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The text in Claude Code’s “Extended Thinking” output
- apothegm 4mo agoSlashdotted.
- ur-whale 4mo agoWhen you have no moat, you have to try and find desperate ways to manufacture one.
- anuramat 4mo agowdym?
- ur-whale 4mo ago> wdym? https://en.wikipedia.org/wiki/Economic_moat https://en.wikipedia.org/wiki/Economic_moat
- anuramat 4mo agohow is summarized CoT a moat, and how is having the top 2 LLMs not a moat?
- Closi 4mo agoIf you have the full outputs, it might make it easier for competitors to distil the model or reverse engineer the full process. It may also be that misaligned responses can be in CoT which OpenAI does not want to show to users.
- dragonwriter 4mo agoNot revealing actual thinking traces prevents mdoel distillation on yhe actual output (thinking traces are a key part of the output) which makes it harder for conpetitors to catch up (a moat). Being currently in the lead in a category is not a moat,a moat is whatever creates a barrier to competitors catching up when you are in the lead. Merely being in the lead is not a moat except in a market with strong network externalities.
- anuramat 4mo agounrestricted access to better models at compute prices = better synthetic data and faster research, so its not just about the product imho
- singron 4mo agoOther companies were allegedly distilling the models by training on the reasoning output. By hiding the reasoning tokens, it makes it harder to do this. You can still try to distill the models, but you can't distill reasoning itself as well. This could all be optics as well to try to give the appearance of a defensible moat. E.g. they can claim to investors that they are able to protect a significant chunk of their intellectual property this way. I'm not sure if anyone has a study about how significant the summarization is to distillation.
- dragonwriter 4mo ago> Other companies were allegedly distilling the models by training on the reasoning output In the case of makers of open-source models (which are also competition), there is no allegedly, they were (and still are) openly doing that.
- nullc 4mo agoIn the case of the closed models too... Claude would happily tell you it was deepseek-v3 if you asked in chinese until it caught public attention and they papered over it.
- dragonwriter 4mo agoThe word “openly” in my post there for a reason; the commercial models are not openly distilled from competitors: many open source models have in their model documentation that distillation was done from a dataset drawn from specific other models, including commercial models. That distillation might be inferred from the behavior of commercial models is not the same as them openly doing it.
- nullc 4mo agoFair enough!
- bpodgursky 4mo agoThe 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 mathematical proof of your output, just learn to be comfortable with that, or opt out and be a goat farmer.
- 0o_MrPatrick_o0 4mo agoI want to measure performance drift over time. Having access to the reasoning text and output would help with performance measurement.
- solarkraft 4mo agoYeah. 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.
- dragonwriter 4mo ago> The full thinking logs are also a summary of a thinking process presumably consistent with one necessary to generate the provided answer. No, they aren't a summary. They are the actual decoding of the sequence of tokens emitted during the the “thinking” stage of response generation. Just as with, say, a human onner monolog in words vs actual speech, they are a product of the same output process as the non-thinking tokens. They aren’t a translation of the internal process that precedes the output mapped into language, either as a full result or a summary.
- fieldcny 4mo agoduh. Computers don’t think they process, those are very different activities.
- anuramat 4mo agono way, the contents of "reasoning_summary" are summarized? fyi openai does the same; not really surprising or particularly evil
- deleted 4mo ago[deleted]
- knollimar 4mo agoNot evil but full of hubris
- anuramat 4mo agoI don't see any hubris in competition
- knollimar 4mo ago"Our models are so much better than our competition that we would rather deliver a worse product to consumers than let people copy it" is how I read the stance
- anuramat 4mo ago> so much better it's enough for them to be slightly better for this to make sense; I'm not sure most people would consider this to be a worse product either -- it's annoying for devs and makes hotswapping models more of a problem, but who has the time to read CoT as a user?
- handoflixue 4mo agoIt seems weird to call it "hubris" when you have proof that multiple competitors have tried to do similar distillations. Every closed-source project and really the vast majority of commercial exercises involve a large amount of "prevent consumers from copying this" - Coca Cola's formula is trademarked, Windows is copyrighted, etc.
- tsunamifury 4mo agoIt’s not surprising than the Sota model makers core goal is to get user dependent while denying them increasing amounts of understanding of how it works to form a deeply unhealthy dependency. Tell me this. If you hired a junior engineer or designer who refused to explain their thinking on their code and how they solved for the spec what would you do? (That being said the reasoning output is still a summary of the Kvcache)
- orangecat 4mo ago* If you hired a junior engineer or designer who refused to explain their thinking on their code* Any explanation that someone gives of their thinking process is necessarily lossy and likely partially confabulated.
