Wait I think there are 2 levels of summary. Anthropic is definitely not showing its real thinking even with enterprise agreements. For example in Claude.ai the thinking traces are not real and are themselves summaries.
The text in Claude Code’s “Extended Thinking” output
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Re: The text in Claude Code’s “Extended Thinking” output
#12> It isn’t the actual thinking that drove the model’s actions in a session- but a summary of the thinking logic. This is like using saving a jpeg as a .bmp and then editing the .bmp and presenting it as a .jpeg. The conversion produces data loss.
You've got that backwards, .bmp is a lossless format and .jpeg is the lossy one.
Re: The text in Claude Code’s “Extended Thinking” output
#13IMHO I've never found the entire reasoning chain that particularly useful for my work. For me having a summary is honestly better from a context management perspective. I understand why they would encrypt it though, because those reasoning chains are VERY useful if you're distilling the model.
Re: The text in Claude Code’s “Extended Thinking” output
#14The full thinking logs are also a summary of a thinking process presumably consistent with one necessary to generate the provided answer. Nobody really understands how LLMs think. Thinking logs seem to be accurate, and summary thinking logs seem to be a good summary of the full thinking logs. If it's useful, it's useful, enjoy. If you aren't comfortable with that, don't use LLMs. You aren't going to get a mathematica…
Yeah. The output is magic either way, with or without reasoning.
For daily use I actually like the reasoning summary to be brief/quick to scan.
That said, I understand the author’s desire for the real thing. It just feels better to have that access, especially when Anthropic will give it to you, but encrypted.
Re: The text in Claude Code’s “Extended Thinking” output
#15> It isn’t the actual thinking that drove the model’s actions in a session- but a summary of the thinking logic. This is like using saving a jpeg as a .bmp and then editing the .bmp and presenting it as a .jpeg. The conversion produces data loss. You've got that backwards, .bmp is a lossless format and .jpeg is the lossy one.
My bad! 10 points for House Slytherin!
Re: The text in Claude Code’s “Extended Thinking” output
#16Re: The text in Claude Code’s “Extended Thinking” output
#17no way, the contents of "reasoning_summary" are summarized? fyi openai does the same; not really surprising or particularly evil
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Re: The text in Claude Code’s “Extended Thinking” output
#18This is not just Anthropic. Almost all big AI companies, including OpenAI and Google, hide their model's actual reasoning. This is because revealing the raw reasoning exposes exactly how the AI processes information.
These companies spend in huge amounts on R&D to develop a thinking process that is superior to their competition. Exposing those thinking mechanics to competitors would completely defeat the purpose of their spending. They simply won't do it. It's like you telling your exact location to someone who is trying to hunt you down.
Re: The text in Claude Code’s “Extended Thinking” output
#19AIUI it's fairly well established that the models can be saying one thing and "really" thinking another anyhow. The ones I recall seeing traced how simple one-digit arithmetic was done in the chat versus the actual activations under the hood. Tracing a real, non-trivial task through that way would be challenging, and I'd expect it is unlikely that the reasoning would say one thing while some utterly unrelated actual thought process is happening below, but I would expect that there might be a lot of places where the text of the reasoning diverges from what is "actually" being done. I'm not sure the full reasoning readout would produce much real insight anyhow.
I suspect that in some decades, as other architectures are found and used, that the inability of an LLM to "think" without also emitting a token will be seen as one of their fundamental limitations.
Re: The text in Claude Code’s “Extended Thinking” output
#20Not surprised at this. The questoins for enterprises are
+ where can you depend on a black box as a service?
+ what evals and observability do you need to deploy a black box as a service confidently?
+ what's the ROI (considering a total footprint of people, token spend, infrastructure, service, ops etc.)
The LLM providers will clearly evolve to be more and more opaque as their services get more capable. The frontier models may even be provided as purely internal advisor or async only so they can monitor your CoT and final answers for cyber etc.