upvote
Personally, I've found myself getting lazier recently, trying to get a single session to handle tasks that should be broken into multiple sessions, etc. I've suspected claims of nerfing might come from changes in user behavior. I'm glad more disciplined checks are being run.
reply
User behavior is overwhelmingly the most likely reason, regardless of what the text above your reply says.
reply
It could be harness changes too, been quite unhappy with opus 5.5 the last few days and there have been a lot of harness updates.
reply
Why dont we have the website that checks performance every day anymore? It’s a quite simple thing to build and clarifies the question re: degradation.
reply
Who's paying for the tokens, and since all inputs and outputs are logged "for abuse and safety" what is stopping anthropic from identifying which account is running the daily tests once it gets popular, and A/Bing it into a different slot?
reply
deleted
reply
This is an example of LLM text that doesn’t make sense.

Paragraph 1 says there were 30 bug fixes.

Paragraph 2 first says they tested it on 20 of the “tasks”. But why is the denominator 40? Where did that come from? It should be 30.

It goes on to mention another test, this time with 20 tasks. Again, it’s unclear where this is even coming from. Based on the first paragraph it should be 30 not 20.

reply
Sorry this was confusing -

There are 30 tasks in my eval total:

- 20 of them ran on Nov 1, twice each (so 40)

- I created 10 more tasks and ran them on Nov 5 (also ran them twice each)

- Today I ran all 30 tasks, once each, on Opus 5.5 to see if the performance decreased at all

- I only put the comparison against the original 20 in my post, since I saw in another post that people are claiming Opus was nerfed on November 3

This will all get less confusing when I make my benchmark public (hopefully this week).

reply
It stands as written? The previous test had 20 tasks run twice each, so denominator 40. The recent retest did the same 20 tasks but only once each, as mentioned in the caveat. The 30 is presumably either a typo or a separate count (like the test had 20 tasks related to code with 30 bugs/features).

Also - LLM text? If GP is confusing it's because it's overly succinct and might have typos, which are not exactly the hallmarks of LLM text.

reply
2 alternative interpretations to consider:

1. 30 was a typo, it should have been 20.

2. 30 was the full suite, but they only ran 20 of those 30 on Nov 1st and so ran the same corpus again for a fair comparison.

reply
I think it's confusing and incomplete, but not wrong. Paragraph 2 says the Nov 1 test included 20 of the tasks and ran each twice. That's the 40 denominator.

Why paragraph 1 even mentions the full set of 30 tasks if 10 of them are irrelevant to the comparison, I don't know.

I'm not sure if it's LLM text, or somebody who has been reading way too much LLM text and is now corrupted into mimicking it. My guess would be a mix of human and LLM. "...evaluated by tests the agent didn't see" sounds very Claude. The first line (2 sentences) sounds human to me. Everything after sounds AI, though the last line is ambiguous.

It's kind of sad how our meat brains are getting non-consensually trained on this categorization task....

reply
It’s impossible to say for sure. But I never saw anyone write “caveats:” at the end of their analysis. But LLMs do it all the time.
reply
If I read your results correct it solved 5% more problems and used up between 3-5% fewer tokens, with a faster response time?
reply
Correct. But the 5% more solves and 3-5% fewer tokens are within the noise of previous eval runs I've done on models. The faster response time is not.
reply
Someone using an actual statistical test to quantify if there's actual measurable improvement?! An angel among men.
reply
from my basic stats knowledge, it's not reliable to calculate the confidence interval of the sample proportion with fewer than about 10 samples on both sides. So if you're only seeing 1 or 2 failures in 20, and that approximately held as you scaled, you would have to run about 200 tests with each in order to get at least 10 failures in order to calculate the confidence intervals of the two samples and see if they overlap or not.
reply
And this is why not many people do this. Even if you have the decals and the statistical knowledge, it's pretty expensive to do this regularly.
reply
Uncertainty from count statistics are far larger than this. These are two perfectly consistent results there’s no statsig difference
reply
The spending limit definitely goes down much faster as of last week or so.
reply
deleted
reply