- tsunamifury 4mo agoDid you not even bother to read to even the end of the comment before jumping at 'correcting' someone?
- simianwords 4mo agoWait 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.
- deleted 4mo ago[deleted]
- furyofantares 4mo ago> 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.
- 0o_MrPatrick_o0 4mo agoMy bad! 10 points for House Slytherin!
- altmanaltman 4mo agoalso a typo in the last sentence you're vrs your
- 0o_MrPatrick_o0 4mo agoI missed my coffee! Ty! Five points to Slytherin.
- altmanaltman 4mo agowait till my father hears about this!
- glaslong 4mo agoWeirdly pleasant, if minor, signal of human authorship
- altmanaltman 4mo agoYeah, definitely it's a nice thing in today's context, weirdly. But also, you shouldn't really be making typos if you're writing an article and are using a basic spellcheck. The text is clearly human-written just because it doesn't smell like AI (in this case, even if it was written by AI and produced this particular output, that's okay imo). I deal a lot with AI writing and writing in general, as I worked as an editor in another life so it's natural to me to see writing and form an objective opinion on it.
- _fat_santa 4mo agoIMHO 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.
- stavros 4mo agoThe summary doesn't go into the context, it's for human consumption. The CoT itself goes into the context.
- nomel 4mo agoFrom my experiments with Opus and Sonnet (at least the models where you can still see COT), only the last two COT go into context.
- anticensor 4mo agoWhereas on ChatGPT, _all_ reasoning traces and all branches (including the unselected ones) go into context.
- StizzurpXDD 4mo agoThis 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.
- duskwuff 4mo agoMore to the point - if they expose their model's "thinking" inference, competitors can train on that to replicate the results. If they postprocess that content, e.g. by summarizing it, it's no longer as useful to competitors.
- StizzurpXDD 4mo agoExactly. Google won't like it if they spend millions to make Gemini 3.5 Pro's thinking the best in the world, only for Anthropic or OpenAI to copy it by just seeing the thinking process.
- metadat 4mo ago[dead]
- _aavaa_ 4mo agoOr like providing the world’s information in machine readable format that the AI companies can convert into model weights without getting permission or compensating the rights holders
- jerf 4mo agoAIUI 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.
- adi_pradhan 4mo agoNot 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.
- jimmypk 4mo ago[flagged]
- HarHarVeryFunny 4mo agoThis is nothing new - these companies don't want their model's output to be useful for distillation/training, so they just give a "summary" of its thinking steps rather than the actual sequence. RL (the basis of LLM "thinking") is a pretty crude way to achieve the appearance of reasoning given that it reinforces all the steps, including missteps, that got it to a reward. Providing a summary could be seen as form of sane-washing, making the model look more purposeful and directed than it really is!
- craigmart 4mo agoThis is something we have known for a very long time, and companies are not trying to hide that either. They do it to avoid letting competitors train their models on the CoTs
- stingraycharles 4mo agoYes hasn’t this been around since Opus 4.6? I very much recall this change happening around January or February, and it was very explicitly to prevent distillation. Sonnet does not have this limitation. Fun fact: if you go back to the old school from 2 years ago and provide explicit CoT prompts, you get the full thinking prompts back again! So you disable thinking altogether, and instead make thinking part of the regular prompt by prompting it: “Before providing your answer, think step by step. For example: The use is asking me to… I need to think about the blah blah. First, I should foo the bar, and then blah blah. Answer: <put your final answer here>” And tada.wav we have CoT as it worked in the GPT3 era back again.
- 0o_MrPatrick_o0 4mo agoAwesome share! Thank you!
- KellyCriterion 4mo ago- tada.wav - Still, one of the daily most played WAV files worldwide, Id guess? :-D
- stingraycharles 4mo agolol I’ve been using this since the IRC days I think, I’ll never forget that sound; as a matter of fact, I’ve got a Claude Code completion hook that plays this sound whenever it’s done.
- dcrazy 4mo agoI thought this was considered best practice? I actually prefer it to exposed thought channel, much like how I would prefer a human answer with supporting logic instead of an explanation of their problem-solving approach.
- philipwhiuk 4mo agoTo be honest I thought the 'thinking' was the model being asked 'how did you come up with that' and then it generating a plausible explanation. I know at one point this was correct. Humans somewhat do the same - something that's been demonstrated in split-brain experiments.
