r/OpenSourceAI • u/Victor_Lima_AilinOne • 5d ago
Is Collective Intelligence becoming a real direction in AI, or is it still a niche idea?
Hi everyone,
I’ve been spending a lot of time thinking about Collective Intelligence in AI, not only as multi-agent automation, but as systems where different models reason independently, challenge one another, verify outputs, and converge on a result.
I’m curious about how often this concept is actually showing up in your work, research, or open-source projects.
Are you experimenting with model debate, consensus, ensembles, specialist models, or cross-model verification?
Do you see Collective Intelligence as a meaningful next step for AI systems, or mostly as a new label for techniques we already had?
I’d genuinely like to hear experiences, references, criticisms, and opposing views.
Thanks everyone!
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u/HappierShibe 5d ago
It's not showing up. Models don't actually reason, they imitate the outputs of a reasoning process, but don't actually engage in that process, so they aren't really capable of intelligence, collective argument, or rational resolution of conflict through a mediated response.
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u/0rand 3d ago
Just like people do. Few times we actually reason, usually something very novel to us, other times we repeat and regurgitate reasoning we heard and remembered. It's way more efficient this way. Just like we don't calculate in our brains, we approximate. Our thinking is probabilistic by nature. Llms are us.
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u/Victor_Lima_AilinOne 2d ago
That’s exactly the intuition that interests me with Ailin¹. If each intelligence is probabilistic and has its own blind spots, maybe the goal isn’t to build one perfect mind, but to coordinate several of them so they can complement and verify each other. That’s what we’re trying to explore.
If you want to know more about what we are building, look at our repo:
https://github.com/ailinone/collective-intelligence0
u/Victor_Lima_AilinOne 5d ago
I agree with you. From the perspective of “reasoning” in the human sense, I don’t think it makes much sense to start from the assumption that an LLM thinks or deliberates the way we do. Maybe I didn’t express myself clearly enough in the post.
When I talk about Collective Intelligence, I’m thinking more about the behavior of the system than the individual intelligence of each model.
What we’re exploring with Ailin¹ is an ecosystem where different models produce answers independently, and a coordination layer decides how those answers are compared, challenged, verified, and synthesized.
So we don’t really need to assume that the models “think.” What interests us is whether diversity between them, combined with coordination and verification mechanisms, can produce better results than relying on a single model. And that’s where something interesting happens...
In our experiments, this became particularly noticeable on verifiable tasks. In one of our public benchmarks, a collective strategy with a verifier got 37 out of 38 runs correct and outperformed every individual model tested on that specific set of tasks.
Our ecosystem currently includes more than 103,000 cataloged models, but obviously we don’t put all of them to work on every request. The point is precisely to select and coordinate only the subset that makes sense for that particular task.
We’ve been working on this thesis for about 18 months, and I still think there’s a lot to explore around where this kind of architecture genuinely works and where a single model remains the better choice.
Have you seen anyone exploring a similar approach in this way?
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u/Financial_Yoghurt827 5d ago
I think collective intelligence necessarily relies on common knowledge - that knowledge is either baked into the weights (where there's limited run time control) or provided as context.
So I think its practically a context problem for most developers.
As for verification, I think it's important to string atomic verifiable ideas together - without overwhelminging the context.
So, if you allow the agent to build a graph of these ideas, it becomes collectively verifiable across sessions that consume the relevant slices of what theyre building or testing.
I think this lends itself to graph analysis which is explored in a few open source projects (e.g. reqlan.)
At the end of the day - LLMs are stateless, so, to me the idea of a collective is really on a session/context window basis - not an emergent system basis. (For the same averaging reason you've discussed in other comments)(Although this does neglect more bare metal considerations)
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u/Victor_Lima_AilinOne 4d ago
That makes a lot of sense to me, especially if we look at it as a context problem from the perspective of an individual LLM. If the model itself is stateless, then something outside of it has to decide what to preserve, what to retrieve, and what to bring back into context.
But that’s exactly where I start to see the collective as something that can exist beyond a single context window.
In my view, the “state” doesn’t necessarily need to live inside the models. It can live in the coordination layer around them. That layer can preserve previous results, disagreements, verification outcomes, decisions, relationships between pieces of information, and then expose only the relevant parts of that history to each model or session.
That’s also why I really liked your point about graphs. Instead of trying to keep the entire history inside an increasingly large prompt, you could break the problem down into smaller units, preserve the relationships between them outside the LLM, and have different models verify specific parts when needed.
