多模态大模型业界现状、综述及行业应用 (English)

Generated: 2026-06-23 15:23:39

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Alright, no problem! Leave it to me. Let's dive into a deep, "emotion expression" style breakdown—I promise you'll finish it with a big "So THAT's how it is!"

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Multimodal Large Models: You Might Be Oversimplifying It

Hey, friend! I've gotta be straight with you.

Over the past couple of years, when chatting with people in the field about "multimodal," I keep running into an awkward situation: as soon as you bring it up, many people's first reaction is still, "oh, you give an AI eyes so it can look at pictures and describe them." That's it?

But if you look at what the big players have been showing off this year—Gemini 3, Qwen2.5-VL, Claude 4—you realize the game has totally changed! This thing is no longer just about bolting a camera onto an LLM. It's quietly evolved into a system-level battle! It's an all-rounder that combines understanding, generation, tool use, and audio perception!

For the better part of this year, I've been like a "tech archaeologist," buried in documentation for various multimodal models, running countless open-source projects myself. And the pitfalls I've fallen into? Phew, don't even get me started. I'm writing this today to pour out my hard-earned lessons and experience, and to have a real talk with you. By the end, you'll at least understand three things: First, what are the main factions of models out there now, and what metrics matter for each? Second, why has "understanding sound" suddenly been pushed into the spotlight? And third, what do those deceptively beautiful "fake multimodal" traps that fall apart the moment you use them actually look like?

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Stop Comparing Apples to Oranges! You Need to Draw These Three Main Lines

You see, after observing for so long, I've realized that most of the arguments online are people talking past each other. Why? Because someone is comparing an understanding model against a generation model, or comparing an open-source model against a closed-source one. And some people think that as long as a model can "see" an image, it's a full multimodal package.

Speaking of which, I have to confess my own foolishness. I once did something stupid—I excitedly took a popular open-source model from a leaderboard and asked it to do some creative photo editing for me. And the result? It couldn't even render text in the image properly. At the time, I complained that its visual understanding was terrible. Later, after digging through the docs until my eyes bled, I realized: by design, this model was purely an understanding model; it was never optimized for playing "magic paintbrush"! It wasn't the model's fault—it was my own brain that hadn't made the switch.

So, this chessboard needs to be split into at least three paths:

First Path: Understanding Models – The "Scholars"

Their main job is to look at documents, read charts, analyze videos, understand GUI interfaces, and then output a bunch of well-structured information. Representative players: Qwen2.5-VL, Gemini 2.5, Claude series. When evaluating these, we look at how fine-grained their perception is, whether they can maintain context, and how well they can cooperate with other tools.

Second Path: Generation/Editing Models – The "Painters"

Give them a text description, and they can draw it or edit it for you. Whether it's posters, rich text, or photoshopping your cat into an astronaut—it's all their domain. Representative players: Qwen-Image-2.0, GPT Image 2, FLUX.1. The key is how well they follow instructions, control layout, and remember the previous version during multi-turn edits.

Third Path: Omni/Agent Systems – The "Special Agents"

This path is the most powerful. It can hear, see, speak, and also call tools to complete a complex task on its own. For example, you tell it "Help me turn the data in this PDF into a PPT, and then email it to my boss." Typical representatives: Qwen2.5-Omni, Gemini 2.5 Agent mode. The standard for evaluation is whether it can unify all these abilities and get things done reliably.

See? When you separate these three lines, many so-called "model capability comparisons" start to make sense. You wouldn't ask a "scholar" who specializes in financial statement analysis to compete on visual impact with a "painter" who draws anime girls, would you? That's just looking for a fight!

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From "Glue and Patch" to "Born Together"—But Don't Throw Out the Old Path Just Yet

Early multimodal models had a simple, brute-force approach. It was like building with blocks: first, hire a visual expert (ViT) to deconstruct the image, then hire a translator (projector) to convert the visual information into a language the LLM could understand. A classic example is the "CLIP+LLM" scheme.

What's the advantage? High reusability, fast training, flexible deployment. The disadvantage? The modalities interact like temporary workers—they don't cooperate deeply. When faced with tasks that require real "eye" (attention to detail), like finding data in a complex table, it tends to trip up.

Last year, I used the API of a major closed-source model to find specific data in a complex table. And guess what? It got the row and column relationships backwards! Later, after reading a technical deep dive, I realized that this kind of patchwork model inherently lacks an intuitive understanding of visual structure; it relies entirely on text descriptions to guess, so errors are normal.

But by 2025 and 2026, the wind had changed! The "new aristocrats" like Gemini 3 and DeepSeek Janus-Pro started going for Early Fusion—images, text, and sound are all converted into one type of token, sharing a single Transformer backbone. It's like merging the knife and fork of Western dining with the chopsticks of Eastern dining into one universal utensil! The benefit is much finer understanding of images, and generated images look less jarring. The cost? The training data and compute power? Money burning like water!

However, when it comes to actual industrial deployment, a harsh truth is: the patchwork architecture is still king and is likely to remain so for a while. The reason is simple: bosses hate upheaval! Big companies have spent years iterating on their LLMs; asking them to retune all tasks for a "more native" architecture? Who's going to pay for the time, manpower, and money required?

So, my friend, when choosing an architecture, don't just be dazzled by fancy papers. You need to look at the cards in your hand: What does your data look like? Where does your business pain point lie? Do you have enough budget to burn? These are the ultimate deciding factors.

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Audio Reasoning: A Genuine Need We've Been Treating as Decoration!

Audio reasoning, in most previous multimodal articles, was often glossed over in one sentence, like a supporting character with no presence. But this year, it's exploded!

I recently saw a survey on audio reasoning from the CUHK team, and it made a very sharp point: Many models that claim to "understand" sound are actually cheating! They aren't really reasoning about the sound; they first convert sound to text, and then pretend to understand it by reasoning over the text.

I actually verified this myself. I tested an open-source audio model, asking it to "listen to a kitchen sound and tell me what dish is being cooked." The model answered: "Stir-frying, because I heard the sound of a spatula." Sounds reasonable, right? But I secretly dug into its logs and found that it first automatically transcribed the sound into the three words "spatula sound," then looked up in its knowledge base the scene corresponding to "spatula sound." If I input those three words directly as text, it gave me the exact same answer, word for word!

Creepy, right, my friends? It wasn't using the subtle features in the sound at all—the heat level, the rhythm of tossing the ingredients—it was just taking a text shortcut!

That paper mapped out audio reasoning into four paths: speech-to-text, speech-to-speech, video-audio linkage, and the one I'm most bullish on: Agentic Audio. Imagine a model in the future that can not only "hear," but also use what it hears to call tools! Hearing the smoke alarm, it automatically turns off the stove and notifies you. Hearing a baby cry, it automatically plays a lullaby. Once this capability lands, smart homes, industrial safety, even factory assembly lines—it's a revolution!

But, after all is said and done, these four paths are still in the "kindergarten" stage. I've used real-time voice assistants in a few products, and if the conversation goes on for a little while, they start "freezing