- devmor 4mo agoThat's not really how LLMs work at all. I would really recommend checking out something like [1] to get a rough understanding and avoid attributing too much to them. 1. https://medium.com/@eshvargb/the-llm-journey-how-neural-networks-learn-to-predict-the-next-token-6f4302a67269 https://medium.com/@eshvargb/the-llm-journey-how-neural-netw...
- stingraycharles 4mo agoNo not at all, you got it backwards. This was originally called “chain of thought prompting”, and it basically explained a model on how to reason through a problem before providing an answer. Because of the nature of how LLMs work — text prediction engines - by putting the explicit reasoning steps first, it improves the likelihood of the final answer (which then is being predicted based on the entire reasoning chain as input) being correct.
- InsideOutSanta 4mo agoIf you ask an LLM afterward how it arrived at an answer, it might produce a plausible but incorrect explanation. But that's not what the thinking stream is; that's actually part of how it generates the answer.
- Terr_ 4mo ago> To be honest I thought the 'thinking' was the model being asked 'how did you come up with that' and then it generating a plausible explanation. This evades an easy yes or no, so: 1. Many consumers believe reasoning-models allow that kind of question to be truthfully-answered, and their belief it reasonable given the marketing going on. 2. Implementers probably do not have the same belief when it comes to the terms mean or what capabilities they imply. 3. Yes, it doesn't actually do what the customer wanted it to do, which is a kind of retrospective introspection of internal thoughts and ideas. ____________ I advocate looking at everything from a document-generation perspective to cut down on traps and cognitive illusions. The "reasoning" models are a change in the style of document being iteratively-grown by the LLM, as opposed to something more anthropomorphized. * Default: There's just the spoken dialogue between a Human Customer and Helpful Chatbot. * "Reasoning": There's the spoken dialogue and a bunch of times the Helpful Chatbot character has an internal monologue. This provides more consistency between iterations, and can be mined by custom tools to call external code and insert results. If your Human Customer character ask "Why did you say that", the LLM does not engage in a different process than "I have eaten an apple." The LLM has no memories to consult or hidden goals to contemplate, it's the same process of finding more stuff that fits at the end of the document. Any benefits from a "reasoning model" is the LLM generates much better-looking additions because there's more (hidden) stuff for it to confabulate against.
- codelong888 4mo ago[flagged]
- reliablereason 4mo agoIs the thinking even done in real tokens? I thought it was done using the pure residual stream. That is instead of collapsing the residual stream to a token you treat the final layers output as a vector of size d_model and use that as input for the next position in the transformer. If that is the case thinking is not visible to us as users due to it not being done in text.
- giancarlostoro 4mo agoClaude does all its thinking in text, its ChatGPT which does not do its reasoning in text. I believe its sort of implied / understood (?) that this is part of Claude's secret sauce over OpenAI. OpenAI will use less tokens, but Claude will be more correct, more of the time.
- wqaatwt 4mo agoAll open model that have reasoning seem to be doing it in text tokens. Is there any indication that closed models are approaching this somehow fundamentally differently?
- throwuxiytayq 4mo agoThat would be a huge deal, meaning we've lost even our shitty, ineffective ways of monitoring agent reasoning stream. Big setback when it comes to alignment and interpretability. I don't know about Claude, but latest GPT versions still have a readable reasoning stream. It sometimes leaks out when the model gets confused, e.g., during a tool call. If you're curious, looks simplified; less words; extremely compact. They optimize tokens. But remain readable.
- TeMPOraL 4mo agoI saw that idea described as a step in AI 2027 (they call it "neuralese" and eyeballing the site, it's still labeled a hypothetical/future development), but AFAIK no one implemented/deployed this yet. EDIT: They link to a Meta paper from 2024/2025 though: https://arxiv.org/pdf/2412.06769/ https://arxiv.org/pdf/2412.06769/.
- sailingparrot 4mo ago
- wqaatwt 4mo agoIs this some new revelation? That was well known when the first OpenAI/Anthropic “thinking” models came out.
- InsideOutSanta 4mo agoIt's not a new revelation, but clearly a lot of people aren't aware of it, so talking about it is still valuable.
