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opus 5.5 is anthropic's My Beautiful Dark Twisted Fantasy
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What are idea basins and how do agents explore them?View quoted post on X
A few weeks ago, researchers from different labs independently discovered what we now call ‘Idea-basins’: As an autonomous agent iterates to solve a problem, it gets stuck into the methods it has tried before and its trajectories remain in localized regions of the solution space. Techniques like ‘Fork-and-Flush’ and ‘Scaling Discovery requires forgetting’ reduce this idea fixation by periodically deleting the session context.
In this post we study how frontier models explore different idea basins for AutoResearchExam. E.g. for a problem on accelerating transformer decoder graphs on CPUs models can try Numpy+Asm Kernels, Native BLAS, JIT compilers and other idea basins. We find that some models switch ideas more than others while others get to the best idea faster and explore deeper.
We find that most runs stick with one or two ideas, and models that submit more spend the extra submissions refining and overfit more.
We see that models explore most in the first six hours, after which visits to new idea basins drop sharply. This suggests models start refining early leaving potential gains on the table: 58% of runs never reach the basin where the best solution to their task was found. Finally, bold exploration pays off: exploring a new idea gains about nine times as much as a refining step on average.
Read more in our blog:
bespokelabs.ai/blog/how-model…
We switched 10 people from iPhone to Blue. Weeks later, none of them want their iPhone back.View quoted post on X
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