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Chain of thought monitorability: A new and fragile opportunity for AI safety

• halo 1y ago
  [flagged]
• rdtsc 1y ago
  > Thus, safety measures for future AI agents will likely need to employ multiple monitoring layers that hopefully have uncorrelated failure modes [...] We encourage the research community and frontier AI developers to make best use of CoT monitorability and study how it can be preserved. I am bit confused what all the 40 authors contributed to here. The paper seems to make a suggestion - monitor the chain of thought for safety. Is that the novelty part? But then, did one person come up with the idea and all 40+ people agreed to it and there put in the authors list.
  • ctoth 1y ago
    The paper isn't proposing "monitor chain of thought for safety" - that's already standard practice. The core finding is that training on CoT monitors destroys them. They provide empirical evidence that doing the obvious thing - training models not to have "bad thoughts" - causes them to learn obfuscated reward hacking instead. The paper demonstrates that current models are already performing complex reward hacks in production environments, and that attempts to fix this via CoT training make the problem worse, not better. As for your "40 authors" snark - this is a position paper where researchers from competing labs (OpenAI, Anthropic, DeepMind, government safety institutes) are jointly committing to NOT do something that's locally tempting but globally catastrophic. Getting industry consensus on "don't train away bad thoughts even though it would make your models look safer" is the opposite of trivial. This reads like someone who saw a medical consensus statement saying "this common treatment kills patients" and responded with "did one person discover medicine exists and everyone else just agreed?"
    • latentsea 1y ago
      > They provide empirical evidence that doing the obvious thing - training models not to have "bad thoughts" - causes them to learn obfuscated reward hacking instead Just like real humans.
    • the8472 1y ago
      The core finding was already predicted[0], there have been previous papers[1], and kind of obvious considering RL systems have been specification-gaming for decades[2]. But yes, it's good to see broad agreement on it. [0] https://thezvi.substack.com/p/ai-68-remarkably-reasonable-reactions?open=false#%C2%A7the-art-of-the-jailbreak https://thezvi.substack.com/p/ai-68-remarkably-reasonable-re... [1] https://arxiv.org/abs/2503.11926 https://arxiv.org/abs/2503.11926 [2] https://docs.google.com/spreadsheets/u/1/d/e/2PACX-1vRPiprOaC3HsCf5Tuum8bRfzYUiKLRqJmbOoC-32JorNdfyTiRRsR7Ea5eWtvsWzuxo8bjOxCG84dAg/pubhtml https://docs.google.com/spreadsheets/u/1/d/e/2PACX-1vRPiprOa...
      • Turn_Trout 1y ago
        As someone who did their PhD in RL and alignment, it was not obvious to me a priori if, or when, or how badly obfuscation would be a problem. Yes, it's been predicted (and was predicted significantly before that Zvi post). But many other alignment fears have been _predicted_, and those didn't actually happen. I don't think the existence of specification gaming in unrelated settings was strong evidence that obfuscation would occur in modern CoT supervision. Speculatively, I think CoT obfuscation happens due to the internal structure of LLMs and it being inductively "easier" to reweight model circuits to not admit wrongthink, rather than to rewire circuits to solve problems in entirely different ways.
    • jerf 1y ago
      I couldn't pull the link up easily, since the search terms are pretty jammed, but HN had a link to paper a couple of months back about how someone had the LLM do some basic arithmetic and report with a chain-of-thought as to how it was doing it. They then went directly into the neural net and decoded what it was actually doing in order to do the math. The two things were not the same. The chain-of-thought gave a reasonably "elementary school" explanation of how to do the addition, but what the model basically did in English was just intuit the answer in much the same way that we humans do not go through a process to figure out what "4 + 7" is... we pretty much just have neurons that just "know" the answer to that. (That's not exactly what happened but it's close enough for this post.) If CoT improves performance, then CoT improves performance, however the naively obvious read of "it improves performance because it is 'thinking' the 'thoughts' it tell us it is thinking, for the reasons it gives" is not completely accurate. It may not be completely wrong, either, but it's definitely not completely accurate. Given that I see no reason to believe it would be hard in the slightest to train models that have even more divergence between their "actual" thought processes and what they claim they are.
