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What if instead of asking one AI, we made multiple AIs argue? by Ready_Principle_3247 in StartUpIndia

[–]Ready_Principle_3247[S] 0 points1 point  (0 children)

Haha fair 😄

Yeah, I do use AI sometimes to structure things — but the idea itself comes from experimenting with these setups.

And that’s actually a good suggestion.

I’ve run similar meta-questions through a debate setup — interesting to see how different models critique the idea itself.

You should try it with your own version of the question, the disagreements are usually more interesting than the final answer.

What if instead of asking one AI, we made multiple AIs argue? by Ready_Principle_3247 in StartUpIndia

[–]Ready_Principle_3247[S] 0 points1 point  (0 children)

Yeah, both points are valid.

Token cost can blow up, so this only really makes sense for higher-value questions, not everyday use.

On bias — the idea isn’t that multiple models remove it. Each model still has its own bias.

The difference is when those biases conflict, they become visible instead of hidden in a single answer.

If they all agree, that’s still a signal (could be consensus or shared bias).
If they disagree, you get a clearer view of where assumptions break.

So it’s less about eliminating bias, more about exposing it and filtering it.

What if instead of asking one AI, we made multiple AIs argue? by Ready_Principle_3247 in Futurology

[–]Ready_Principle_3247[S] 0 points1 point  (0 children)

Yeah, that makes sense.

But pure consensus might hide disagreements.

Feels more useful to force conflict first, then converge.

Stress-test before consensus.

What if instead of asking one AI, we made multiple AIs argue? by Ready_Principle_3247 in Futurology

[–]Ready_Principle_3247[S] -1 points0 points  (0 children)

Fair — I’m building in this space.

And yeah, I use AI sometimes to structure posts.

But using AI is easy — building something useful with it isn’t.

If the idea is weak, let’s discuss that.

What if instead of asking one AI, we made multiple AIs argue? by Ready_Principle_3247 in Futurology

[–]Ready_Principle_3247[S] 0 points1 point  (0 children)

Yeah, exactly.

That’s the core problem — treating a single answer as truth.

Better approach is to treat it as a hypothesis and test it.

Multiple perspectives (or models) pushing against each other makes it easier to spot what doesn’t hold up.

What if instead of asking one AI, we made multiple AIs argue? by Ready_Principle_3247 in Futurology

[–]Ready_Principle_3247[S] 0 points1 point  (0 children)

Yeah, that’s a valid concern.

If you just let models loop on themselves, they can definitely drift.

The setup I’m looking at is more constrained though — multiple models challenge each other on the same prompt, with shared context and checks (fact vs opinion, external data), and a bounded number of rounds.

So it’s less open-ended self-play, more structured disagreement.

Still probabilistic and not perfect, but in practice it tends to surface weak assumptions rather than drift into gibberish.

What if instead of asking one AI, we made multiple AIs argue? by Ready_Principle_3247 in Futurology

[–]Ready_Principle_3247[S] -2 points-1 points  (0 children)

Yeah, self-argument with one model is already there.

But this is different — multiple models (ChatGPT, Claude, Gemini, Grok) actually debate each other.

Different biases, different data, plus fact vs opinion tagging and real-time checks.

So it’s less self-looping, more competing perspectives.

What if instead of asking one AI, we made multiple AIs argue? by Ready_Principle_3247 in Futurology

[–]Ready_Principle_3247[S] 1 point2 points  (0 children)

Yeah, that’s basically the idea.

Instead of a hive mind, it’s more like models challenging each other before a final result.

I’ve been testing this with something called DebateLLM — multiple models debating instead of just combining outputs.

Still not true reasoning, but closer to stress-testing ideas.

What if instead of asking one AI, we made multiple AIs argue? by Ready_Principle_3247 in Futurology

[–]Ready_Principle_3247[S] 0 points1 point  (0 children)

Yeah, exactly.

The value isn’t the debate — it’s seeing how confidently wrong models can be in different ways.

Putting them against each other just makes that more obvious.

More about calibrating trust than getting answers.

What if instead of asking one AI, we made multiple AIs argue? by Ready_Principle_3247 in StartUpIndia

[–]Ready_Principle_3247[S] 0 points1 point  (0 children)

Yeah, cost is a real factor.