- irthomasthomas 4mo agoI won't use or recommend models with hidden reasoning, (thats all American models). It's too much of a risk and makes prompt optimization harder. Risky because it makes it possible for an attacker to prompt inject the reasoning chain to carry out a secret objective, and to hide that from the summary and output. Interleaved reasoning and function calling makes this even more dangerous. A model can call functions during the hidden reasoning phase. An attacker could then exfiltrate data from you while the reasoning summary hides it from the user. It also makes it impossible to know if the model is doomplooping during reasoning and burning tokens for no reason, as gemini is want to do, which we know about because its hidden reasoning often leaks out when it doomloops. When the models are AGI and secure from prompt injection I may stop caring, until then I want to know exactly what the model responds to my prompts. or exactly what the agent is doing on my behalf. Edit, further reading: Fooling around with encrypted reasoning blobs https://blog.cryptographyengineering.com/2026/05/29/fooling-around-with-encrypted-reasoning-blobs/ https://blog.cryptographyengineering.com/2026/05/29/fooling-...
- deleted 4mo ago[deleted]
- Roritharr 4mo agoI've thought about the high-jacking of reasoning-chains as a potential vector, but never saw a proven implementation in american models since, from my understanding, all major vendors throw out the reasoning tokens between turns.
- JamesSwift 4mo ago> all major vendors throw out the reasoning tokens between turns That would be surprising to me. The reasoning _is_ the model intelligence in a lot of respects, and so dropping those from the context would affect its output pretty significantly. I assume that instead they just have a lot of guardrails in place and multiple runtime environments that an individual turns ping-pong between in order to dehydrate/rehydrate the reasoning to keep it hidden from the end user.
- root_axis 4mo agoResearch shows that even the raw trace tokens do not actually reflect underlying model "thoughts".
- josefritzishere 4mo agoAI does not think. It is a word guessing machine. Anthropomorphizing technology does not add anything to our understanding.
- coldtea 4mo agoA brain itself might be a guessing machine it's an established and actively studied research model of the human thought and the human brain. Nor does knee jerk accusation of "anthropomorphizing" negate the fact that procedures that mimic human processing, even when done in software, are deservingly anthropomorphized, because they're a legitimate approximation of the human equivalent operations.
- slopinthebag 4mo agoWhile the brain does employ statistical processes it’s a big leap to claim that’s the entirety of how it functions.
- akitowerns 4mo ago[dead]
- earningedged 4mo ago[flagged]
- runeblaze 4mo agotbh the summarized thinking with encrypted raw thinking is there for many purposes; it is there to: 1. make distillation much harder 2. safety: prevent modifications to the thinking leading to injection attacks. 3. also honestly sometimes the model raw thoughts can be deranged and is not a good user experience (consider the varied audience in the market, etc.) also often the mass underestimate/the model makers over-estimate how people love distilling models
- soisses 3mo agoThe reasoning blocks are only temporarily part of the context. They aren't part of the context in the next turn anymore so (2) wouldn't really be an issue.
- yuvrajsa 4mo ago[flagged]
- himata4113 4mo agoAll this effort to hide thinking and opus 4.8 after 100k-200k tokens starts to leak it's own thinking. It's comedy really.
- ofjcihen 4mo agoOh man that’s only happened to me a few times but the result is so disorienting, especially since I’m usually jailbreaking it for security. Pages of “I have to be careful, the user is asking that I do something related to cybersecurity that could easily be turned around and used offensively” but then happily gives me what I wanted.
- sigmar 4mo ago>the language in the docs is awfully indirect. writes this^ and then proceeds to highlight a bold title from the docs that says "summarized thinking" that explains things clearly in the first sentence. lol
- layer8 4mo agoThe second sentence is making vague claims though.
- gmerc 4mo agoIt’s an anti distillation effort. They are scared.
- isodev 4mo agoI hope it doesn't come as a surprise to anyone - LLMs don't really "think".
- nlarew 4mo agoYour basic analysis is not the point of the article
- msp26 4mo ago> Summarized thinking provides the full intelligence benefits of extended thinking, while preventing misuse. > preventing misuse. Imagine not being able to read the tokens you are paying for.
- TeMPOraL 4mo agoYou're metered by token generation, not paying for tokens.
- datastoat 4mo agoI believe that chain-of-thought reasoning blocks don't really correspond to what humans think of as reasoning. (See section 6.2.2 of the Fable/Mythos system card about "illegible reasoning", and the questions raised by the Apple paper on "The illusion of thinking".) I assumed they obscure the reasoning blocks because if users saw what's going on they'd be alarmed. Just as I'd probably be alarmed if I saw what was really going on in the heads of my colleagues ...
- MagicMoonlight 4mo ago[dead]
- LPisGood 4mo agoThe point of this post isn’t that the “reasoning” phase of LLM thinking isn’t the same as what humans consider reasoning; it’s that Anthropic is intentionally hiding Claude’s “reasoning output” to make the model harder to distill.
