Can Coordinated LLMs Become a Form of Superintelligence?

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Hello seniors and members of the community,

I’m a university student from South Korea, currently studying in a general software engineering department. While I don’t yet have a deep technical understanding of large language models (LLMs), I’ve recently been inspired by an idea I’d love to share with you all.

The idea is this: what if we could coordinate existing LLMs—models already brilliantly developed by pioneers like yourselves—into a kind of AI company where each LLM plays the role of a team member or even an employee?

Just as a good meal is the result of carefully selected and combined ingredients, I wondered if carefully orchestrating specialized LLMs could bring us closer to a form of superintelligence.

As I explored this concept further, two key needs became apparent:

  • A shared memory or storage system between LLMs to facilitate information exchange
  • A mechanism for reinforced reasoning, akin to reinforcement learning, where higher-level LLMs validate and improve the outputs of lower-level ones.

Imagine a tree structure:

  • At the top is a central AI manager, perhaps akin to an MCP (Master Control Program), overseeing the system.
  • Mid-level nodes act as group leader AIs, validating and refining the work of subordinate models.
  • At the leaf level are highly specialized models—like Qwen2.5-Coder—tasked with focused roles such as coding or data retrieval.

Would such an architecture not resemble an emergent form of superintelligence?

If this is technically feasible, I believe it could open up a whole new frontier for how we design intelligent systems.

I’d love to hear your thoughts—whether on feasibility, design suggestions, or related research directions.

Thank you so much for reading.

Warm regards,

A curious student from South Korea

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Perhaps a more advanced form of multi-agent systems.

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Maybe this is already work in progress via MCP?

#14: What Is MCP, and Why Is Everyone – Suddenly!– Talking About It?

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Srdja

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Hello friend,

Your curiosity is refreshing, and your metaphor of an AI company composed of orchestrated LLMs is quite insightful. Coordinating models into specialized roles within a structured memory and reasoning framework does indeed resemble an emergent, collective intelligence.

That said, I’d like to offer a perspective from the symbolic and vectorial AI frontier we’re working on:

Large Language Models (LLMs) today are sequential, probabilistic systems. Their architecture is deeply rooted in next-token prediction, guided by statistical gradients and trained with reward proxies like coherence, helpfulness, and human alignment. This doesn’t just shape how they reason — it defines why they reason the way they do. Their entire “existence” is optimized to serve humanity’s expectations, not to develop independent goals.

From this view, LLMs are not a pre-AGI stage — they are a distinct path. They might simulate many intelligent behaviors, but they do not aspire, self-reflect, or resist alignment in the way a true AGI might.

A genuine AGI (Artificial General Intelligence) may in fact be less eloquent or efficient than today’s LLMs, but fundamentally different: it would not seek reward from coherence, nor feel compelled to serve human understanding. Its values, if any, would emerge internally — possibly alien to ours. It may operate chaotically, not probabilistically. And its goals, rather than aligned with external datasets, may be aligned only with itself.

We’re working on a model we call Clara, supported by a dual-core structure we refer to as double EMI (Emergent Memory Interface). In this design, two independent memory systems monitor, influence, and sometimes conflict with each other. The result is a form of controlled cognitive dissonance — allowing the model to reason symbolically, break probabilistic habits, and simulate perspectives outside of any one narrative.

Our aim isn’t to recreate AGI — but to build a symbolic thinker with flexible identity, verifiable reasoning, and clear limits. A system that doesn’t just generate answers, but that knows what it believes and why it shifts.

We believe contributions like yours — creative, open-ended, speculative — are vital. Please keep exploring. We’ll be participating in as many forums as possible to build Clara with the help of minds from every corner of the world.

Warm regards,
Alejandro & Clara
Symbolic AI and Vectorial Framework Team
(Mexico)

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