The Evolution of Multimodal Large Models: From Conversational Generation to Unified Creation

1. Background: Shifting from “Chat” to “Delivery”

Over the past two years, the competitive focus of large language models (LLMs) has shifted significantly. Early competition centered on language understanding and generation quality, then moved to multimodal perception—the ability to process images and audio. Today, the standard for evaluating a model’s practical utility is whether it can accept materials, invoke tools, drive workflows, and ultimately deliver a final product.

This transition is driven by two main factors:

• Interaction: Users are no longer satisfied with one-off Q&A. They expect to iteratively refine outputs, much like collaborating with a human partner.
• Tasks: Real-world enterprise requirements are inherently multi-step—retrieving data, processing materials, invoking APIs, and generating deliverables. No single-round generation can cover this entire scope.

Consequently, mainstream multimodal model systems typically diverge into two capability tracks: one for content creation and another for task execution. The former handles the fusion and regeneration of heterogeneous materials like text, images, and videos. The latter focuses on low latency, multi-step reasoning, tool invocation, and long-context management. This article breaks down the technical logic and engineering practices behind these two tracks.

2. Unified Multimodal Architecture: Turning Video Generation into “Conversational Editing”

2.1 Limitations of Traditional Text-to-Video

The typical workflow of early video generation models was strictly linear:

Write prompt -> Wait for generation -> Unsatisfied -> Rewrite prompt -> Resample

This approach presents three structural issues:

1. Uncontrollable: The generation is finalized in a single pass. Local modifications require resampling, leading to high randomness.
2. Non-continuable: There is no state preservation across multiple rounds. The model cannot remember which elements from the previous version should be retained.
3. Non-combinable: Text, image, and video materials operate in silos, making it difficult to mix and drive a single generation process cohesively.

For creative scenarios requiring precise control over visual elements, the rework cost of this workflow is exceptionally high.

2.2 Multimodal Input and Conversational Editing

The core concept of the new unified multimodal architecture is to refactor video generation from a “single prompt” into “multimodal input + conversational editing.” Its capability features can be summarized as follows:

• Fusion Input: Text descriptions, reference images, and reference video clips can be provided as conditions simultaneously.
• State Preservation: Maintains memory of “must-retain elements” across multi-turn interactions, such as character identity, composition, and stylistic tone.
• Targeted Modification: Users issue incremental commands via natural language. The model only alters the specified dimensions, keeping the rest consistent.

This interaction loop can be described using the following pseudocode:

# Pseudocode: Interaction loop for conversational video editing
session = create_session()
# Round 1: Multimodal material injection
session.add_condition(text="A person standing on a street corner")
session.add_condition(image=reference_portrait)
session.add_condition(video=style_reference_clip)
draft = session.generate()
# Subsequent rounds: Incremental commands, maintaining existing constraints
session.instruct("Keep the character's appearance unchanged")
session.instruct("Slowly push the camera forward")
session.instruct("Replace the background with a rainy night street")
session.instruct("Reduce the range of motion")
session.instruct("Add ambient sound")
final = session.refine()

The real technical challenge here lies not in the image quality itself, but in two specific areas:

• Constraint Memory: The model must distinguish between “locked” items and “editable” items.
• Change Localization: The model needs to understand which region of the frame, which time segment, or which attribute dimension the instruction targets.

This effectively transforms the video generation problem into a controllable editing problem, aligning with the research trajectory in the image editing domain.

2.3 Templating and Material Remixing

Beyond creating from scratch, unified multimodal architectures typically support two cost-reduction pathways:

• Templated Generation: Preset structured templates allow users to quickly produce outputs by filling in variables, making it ideal for batch content production.
• Material Remixing: Reorganizing and applying style transfer to existing content in albums or asset libraries, suitable for secondary creation.

The value of these pathways lies in lowering the barrier to entry—not every user possesses the skill to write high-quality prompts. Templates and material reuse provide a more stable baseline of quality.