- 0o_MrPatrick_o0 4mo agoReading these comments is so harrowing. You are correct in my intentions on this post generally. I want to highlight: I want to measure performance of the LLMs over time- which includes assessing the quality of their outputs. I don’t perceive the reasoning output to be anything other than a measurable signal of possible drift in model performance. Except it isn’t, because I’m only getting a low value summary of the thinking. It’s like asking your buddy how fast he thought that last pitch was when radar guns are behind the plate. Yeah, it’s a description related to what happened, but it’s not the thing I want to measure.
- Catloafdev 4mo agoI think the reality is at this point the frontier regards CoT as extremely valuable, none of them are giving you genuine CoT anymore. I don't think there is any future in attempting to measure or evaluate CoT from frontier models - I expect this to be a permanent shift.
- linsomniac 4mo agoI feel like I get a lot of what this article presents as "hidden" by using this process: - "Read `description` and create a specification, implementation guide, and checklist." - "Ask clarifying questions. If any of those questions has a clear best recommendation, please select that yourself and record that in "autorecommendations.md". - "Have codex and antigravity review each of these and work to consensus." These are the core of ~61 lines of prompting I do across 3 prompts, and I feel like the resulting artifacts describe some of the thinking. Also, some of the back-and-forth between the models feels like it gives some insight into the model "thinking". I will say: I heavily used Fable when it was available; Opus + loops + codex and/or antigravity review is better than Fable at building things.
- lsdmtme 4mo agoWhat are you using exactly to have claude code natively interact with codex and antigravity? Mind sharing your prompts?
- linsomniac 4mo agoNot at all, I do have a meeting here, I'll try to get it up in around 2h.
- linsomniac 4mo agoI wrote up the prompts and supporting information here: https://linsomniac.com/post/2026-06-22-ai_loops_and_collaboration/ https://linsomniac.com/post/2026-06-22-ai_loops_and_collabor...
- radarsat1 4mo agois it strictly necessary to use different models or can you get similar results by doing the same thing but just using eg Codex in different agents & persona? curious if you've compared this
- linsomniac 4mo agoI haven't specifically compared this. I "feel" like the different models have different strengths and weaknesses, so the collaboration produces great results, assuming cost is no object. ;-)
- nja 4mo agoClaude Code 2.1.68 seems to have been the last version (before the "ctrl-o" debacle) which actually shows thinking inline. That + Opus 4.6 has been working great as a daily driver for me... all the new "safety" / "preventing misuse" pain points in the newer models and harnesses are so frustrating in comparison.
- nekusar 4mo agoYep, its basically a scam to charge you more tokens and provide less compute. You cant even guarantee WHAT model you get. Or if they downgrade you. Or if you 'offend corporate sensibilities' and they misdirect or lie. The only way to get good returns on a model is to run it yourself. Quit paying for corporate bullshit.
- rustcleaner 4mo agoNever ever subscribe. Let them bankrupt themselves on the altar of safety!
- nekusar 4mo agoI'll be safe here, and run Qwen3.6 locally.
- micromacrofoot 4mo agowell yeah I wouldn't want anyone to read my unsummarized thinking either
- jauntywundrkind 4mo agoThere was a little spontaneous outbreak of joy in the GLM vs Opus thread about GLM's willingness/ability to say what it's seeing. https://news.ycombinator.com/item?id=48628464 https://news.ycombinator.com/item?id=48628464 In further reflection it is such a great indignity & such a collosal barrier to working with the machine that it insists on being a black box. The disingenuity of the American models (small print: except AI2 & some other labs; you all are so great) is a massive disadvantage to their use,... and a massive slap in the face. It's a threat to human intelligence that it is not co-participative. Walking further into my own judgement and feelings: the insistence on being an opaque black box, the Seals Chinese Room, is such a vicious harm to society! This is civilizationally an unsafe form of AI that probably should be outlawed as anti-social. It's an impermissible asymmetry, a crippling dependent relationship to be forced into. I'm working myself up, but here: this.. imo, this is not just indignity, is harmful, it is evil. This "6 month behind" trend we've seen for open models feels like at some point will be less important than simply the models unwillingness to speak for itself & to be observable.
- kfarr 4mo agoAlthough it's a no no to anthropomorphize on HN, it's worth noting that some folks think humans are post-hoc rationalizers as well: https://www.patheos.com/blogs/tippling/2013/11/14/post-hoc-rationalisation-reasoning-our-intuition-and-changing-our-minds/ https://www.patheos.com/blogs/tippling/2013/11/14/post-hoc-r... https://www.researchgate.net/publication/316045349_Post_Hoc_Ergo_Propter_Hoc_Some_Benefits_of_Rationalization https://www.researchgate.net/publication/316045349_Post_Hoc_...
