alt Hacker NewsAny model with a good score on this benchmark would have a good claim on superhuman abilities. Humans are pretty terrible at being thrown a long policy document and being expected to follow it
And while we shouldn't anthropomorphize these models too much, I wouldn't be surprised if many of the core reasons for failures are similar. Working memory is a limited resource; you can only focus on so many things at once; reasoning depth is limited; and many real-world policies are not actually meant to be implemented in the same way they are written and have insufficient specification of edge cases
With humans, we usually do the equivalent of RLHF, both via "training" with simulated cases, and via feedback while on the job. You would never hand a newbie a 124 page policy document and expect them to correctly apply it on the first task, or to do it reliably in the first month
Replies
"You would never hand a newbie a 124 page policy document and expect them to correctly apply it on the first task, or to do it reliably in the first month"
So the behavior policy should reside in the weights, not in some ever expanding KV cache context, like sticking it in the wrong orifice if the goal is to be reproductive.
Instead of putting the policy document in some tightly crammed RAM, shove it in the weights that already exist, online / post training.
It does make one wonder if there is some way to compute the change to the weights from the computed context (so that we can free it up) without essentially continued "pre" training RMAD after each communication round.
Exactly: The best way to deal with this for humans is to use procedural scripts for different tasks, referring to the relevant bits of the (declarative) documentation.
I would imagine that doing something similar (using agent skills for insurance) would work much better for AI.
> Any model with a good score on this benchmark would have a good claim on superhuman abilities. Humans are pretty terrible at being thrown a long policy document and being expected to follow it
Hmmm, if AI has to grow in the workplace, it has to follow processes to the letter, yet claude code forgets by the second turn my "don't commit" prompt.
Of course claude code is a generic and crap harness over terrific models that can't fit bureaucratic processes, and is increasingly worse at doing so anyway since Opus 4.6 peak.
That's a great comparison, human vs ai on a wall of text.
The problem is that it doesn't fit the sales pitch of LLMs and agents - humanlike or better, repeatably, 24/7, for a fraction of the price, you just need to make sure that you give it all the rules.
Unfortunately we can't really have a meaningful conversation until the money vampires have left so we will need to reschedule this until after the bubble.
isn't it because there are too many contradictions and ambiguity? the reason it works for humans is because we don't apply everything at once either.
The challenge with comparing these things to humans, is that humans learn. A newbie might not respect your organization’s set of policies on day one, but what about 3 months in? Or 3 years? Meanwhile there’s still no reasonable mechanism for automatically fine tuning LLMs or adjusting their harnesses to make them better at completing your organization’s objectives more successfully. They’re still overwhelmingly governed by the shared weights and harness policies found to be successful for the average case.