtered for unified representation adherence (Li et al., 2024). This suggests that data engineering is integral to the framework. The Omni-MLLM survey similarly notes that cross-modal instruction data remain limited in scale, diversity, and realism, and that synthetic paired data are often used for low-resource modalities (Jiang et al., 2024).

A further consideration is the boundary between unification and decomposition. UnifiedMLLM is “unified” at the interface and reasoning levels, but “modular” at the execution level, because many tasks are ultimately completed by separate expert systems (Li et al., 2024). This is not a contradiction; rather, it reflects a particular interpretation of unification in which a single semantic control plane coordinates multiple specialized engines. A plausible implication is that future progress may depend less on eliminating modules and more on improving how unified representations communicate with modular experts.

In sum, UnifiedMLLM represents a specific and influential formulation of unified multimodal intelligence: one backbone for intent understanding and reasoning, one representational protocol for diverse tasks, and one routing mechanism for scalable expert invocation (Li et al., 2024). Its place in the literature is best understood not as an isolated architecture, but as part of a broader movement toward LLM-centric systems that align multiple modalities, multiple tasks, and multiple specialized capabilities within a coherent computational interface (Jiang et al., 2024, Chen et al., 2024, Zhou et al., 2023).