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Wes Roth
@WesRoth

how Minimax M2.7 was made is absolutely INSANE it "evolved" 100+ times with zero human input They built a research agent using an early version of that same model soon it was handling 30 to 50 percent of their RL team's entire workflow. and then it got WEIRD

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Wes Roth
@WesRoth

The internal harness began autonomously collecting feedback on its own performance, building evaluation sets for internal tasks, and iterating on its own architecture, skills, and memory systems. The agent was rewriting the tools it uses to do its job.

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Wes Roth
@WesRoth

Then they ran a controlled experiment with 100 rounds of autonomous optimization. MiniMax had M2.7 optimize a model's programming performance on an internal scaffold, entirely without human input.

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Wes Roth
@WesRoth

Over 100 rounds. Zero humans. The outcome was a 30 percent performance improvement on their internal evaluation sets.

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Wes Roth
@WesRoth

Then they put it in a competition. OpenAI's MLE-bench where AI models attempt to beat PhD level Machine Learning researchers at their own game. (btw the competitions were run on a single cheap GPU, a roughly $4,000 data center card.)

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Wes Roth
@WesRoth

Results: 9 gold medals, 5 silver, 1 bronze Average medal rate of 66.6% That score ranks second in the world on this benchmark (!) Opus 4.6 at 75.7% GPT-5.4 at 71.2 % Gemini 3.1 at 66.6% (tied with M2.7) ...on a single cheap GPU. Running autonomously for 24 hours.

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Wes Roth
@WesRoth

and that's not even the end of it full breakdown: <a target="_blank" href="https://www.youtube.com/watch?v=7_Q8ECC9PYA" color="blue">youtube.com/watch?v=7_Q8EC…</a>