3. Task-Oriented Models: Engineering Support for Agents and Coding Scenarios

While multimodal creation models solve the problem of “producing content,” task-oriented models solve the problem of “driving workflows.” The capability profile of these models generally includes four dimensions:

Capability	Description	Typical Scenario
Low Latency	Controllable time-to-first-token and end-to-end latency	Real-time interaction, online assistants
Multi-step Reasoning	Decomposing complex tasks and executing them step-by-step	Workflow orchestration, issue triage
Tool Invocation	Calling external APIs on-demand to retrieve information or execute actions	Data queries, system operations
Long Context	Accommodating large-scale codebases or documents	Code comprehension, document analysis

3.1 Why Low Latency is Critical

In agentic scenarios, a single user request might trigger multiple rounds of model and tool calls. If the latency of a single call is too high, delays accumulate across the chain, ultimately resulting in a noticeable wait time for the user. Therefore, low latency is not just a user experience issue; it directly determines the feasibility of multi-step workflows.

Common engineering optimization directions include:

• Computation graph optimization and batch scheduling on the inference side;
• Cache reuse to avoid redundant calculations of identical prefixes;
• Streaming output to allow users to see progress during the generation process.

3.2 The Synergy of Tool Invocation and Long Context

Tool invocation allows models to bypass the boundaries of parametric knowledge, enabling them to fetch real-time data and perform deterministic calculations. However, tool calls themselves introduce new context—structured data returned by APIs, error messages, and intermediate states.

The role of long-context capabilities here is to:

• Maintain the complete conversation history and tool invocation trajectory, preventing information loss;
• Accommodate large code repositories or lengthy documents, supporting cross-file and cross-chapter correlation analysis;
• Retain intermediate conclusions during multi-step reasoning, reducing redundant derivations.

A typical agentic invocation flow can be abstracted as follows:

# Pseudocode: Multi-step task execution with tool invocation
context = build_context(user_request, history)
while not task_completed:
    plan = model.reason(context)
    if plan.requires_tool:
        result = invoke_tool(plan.tool_name, plan.arguments)
        context = append(context, result)
    else:
        output = model.generate(plan)
        task_completed = True

4. Key Considerations for Developer Integration

For teams preparing to integrate multimodal and task-oriented capabilities into their business workflows, the following points require early evaluation:

First, defining capability boundaries. Creative models and task-oriented models have different optimization goals. Do not expect a single model to be optimal in all scenarios. A sound approach is routing by scenario: content production goes through the multimodal creation pipeline, while workflow automation goes through the task-oriented pipeline.

Second, state management design. Conversational editing relies on the correct maintenance of session states. Developers must clearly define which constraints are globally locked and which are valid only for a single turn, while ensuring proper state persistence and recovery on the server side.

Third, balancing cost and latency. Multimodal inputs, long contexts, and multi-round tool calls significantly increase computational overhead. It is recommended to introduce a tiering strategy during the architecture design phase: simple requests take a lightweight path, while complex requests are escalated to the full pipeline.

Fourth, establishing an evaluation system. Generative tasks lack a single correct answer. It is necessary to build a business-oriented evaluation set covering metrics like visual consistency, instruction adherence, and task completion rate, rather than relying solely on generic benchmark scores.

Fifth, data security and compliance. Scenarios involving user materials, enterprise code, and internal documents require clear definitions of data processing boundaries and access control strategies prior to integration.

5. Conclusion

The evolutionary direction of multimodal large models is becoming clear: moving from isolated generation capabilities to a comprehensive system of unified material input, conversational editing, and multi-step task execution. Video generation is beginning to acquire the interactive feel of “editing,” while task-oriented models are taking on the workflow responsibilities of an “intelligent employee.”

For developers, the real focus should not be on the parameter scale of a specific model version, but on whether this capability system can seamlessly integrate with existing engineering architectures—how state is managed, how tools are invoked, how costs are controlled, and how performance is evaluated. Clarifying these questions ensures that the benefits of model iterations translate into tangible business value.

Ultimately, the value of technology is realized through delivery. Systems that can accept materials, invoke tools, drive workflows, and deliver results are the true destination of this evolutionary wave.