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> Through extensive experimentation across diverse puzzles, we show that frontier LRMs face a complete accuracy collapse beyond certain complexities. Moreover, they exhibit a counterintuitive scaling limit: their reasoning effort increases with problem complexity up to a point, then declines despite having an adequate token budget.

This is exactly my experience with coding. Start simple and build up complexity, and everything is great until you get to some threshold, at which point it completely falls apart and seems to stop even trying. Getting effective utilization out of Claude + aider involves managing the complexity that the LLM sees.



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