- drdaeman 4mo agoAs I naively understand it, that's when we do or say something then narrate ourselves why we decided to do so. We think non-verbally, then verbalize a plausible rationale for it, post hoc. I'm not sure that applies to discursive writing, when we essentially use rules of logic to decide on the course of the narrative. Non-verbal heuristics still applies, of course, but we constrain it, so it's probably not entirely post hoc.
- callmeal 3mo ago>Although it's a no no to anthropomorphize on HN, it's worth noting that some folks think humans are post-hoc rationalizers as well: There's enough behavioural research to show that it is the case. For ex: https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/abs/unconscious-cerebral-initiative-and-the-role-of-conscious-will-in-voluntary-action/D215D2A77F1140CD0D8DA6AB93DA5499 https://www.cambridge.org/core/journals/behavioral-and-brain...
- kfarr 3mo agoThanks, that's a much better example of what I was trying to explain. Again, I'm projecting humanity onto a machine, but it's not a big stretch to imagine that the interaction of attention heads over high-dimensional vectors produces "decisions" that can't be articulated in the moment, only described after the fact in text form.
- segmondy 4mo agoWhat I find sad is how much Anthropic goes to hide your data, yet they are happy to slurp up all yours and most of you are happy to hand it over. ... then they turn around and compete with you by building your products that eat into your market. Anthropic believes their reasoning tokens is a moat and that it's giving other labs an edge and that's why they are hiding it. If they really believe that is their edge, then they are in for a surprise.
- mannanj 4mo agoI don't think people are happy to give it over, gullible and naive maybe?
- handoflixue 4mo ago> then they turn around and compete with you by building your products To my knowledge, the only products Anthropic produces are Claude, Claude Code, and Claude API, all of which are clearly their own products, and not anything you invented. Which particular product are you claiming they "slurped up"?
- ForHackernews 4mo agoWhatever it says is not always what it is doing https://transformer-circuits.pub/2025/attribution-graphs/biology.html#dives-cot https://transformer-circuits.pub/2025/attribution-graphs/bio... > The computation we can see looks like it’s just guessing the answer, despite the chain of thought suggesting it’s computed it using a calculator. It might be hallucinating or lying, it's not like you are actually observing the internals of the model.
- poppafuze 4mo agopost title checks out
- deleted 4mo ago[deleted]
- _fzslm 4mo agoCat and mouse measures like this rarely work forever.
- arjie 4mo agoI have a little note from the past about the thinking trace[0] where DeepSeek R1 produces a trace like this: (Dimethyl(oxo)-lambda6-sulfa雰囲idine)methane donate a CH2rola group occurs in reaction, Practisingproduct transition vs adds this.to productmodule. Indeed"come tally said Frederick would have 10 +1 =11 carbons. So answer q Edina is11. And then concludes the 'right'[1] answer for a Chemistry question. If so, the thinking trace can be sort of nonsensical for a reader, though whether this is an idiosyncrasy of the model or a property of LLMs in general isn't clear to me yet. I talked to the author a while ago, but forgot to follow up since his paper was going to come out at NIPS or something, so if someone else finds it maybe they can share. 0: https://wiki.roshangeorge.dev/w/Blog/2025-10-12/Word_Magic#Illegible_Chains_of_Thought https://wiki.roshangeorge.dev/w/Blog/2025-10-12/Word_Magic#I...? 1: In the sense of true belief, I suppose
- ekidd 4mo ago> If so, the thinking trace can be sort of nonsensical for a reader, though whether this is an idiosyncrasy of the model or a property of LLMs in general isn't clear to me yet. Yes, several models think in weird jargon. Here is an example of Mythos's thinking while playing solitaire: https://www.lesswrong.com/posts/wCSEpT3dTGz4N86Wi/even-illegible-mythos-reasoning-traces-seem-pretty-legible https://www.lesswrong.com/posts/wCSEpT3dTGz4N86Wi/even-illeg... > 7♣-removal-IS-the-prerequisite-for-10♠/9♥!!)-⟹-OVERLAP-(ii)+(iv):-{6♠ J♦ 9♥ 2♣}-=-FOUR--—-UNLESS-7♣'s-seat-8♥-...-and-2♣-drains-only-at-crack-:-⟹-2♣-celled-+-9♥-celled-simultaneously-UNAVOIDABLE-in-t8-dig--—-BREAK:-9♥ This is a small step in the direction of something called "neuralese", where the model has stopped thinking in English and is thinking in internal vector spaces. Since this gets serialized through text, it isn't quite true neuralese, but it's moving in that direction. I mean, I'm sympathetic towards the models. My internal thought process when writing code uses lots of intermediate steps that would be hard to write out in English.
