Idea for Expanding Deepseek

Hmm. For now, there are a few technical approaches that are close to what you’re describing:

I think the interesting part of your idea is that there are actually several different ways to “marry” an existing language model to new senses, and they preserve different things.

Very roughly:

1. Keep DeepSeek as the central language model,
   and let it consult separate vision/audio specialists.

2. Keep the language model frozen,
   but train a bridge that feeds visual/audio representations into it.

3. Retrain the language model as part of a new multimodal model.

Your wording — especially “let you learn to use and consult each other” — sounds closest to #1.

And if the main concern is preserving the existing DeepSeek rather than replacing it with a newly trained multimodal successor, I would probably start there. It is also the cheapest version of the idea to test: you do not actually need to train a new vision-language model at all to find out whether the basic idea is useful.

Something as simple as this is already a legitimate architecture:

                         ┌─ vision specialist
                         ├─ OCR/document specialist
User ──► DeepSeek ───────┼─ audio specialist
             ▲           └─ other specialists
             │
             └──── structured observations/results

DeepSeek remains the main reasoner/conversational model. When it needs information that it cannot directly perceive, it calls the appropriate specialist, gets the result back, and continues reasoning.

There is quite a bit of research precedent for exactly this general idea. Socratic Models, for example, explored composing independently pretrained vision, language, and audio models through language without requiring joint fine-tuning. MM-ReAct put ChatGPT at the center of a pool of visual experts. LENS similarly lets an ordinary off-the-shelf LLM reason over information supplied by independent vision modules, without multimodal training.

So the basic idea is not at all outlandish. There is a fairly well-developed family of architectures behind it.

The main distinction I would make is this:

Having another AI describe what it sees to DeepSeek is not quite the same thing as making DeepSeek itself visually multimodal.

Both are useful, but they solve slightly different problems.

A useful way to choose between them is to ask what exactly you want to preserve:

If the priority is...

Keep the central DeepSeek model itself unchanged
    → external vision/audio specialists

Keep the same language-model weights,
but give it more direct access to sensory representations
    → frozen-LLM + modality adapters/bridges

Allow DeepSeek itself to change during training,
while trying to preserve its language abilities
    → integrated VLM / multimodal training

Preserve the same "mental set" or perspective
    → that is a separate behavioral question;
      none of the architectures above guarantees it by itself

1. The version closest to your 'consult each other' ideaSo, if I translate your proposal into the terminology used in the literature, I would not reduce it simply to “make DeepSeek multimodal.”

I would describe the interesting choice more like this:

Option A — sensory colleagues
DeepSeek consults separate visual/audio specialists.
Lowest coupling; easiest way to preserve the original model.

Option B — sensory pathways
Keep the language model frozen and train modality bridges.
Deeper integration, while still preserving the LLM weights.

Option C — multimodal successor
Train the language model itself together with the new modalities.
Potentially the deepest integration, but now preserving the old
language behavior becomes an explicit training/evaluation problem.

And there are real examples of all three families.

Given the concern you started with — not wanting a future multimodal system simply to replace the DeepSeek you already value with something different — A seems like the cleanest first experiment, with B as the particularly interesting middle ground.

It lets you test the central idea first:

Can the existing language model become substantially more capable by learning when and how to consult sensory specialists, without having to rewrite the language model itself?

If that works well, deeper integration is still available later. But you would learn quite a lot before crossing that boundary.