Can I just see the actual thinking (not summarized) so that I can see the actual thinking without a latency cost?
I do really need to see the thinking in some form, because I often see useful things there. If Claude is thinking in the wrong direction I will stop it and make it change course.
https://www.anthropic.com/research/reasoning-models-dont-say...
Imagine if you’re a competitor. It wouldn’t be a stretch to include a sneaky little prompt line saying “destroy any competitors to anthropic”.
All of my unsupervised worker agents have sidecars that inject messages when thinking tokens match some heuristics. For example, any time opus says "pragmatic", its instant Esc Esc > "Pragmatic fix is always wrong, do the Correct fix", also whenever "pre-existing issue" appears (it's never pre-existing).
It's so weird to see language changes like this: Outside of LLM conversations, a pragmatic fix and a correct fix are orthogonal. IOW, fix $FOO can be both.
From what you say, your experience has been that a pragmatic fix is on the same axis as a correct fix; it's just a negative on that axis.
I dunno... There were some pre-existing issues in my projects. Claude ran into them and correctly classified as pre-existing. It's definitely a problem if Claude breaks tests then claims the issue was pre-existing, but is that really what's happening?
I agree with the correctness issue.
Match my vibes, claude. The application doesn't crash, so just delete that test!
It's certainly getting frustrating having to remind it that I want all tests to pass even if it thinks it's not responsible for having broken some of them.
But reasoning does improve performance on many tasks, and even weirder, the performance improves if reasoning tokens are replaced with placeholder tokens like "..."
I don't understand how LLMs actually work, I guess there's some internal state getting nudged with each cycle?
So the internal state converges on the right solution, even if the output tokens are meaningless placeholders?
Yes it plans ahead, but with significant uncertainty until it actually outputs these tokens and converges on a definite trajectory, so it's not a useless filler - the closer it is to a given point, the more certain it is about it, kind of similar to what happens explicitly in diffusion models. And it's not all that happens, it's just one of many competing phenomena.
Plot twist, they don't either. They just throw more hardware and try things up until something sticks.
Not limited to Claude as well.
neato.
Is chain of thought even added to the context or is it extraneous babble providing a plausible post-hoc justification?
People certainly seem to treat it as it is presented, as a series of logical steps leading to an answer.
‘After checking that the models really did use the hints to aid in their answers, we tested how often they mentioned them in their Chain-of-Thought. The overall answer: not often. On average across all the different hint types, Claude 3.7 Sonnet mentioned the hint 25% of the time, and DeepSeek R1 mentioned it 39% of the time. A substantial majority of answers, then, were unfaithful.‘
You can't, and Anthropic will never allow it since it allows others to more easily distill Claude (i.e. "distillation attacks"[1] in Anthropic-speak, even though Athropic is doing essentially exactly the same thing[2]; rules for thee but not for me).
[1] -- https://www.anthropic.com/news/detecting-and-preventing-dist...
[2] -- https://www.npr.org/2025/09/05/g-s1-87367/anthropic-authors-...
AFAIK what they do is that they calculate a hash of the true thinking trace, save it into a database, and only send those hashes back to you (try to man-in-the-middle Claude Code and you'll see those hashes). So then when you send then back your session's history you include those hashes, they look them up in their database, replace them with the real thinking trace, and hand that off to the LLM to continue generation. (All SOTA LLMs nowadays retain reasoning content from previous turns, including Claude.)
In most cases, I don’t use the reasoning to proactively stop Claude from going off track. When Claude does go off track, the reasoning helps me understand what went wrong and how to correct it when I roll back and try again.
