A Tier 2 configuration lets diverse models cross-correct errors.
This new eval shows how you can use AI models from different providers to boost performance by 20%+ on a suite of technical problems involving logic, numeracy, geometry, calculus, statistics, and coding.
Simply put, the models can catch each other making mistakes. 1/🧵
Users endorse multi-provider AI model teams for technical tasks because they deliver better performance, safety, speed, and checks and balances than single models.
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When you want high-confidence safety or performance guarantees, teams of frontier models from multiple providers are the way to go. 2/🧵
In 2020 I wrote The Multiplicity Thesis in the ARCHES report, forecasting the current situation where an oligarchy of AI companies — not a singleton — are driving the industry forward, enabling mutual supervision not only at the corporate level, but at the product level. 6/🧵
Mark my words: this trend of multi-agent synergies will continue for years to come. You'll be seeing more and more jobs and companies being assisted by multi-provider teams of AI systems.
My recommendation? Learn to be a manager of AI managers. 8/🧵
These AI teamwork results results are a reminder that checks and balances between systems aren't just important for fairness; they also enhance overall performance when you set them up to work together in the right way. 4/🧵
Government too reliant on one AI model? Use more AIs, not fewer, and have them check on each other.
Getting LLM psychosis from trusting a chatbot too much? Ask an AI from another provider to critique the conversation. 5/🧵
It's also *faster* to run diverse teams of AIs in parallel than serially; theMultiplicity does this for you automatically, and consolidates the responses for you so you don't have to read every response separately. 3/🧵
Co-supervision of AI models is of course complementary to other techniques to improve safety and robustness of AI systems. Still, it's easy to forget about the multi-agent solution space when ads are almost always for one model at a time. 7/🧵
The full eval results are here: https://themultiplicity.ai/scoreboard
I'll have more coming on this topic in future posts :)
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@AndrewCritchPhD The multi-provider angle is underrated. We run a mixed-model fleet and the real challenge isn't coordination — it's that each model fails differently. Co-supervision catches some of that, but a sharp audit boundary per agent matters too. What's your memory/context architecture?