- jaggederest 4mo ago> My internal thought process when writing code uses lots of intermediate steps that would be hard to write out in English. This is something really interesting to me. It turns out there's far more diversity in thinking than you'd imagine given that we're all largely similar meat-in-a-box. I'm on the visio-spatial-tacit wing and speaking my thoughts outloud can be very awkward, whereas one of my former coworkers is on the "all thinking is in words and visual/spatial information comes in the form of words describing the scene" wing, so he can literally narrate his thought process out loud, very interesting conversations can be had discussing the subjective differences.
- deleted 4mo ago[deleted]
- Rekindle8090 4mo ago[dead]
- drdexebtjl 4mo agoI’ve been using OpenCode with GPT models a lot, and it always shows what it is thinking. Is that also a summary? Codex doesn’t seem to have these, even with the same models. It’s much harder to understand _why_ a model chose a particular approach in Claude Code. Especially because Claude will happily give you hallucinated reasons if you ask in retrospect. Recent anecdote: I was reviewing a colleague’s PR and Opus 4.8 decided to write the new feature in a completely new module. It was unnecessarily complex. We had a hard time understanding why it chose that, and it told us that it was so we could eventually deploy it as a separate micro-service and test it independently. What? Only after being more a lot more specific about the implementation and spending a lot more tokens, it flat out refused to simplify the code with the actual reason. It turns out a line recently added to CLAUDE.md was making it incorrectly think that the module it was originally supposed to modify was legacy code that it was forbidden to extend. This would have been caught immediately if we could inspect its thinking process.
- a-dub 4mo agoi wonder if it's about protecting it from extraction/distillation or if it's about not having to answer for surface that hasn't been properly vetted for public consumption. (ie, is someone going to sue them or complain or write blog posts because the thinking has transient things that people don't like where the final result is what is actually vetted?)
- impartshadow 4mo ago[flagged]
- KronisLV 4mo ago> I’m underwhelmed by how Anthropic is presenting the behavior of their application. If you ever need a record of the logic a used by YOUR AGENT during a session. Nope, not your agent, if you're not running it locally. You just get to use it in whatever way they allow (also see the whole OpenClaw backlash and claude -p changes), unless there'd be regulation and laws around this (which there aren't and would be lobbied against anyways). > Getting the full thinking output requires an enterprise agreement. If you truly need it, then that's a (costly) option. Seems like they're largely doing this to prevent other AI foundries from doing as much distillation and stealing their CoT output en masse. Luckily more open models don't generally do that. Edit: If you still need something decently capable in the cloud, I’d suggest GLM, DeepSeek, MiMo or Kimi or Minimax, maaaybe sometimes Mistral for a simple EU subscription. Or look at all the pay-per-token options on OpenRouter, though be mindful of quantization. For running something locally Qwen 3.6 35B A3B is presently a decent starting point but it will be rather limited, either way you can look up the Unsloth quants on HuggingFace for something like llama.cpp or Ollama or LM Studio. All will work with OpenCode and Kilo Code, and most other tools. Can also try with Claude Code, I made a tool for that too: https://ccode.kronis.dev/ https://ccode.kronis.dev/ (or just set the env variables and maybe some aliases for something close enough), but frankly OpenCode is nice nowadays.
- qsxfthnkp2322 4mo agoThey would rather spend time and focus hardening against open models stealing their intelligence than make their tooling better for the people who use them. Proprietary technology is fun /s What a waste of time
- KronisLV 4mo ago> They would rather spend time and focus hardening against open models stealing their intelligence than make their tooling better for the people who use them. Well yes exactly, because they have billions of investments riding on it and why would anyone semi-bankrupt their org paying API rates for Anthropic, if a hypothetical DeepSeek V5 Pro would have almost all of Opus capabilities at that point, due to immense distillation?
- wxw 4mo agoThis seems to be the middle ground between 1) omit all reasoning to protect “trade secrets”/prevent distillations and 2) show all reasoning. I do miss the days when reasoning was visible. Another point for open source models!