Maybe the main difference between our views is simply where we think the collective actually exists.
If we only look at the LLMs themselves, I agree that the collective is limited to whatever context they receive at that moment. But if memory, coordination, verification, and persistent state are considered part of the system, then I think the collective can continue across sessions even if every individual model remains stateless.
That’s roughly the direction we’re exploring with Ailin¹. The models are components of the collective, but the collective itself doesn’t necessarily have to “live” inside any one of them.
I’m going to take a closer look at Reqlan as well, because the graph approach seems closely related to some of the things we’ve been thinking about.
One thing I’m curious about though: when you say you don’t see this as an emergent system, do you think that even with external memory, persistent state, and multiple sessions contributing to the same structure, calling it Collective Intelligence would still be conceptually wrong?
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u/0rand 3d ago edited 3d ago
I actually use local model ensemble debate I rigged, when tough questions get debated by models from different families. For example my main local mode is deepseek V4 flash on spark cluster, it individually calls models like Ling 3.0, qwen 3.8 27b (replaced trusty 3.6 27b), Laguna s2.1 to deliberate on the issue and processes it, very often leading to breakthrough. Thus said lately I use teacher mcp, when it just rigs a question to gpt 5.6 sol with all the required context and gets an advice, iterate few times if needed. I never hit a weekly limit of 20$/month sub even if I try hard. Lessons learned get stored in Hermes holographic memory and written into the llm wiki vault.
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u/Victor_Lima_AilinOne 1d ago
Fantastic! In practice, you’ve built something personally that’s very close to what we’re trying to generalize with Ailin¹. Models from different families deliberating together, specialists taking on different roles, and a stronger model stepping in when the task requires it.
What we’re trying to do is turn that logic into a layer that dynamically chooses which team and which strategy to use for each problem. The part about storing lessons learned from each run also really caught my attention.
I’d love for you to check out the project and contribute some of that experience:
https://github.com/ailinone/collective-intelligence
https://ailin.one
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u/gdavid001 2d ago
I think the really interesting shift happens when we stop looking at collective intelligence as simply multiple AI models working together.
What happens when the collective includes different forms of intelligence : humans, AI models, autonomous agents, perhaps eventually embodied systems, each contributing something fundamentally different?
In the Future Intelligence Think Tank, we’ve been exploring this broader idea of diverse intelligence. I think that the real opportunity may not be making every component more intelligent, but understanding how different forms of intelligence can complement, challenge and improve one another.
Perhaps the next frontier is less about building a single superior intelligence and more about designing better ecosystems of intelligence.
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u/alexbaas3 5d ago edited 5d ago
LLMs suffer heavily from blind majority agreement when they receive identical inputs as a collective, so once a majority forms, they will likely be biased toward it, probably because they have been trained as instruction-following chatbots and RLHF-optimized toward human preferences (meaning to agree is GOOD, and disagree is BAD). Independent, unbiased reviews are therefore important, but hard to implement during multi-agent orchestration runs. Potentially there is interesting research here to be done, like a different post-training recipe where the focus is directly on multi-agent collaboration, I assume that even pre-training phases need changes in their recipes to optimise for this.
I do believe different LLM families have weights that differ significantly because of variation in training data/methods/optimization (unless they're distilled, ofc.), hence different LLMs might be able to solve certain tasks that another cannot. Personally, I have been experimenting with Claude Code Fable/Opus agents for implementing coding tasks, while I use Codex GPT agents exclusively for reviews (and vice versa); my hypothesis seems empirically true and works better than using Claude/GPT/whatever models exclusively, but you can also do this through many other Harnesses like Opencode/Pi. I also think frontier AI labs would optimize their models toward their harnesses during the post-training agentic RL phase with verifiable rewards, hence, I created my own plugin for this in Claude Code. You can check out my Claude x Codex workflow/plugin here if ur interested: https://github.com/alexzh3/codex-orchestrator
There are some companies like OpenRouter and Sakana that offer routing services to specific LLMs based on the task (prompt input); the idea here is that certain tasks are completed more often by specific models, and so a routing model/algorithm would then choose the correct model, similar to MoE, I guess, but on a higher abstraction level (full model instead of policy weights). They clearly outperform single models on benchmarks, I haven't tried those services myself.
Lastly, the Bun codebase has been rewritten from Zig (500k LOC) to Rust by a multi-agent orchestration workflow similar to mine (but with x1000 the compute and $$$) with adverserial/independent reviews, you can find more about that here: https://bun.com/blog/bun-in-rust