Comment by kaicianflone
3 hours ago
Dissent and consensus among frontier models is a good thing.
Just like on a team of high performers, there are a million ways to skin a grape.
In my research, I've found that models perform better when they operate as a collective system with reputation, incentives, and accountability instead of isolated oracles answering alone.
Agreement, dissent, and correctness should all carry rewards and consequences. Just like in real life.
Collective machine intelligence, not AGI.
It's expensive, but it's also naive to believe a single model will consistently produce profoundly correct answers to profoundly novel questions.
Funny timing. I've been working on a prediction market orchestration that runs Claude and a few others over Polymarket/Kalshi. The models are NOT unanimous. At all, really. I spent about a month convinced that I could just run all five and take majority vote. Eventually I pivoted to a chaining approach where I benchmark areas each model excels, and settled on more like a graph-like architecture where outputs get split and verified by another, then reconstructed, and re-verified at each stage. Has actually been working out pretty well so far, 2 months in consistent profit, but I'm not a millionaire yet.
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Not on objective truth though. That's how you get misinformation.