[Kim_Bruning]

> You're implicitly assuming that what you asked the LLM to do is unrepresented in the training data. That assumption is usually faulty - very few of the ideas and concepts we come up with in our everyday lives are truly new.

I made a cursed CPU in the game 'Turing Complete'; and had an older version of claude build me an assembler for it?

Good luck finding THAT in the training data. :-P

(just to be sure, I then had it write actual programs in that new assembly language)

[withinboredom]

But the ideas are not 'new'. A benchmark that I use to tell me if an AI is overfitted is to present the AI with a recent paper (especially one like a paxos variant) and have it build that. If it writes general paxos instead of what the paper specified, its overfitted.

Claude 4.5: not overfitted too much -- does the right thing 6/10 times.

Claude 4.6: overfitted -- does the right thing 2/10 times.

OpenAI 5.3: overfitted -- does the right thing 3/10 times.

These aren't perfect benchmarks, but it lets me know how much babysitting I need to do.

My point being that older Claude models weren't overfitted nearly as much, so I'm confirming what you're saying.

[Kim_Bruning]

Could also be that the model has stronger priors wrt Paxos (and thus has Opinions on what good Paxos should look like)

At any rate, with an assembler, you end up with a lot of random letter-salad mnemonics with odd use cases, so that is very likely to tokenize in interesting ways at the very least.