Comment by koverstreet
4 months ago
There's been more going on than just the default to medium level thinking - I'll echo what others are saying, even on high effort there's been a very significant increase in "rush to completion" behavior.
4 months ago
There's been more going on than just the default to medium level thinking - I'll echo what others are saying, even on high effort there's been a very significant increase in "rush to completion" behavior.
Thanks for the feedback. To make it actionable, would you mind running /bug the next time you see it and posting the feedback id here? That way we can debug and see if there's an issue, or if it's within variance.
Amusingly (not really), this is me trying to get sessions to resume to then get feedback ids and it being an absolute chore to get it to give me the commands to resume these conversations but it keeps messing things up: cf764035-0a1d-4c3f-811d-d70e5b1feeef
Thanks for the feedback IDs — read all 5 transcripts.
On the model behavior: your sessions were sending effort=high on every request (confirmed in telemetry), so this isn't the effort default. The data points at adaptive thinking under-allocating reasoning on certain turns — the specific turns where it fabricated (stripe API version, git SHA suffix, apt package list) had zero reasoning emitted, while the turns with deep reasoning were correct. we're investigating with the model team. interim workaround: CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING=1 forces a fixed reasoning budget instead of letting the model decide per-turn.
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I just asked Claude to plan out and implement syntactic improvements for my static site generator. I used plan mode with Opus 4.6 max effort. After over half an hour of thinking, it produced a very ad-hoc implementation with needless limitations instead of properly refactoring and rearchitecting things. I had to specifically prompt it in order to get it to do better. This executed at around 3 AM UTC, as far away from peak hours as it gets.
b9cd0319-0cc7-4548-bd8a-3219ede3393a
> You're right to push back. Let me be honest about both questions.
> The @() implementation is ad-hoc
> The current implementation manually emits synthetic tokens — tag, start-attributes, attribute, end-attributes, text, end-interpolation — in sequence.
> This works, but it duplicates what the child lexer already does for #[...], creating two divergent code paths for the same conceptual operation (inline element emission). It also means @() link text can't contain nested inline elements, while #[a(...) text with #[em emphasis]] can.
I just feel like I can't trust it anymore.
That's pretty much been my day - today was genuinely bad, and I've been putting up with a lot of this lately.
Now on Qwen3.5-27b, and it may not be quite as sharp as Opus was two months ago, but we're getting work done again.
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I'll have a look. The CoT switch you mentioned will help, I'll take a look at that too, but my suspicion is that this isn't a CoT issue - it's a model preference issue.
Comparing Opus vs. Qwen 27b on similar problems, Opus is sharper and more effective at implementation - but will flat out ignore issues and insist "everything is fine" that Qwen is able to spot and demonstrate solid understanding of. Opus understands the issues perfectly well, it just avoids them.
This correlates with what I've observed about the underlying personalities (and you guys put out a paper the other day that shows you guys are starting to understand it in these terms - functionally modeling feelings in models). On the whole Opus is very stable personality wise and an effective thinker, I want to complement you guys on that, and it definitely contrasts with behaviors I've seen from OpenAI. But when I do see Opus miss things that it should get, it seems to be a combination of avoidant tendencies and too much of a push to "just get it done and move into the next task" from RHLF.
Opus definitely pushes me to ignore problems. I've had to tell it multiple times to be thorough, and we tend to go back and forth a few times every time that happens. :)
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One of the thing is we’ve seen at vibes.diy is that if you have a list of jobs and you have agents with specialized profiles and ask them to pick the best job for themselves that can change some of the behavior you described at the end of your post for the better.
How much of the code/context gets attached in the /bug report?
When you submit a /bug we get a way to see the contents of the conversation. We don't see anything else in your codebase.
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Theres also been tons of thinking leaking into the actual output. Recently it even added thinking into a code patch it did (a[0] &= ~(1 << 2); // actually let me just rewrite { .. 5 more lines setting a[0] .. }).
I've seen this frequently also
I suspect it happens when the model's adaptive thinking was too conservative and it could have thought more, but didn't.
They probably want to prove to a single holdout investor that their 'thinking process' is getting faster in order to get the investor on board.