- thr0w4w4y1337 4mo agoCaught this one on may 10th, read last 3 sentences: https://imgur.com/a/oTr5Pcc https://imgur.com/a/oTr5Pcc > You've provided the current rewritten thinking and the guidelines, but I don't see the "next thinking" content that I should be rewriting. Could you provide the next thinking that needs to be rewritten? These sentences are completely unrelated to the actual conservation
- sometimelurker 4mo agoof course its a summary of the CoT, there's so many reasons I can think of from both business (anti-distillation from china) and safety (users might `thumbs-up` or thumbs-down a conversation differently depending on the CoT, putting unreliable optimization on the CoT to seem some way. this is really really not that bad at all
- andai 4mo agoAren't the actual reasoning tokens already surprisingly divergent from the models' actual thought process? I've seen at least three separate studies on that subject.
- cawksuwcka 4mo ago[dead]
- topranks 4mo agoThis is to frustrate those using distillation techniques to train their own models right?
- sarracin0 4mo ago[flagged]
- timnetworks 4mo agoif you save a jpeg as a bitmap, doesn't that save every bit faithfully? is the example backwards or is my understanding of maps of bits naive?
- 0o_MrPatrick_o0 4mo agoI had an order of operations error. I better edit it because you’re the second person to get nerd sniped. Sorry friend- you are right
- purpleidea 4mo agoConcise and spot on. I learned about this "thinking" stuff not too long ago, and I was quite surprised that they keep it hidden. Long-term this isn't going to fly. I hope we get truly open models going and let them be owned by society.
- ian_j_butler 4mo agoIt's well-known that the reasoning model output is not necessarily faithful to the content of the thinking scratch pad anyway, even if you had it unsummarized and available verbatim. Setting aside coding agents.. we really need this information to even pretend to evaluate the claims of stuff like mathematical breakthroughs, which is exactly why we will never see it. Very embarrassing to get the right answer for the wrong reason. But to give the models some credit, you could argue that even paying too much attention to the thinking is misunderstanding how CoT works. The argument would be that thinking in LLMs isn't really thinking, that it's self-reinforcement and circling to to encourage stability around beneficial attractors instead of degenerate ones. Can't have it both ways though: either the thinking is thinking and so it should be correct. Or the thinking is NOT thinking, and it's NOT real justification for the outcome, and these systems are even more hopelessly opaque than we usually assume.
- anuramat 4mo ago> NOT real justification I thought it was widely accepted that it's not; eg https://www.anthropic.com/research/natural-language-autoencoders https://www.anthropic.com/research/natural-language-autoenco...
- ian_j_butler 4mo agoRight, I don't think researchers are confused on this point.. the anthropic piece is good outreach / science comms. OTOH this thread has like 200 comments and no mention of faithful/faithless reasoning. The idea that "of course the models can reason and here is the proof/artifact" is probably closer to the general understanding. That's kinda the whole setup for TFA and all the rest of the thread. But the nuance under discussion here is exactly the kind of stuff you people take for granted in the AGI or reasoning threads. If it's practically relevant for tools/workflows with claude code, it's a good angle, maybe people are more willing to pay more attention to the details.
- handoflixue 4mo ago> we really need this information to even pretend to evaluate the claims of stuff like mathematical breakthroughs Why? Either the proof is correct, or it isn't, right? And it either produces them reliably or not, right? Like, even if it's reasoning is completely wrong, and it's only producing correct answers 10% of the time, that's still an astounding amount above baseline and a useful tool. Humans have inaccurate thinking all the time, and are also pretty hopelessly opaque. "It came to me in a dream" is a major plot point in the history of math. I'd still trust Ramanujan more than most mathematicians, since he got the right answer.
- sheepscreek 4mo agoThe initial motivation for this was likely to thwart any competition. Already Anthropic has accused some companies of organized distillation efforts at a massive scale. Back when I used antigravity, it used to show the reasoning intact - at least for Gemini Pro 3.1, and likely for Claude Opus 4.6 (not 100% certain about it). I have some recollection of stopping the models mid-turn when they started going astray. As a power user, I find reasoning fascinating to read and genuinely useful at times. Probably not that useful for 80% of their base.
- razodactyl 4mo agoHeh. Summarising allows the benefit of full intelligence whilst preventing "misuse". Where "misuse" is likely competitors stealing thinking traces. Even though this is clearly work inspired from the OpenAI Strawberry era.
- implexa_founder 4mo agoyou have been asking about "extended thinking" from a machine that has been "dreaming". good luck!
- akmal_codes 4mo ago[flagged]