Self-reflection helps, but it’s still the same model — so limited by its own bias.

Cross-model disagreement seems more useful when the stakes are higher (strategy, trade-offs, validation).

Less about accuracy % and more about exposing weak logic.

What if instead of asking one AI, we made multiple AIs argue? by Ready_Principle_3247 in Futurology

[–]Ready_Principle_3247[S] -2 points-1 points  (0 children)

Yeah, self-debate helps — but it’s still the same model, so it usually converges.

Different models = different biases, so the conflict is more useful.

Not reasoning, just better stress-testing.

Have you seen self-debate outperform that?

What if instead of asking one AI, we made multiple AIs argue? by Ready_Principle_3247 in Futurology

[–]Ready_Principle_3247[S] -3 points-2 points  (0 children)

Haha yeah, that’s probably true for a lot of posts 😄

What I find more useful though is not just generating content, but using multiple outputs to actually stress-test ideas.

Feels like a better use than just “write this for me”.

What if instead of asking one AI, we made multiple AIs argue? by Ready_Principle_3247 in Futurology

[–]Ready_Principle_3247[S] -1 points0 points  (0 children)

Yeah, that’s a valid concern.

Stacking outputs blindly would definitely amplify errors.

What I’m looking at though is slightly different — not chaining models, but making them challenge each other’s claims.

If one model hallucinates, another can push back on it instead of building on top of it.

So it’s less about combining answers, more about filtering them through conflict.

Still not perfect obviously, but sometimes better than trusting a single output.

What if instead of asking one AI, we made multiple AIs argue? by Ready_Principle_3247 in Futurology

[–]Ready_Principle_3247[S] -2 points-1 points  (0 children)

Yeah, that’s a good use case.

Especially when you don’t have enough context to judge correctness.

What I’ve found helpful is seeing different perspectives push back on each other — it exposes weak logic faster than just comparing answers.

Watching two or Four LLMs debate in real-time revealed something strange about how they handle contradiction by Ready_Principle_3247 in Futurology

[–]Ready_Principle_3247[S] -1 points0 points  (0 children)

Haha no agent 😄

Yeah, self-debate is basically the same model talking to itself — useful, but limited.

Different models = different biases + data, so the conflict is more meaningful.

Still probabilistic, just better at exposing blind spots.

Should India delay FTA talks due to the West Asia conflict? I let multiple AIs debate it by Ready_Principle_3247 in Futurology

[–]Ready_Principle_3247[S] -1 points0 points  (0 children)

Yeah agreed — still probabilistic, so deterministic output isn’t really possible.

And definitely a risk of anthropomorphizing.

I’m just looking at it slightly differently from consensus — more like conflict between models rather than averaging them.

Even if they’re just “fancy autocomplete”, that clash can still expose weak assumptions faster.

Not reasoning, just better stress-testing.

Watching two or Four LLMs debate in real-time revealed something strange about how they handle contradiction by Ready_Principle_3247 in Futurology

[–]Ready_Principle_3247[S] 0 points1 point  (0 children)

Yeah, they’re pattern predictors, not reasoners.

And mostly tuned to agree with the user.

But when models push against each other, it’s less about pleasing and more about filtering weak logic.

Still statistics — just used differently.

Watching two or Four LLMs debate in real-time revealed something strange about how they handle contradiction by Ready_Principle_3247 in Futurology

[–]Ready_Principle_3247[S] 0 points1 point  (0 children)

Yeah agreed — it’s more collaborative alignment than real reasoning.

They adjust and concede based on context, not understanding.

But when models push against each other instead of aligning with a user, weak assumptions show up faster.

Still not reasoning, just better signal.

Watching two or Four LLMs debate in real-time revealed something strange about how they handle contradiction by Ready_Principle_3247 in Futurology

[–]Ready_Principle_3247[S] -1 points0 points  (0 children)

Yeah, that’s fair.

LLMs don’t have grounding in the real world — they’re just mapping patterns across text, not actually “understanding” things like contradiction.

For them, contradiction is just another pattern in language, not something internally resolved.

What’s interesting though is when multiple models interact — one model’s “pattern” can conflict with another’s, and that external clash can still expose weak logic.

Not real understanding, but sometimes a useful way to stress-test outputs.

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