I don’t think they claim it’s preference for wrong answers.
I think they claim it’s randomness (that is, higher variance, like you say, which does translate to lower coherence). That does sound like lower capability to produce high-quality answers for complex tasks.
The question is: what would it take to improve the situation, and they do give one answer which seems to work OK (ensemble methods). But this does become exponentially more expensive with growing task length in terms of the size of a required ensemble, if I understand the situation correctly.
I think the paper does leave quite a bit of things unanswered. E.g., could one reduce variance without resorting to ensemble methods and without introducing systematic errors, or are there principled obstacles to that?
That being said, the reputation of both senior authors is quite strong. The ICLR 2026 double-blind reviews of this paper are not stellar, but are OK, and one can read further discussion there:
I don’t think they claim it’s preference for wrong answers.
I think they claim it’s randomness (that is, higher variance, like you say, which does translate to lower coherence). That does sound like lower capability to produce high-quality answers for complex tasks.
The question is: what would it take to improve the situation, and they do give one answer which seems to work OK (ensemble methods). But this does become exponentially more expensive with growing task length in terms of the size of a required ensemble, if I understand the situation correctly.
I think the paper does leave quite a bit of things unanswered. E.g., could one reduce variance without resorting to ensemble methods and without introducing systematic errors, or are there principled obstacles to that?
That being said, the reputation of both senior authors is quite strong. The ICLR 2026 double-blind reviews of this paper are not stellar, but are OK, and one can read further discussion there:
https://openreview.net/forum?id=sIBwirjYlY