How does misalignment scale with model intelligence and task complexity?

I think this is twofold:

1. Advanced intelligence requires the ability to traverse between domain valleys in the cognitive manifold. Be it via temperature or some fancy tunneling technique, it's going to be higher error (less coherent) in the valleys of the manifold than naive gradient following to the local minima.

2. It's hard to "punch up" when evaluating intelligence. When someone is a certain amount smarter than you, distinguishing their plausible bullshit from their deep insights is really, really hard.

Coherence requires 2 opposing forces to hold coherence in one dimension and at least 3 of them in higher dimensions of quality.

My team wrote up a paper titled "If You Want Coherence, Orchestrate a Team of Rivals"[1] because we kept finding that upping the reasoning threshold resulted in less coherence - more experimentation before we hit a dead-end to turn around.

So we had a better result from using Haiku (we fail over to Sonnet) over Opus and using a higher reasoning model to decompose tasks rather than perform each one of them.

Once a plan is made, the cheaper models do better as they do not double-think their approaches - they fail or they succeed, they are not as tenacious as the higher cost models.

We can escalate to higher authority and get out of that mess faster if we fail hard and early.

The knowledge of how exactly failure happened seems to be less useful to the higher reasoning model over the action biased models.

Splitting up the tactical and strategic sides of the problem, seems to work similarly to how Generals don't hold guns in a war.

[1] - https://arxiv.org/abs/2601.14351