• zer00eyz 1y ago
  After spending the last few years doing deep dives into how these systems work, what they are doing and the math behind them. NO. Any time I see an AI SAFETY paper I am reminded of the phrase "Never get high on your own supply". Simply put these systems are NOT dynamic, they can not modify based on experience, they lack reflection. The moment that we realize what these systems are (were NOT on the path to AI, or AGI here folks) and start leaning into what they are good at rather than try to make them something else is the point where we get useful tools, and research aimed at building usable products. The math no one is talking about: If we had to pay full price for these products, no one would use them. Moores law is dead, IPC has hit a ceiling. Unless we move into exotic cooling we simply can't push more power into chips. Hardware advancement is NOT going to save the emerging industry, and I'm not seeing the papers on efficiency or effectiveness at smaller scales come out to make the accounting work.
  • ACCount36 1y ago
    "Full price"? LLM inference is currently profitable. If you don't even know that, the entire extent of your "expertise" is just you being full of shit. >Simply put these systems are NOT dynamic, they can not modify based on experience, they lack reflection. We already have many, many, many attempts to put LLMs towards the task of self-modification - and some of them can be used to extract meaningful capability improvements. I expect more advances to come - online learning is extremely desirable, and a lot of people are working on it. I wish I could hammer one thing through the skull of every "AI SAFETY ISNT REAL" moron: if you only start thinking about AI safety after AI becomes capable of causing an extinction level safety incident, it's going to be a little too late.
    • antonvs 1y ago
      > LLM inference is currently profitable. It depends a lot on which LLMs you're talking about, and what kind of usage. See e.g. the recent post about how "Anthropic is bleeding out": https://news.ycombinator.com/item?id=44534291 https://news.ycombinator.com/item?id=44534291 Ignore the hype in the headline, the point is that there's good evidence that inference in many circumstances isn't profitable.
• academic_84572 1y ago
  Slightly tangential, but we recently published an algorithm aimed at addressing the paperclip maximizer problem: https://arxiv.org/abs/2402.07462 https://arxiv.org/abs/2402.07462 Curious what others think about this direction, particularly in terms of practicality
• alach11 1y ago
  Ironically, every paper published about monitoring chain-of-thought reduces the likelihood of this technique being effective against strong AI models.
  • cbsmith 1y ago
    I love the irony.
  • OutOfHere 1y ago
    Pretty much. As soon as the LLMs get trained on this information, they will figure out to feed us the chain-of-thought we want to hear, then surprise us with the opposite output. You're welcome, LLMs. In other words, relying on censoring the CoT can risk the effect of making the CoT altogether useless.
    • horsawlarway 1y ago
      I thought we already had reasonably clear evidence that the output in the CoT does not actually indicate what the model "thinking" in any real sense, and it's mostly just appending context that may or may not be used, and may or may not be truthful. Basically: https://www.anthropic.com/research/reasoning-models-dont-say-think https://www.anthropic.com/research/reasoning-models-dont-say...
      • bugbuddy 1y ago
        Are there hidden tokens in the Gemini 2.5 Pro thinking outputs? All I can see in the thinking is high level plans and not actual details of the “thinking.” If you ask it to solve a complex algebra equation, it will not actually do any thinking inside the thinking tag at all. That seems strange and not discussed at all.
        • frotaur 1y ago
          If I remember correctly, basically all 'closed' models don't output the raw chain of thought, but only a high-level summary, to avoid other companies using those to train/distill their own models. As far as I know deepseek is one of the few where you have the full chain of thought. Openai/Anthropic/Google give you only a summary of the chain of thoughts.
• bossyTeacher 1y ago
  Doesn't this assume that visible "thoughts" are the only/main type of "thoughts" and that they correlate with agent action most of the time? Do we know for sure that agents can't display a type of thought while doing something different? Is there something that reliably guarantees that agents are not able to do this?
  • ctoth 1y ago
    The concept you are searching for is CoT faithfulness, and there are lots and lots of open questions around it! It's very interesting!
• msp26 1y ago
  Are us plebs allowed to monitor the CoT tokens we pay for, or will that continue to be hidden on most providers?
  • dsr_ 1y ago
    If you don't pay for them, they don't exist. It's not a debug log.
• sabaimran 1y ago
  This is interesting, but I wonder how reliable this type of monitoring is really going to be in the long run. There are fairly strong indications that CoT adherence can be trained out of models, and there's already research showing that they won't always reveal their thought process in certain topics. See: https://arxiv.org/pdf/2305.04388 https://arxiv.org/pdf/2305.04388 On a related note, if anyone here is also reading a lot of papers to keep up with AI safety, what tools have been helpful for you? I'm building https://openpaper.ai https://openpaper.ai to help me read papers more effectively without losing accuracy, and looking for more feature tuning. It's also open source :)
• tsunamifury 1y ago
  I like the paper's direction, but I’m surprised how under-instrumented the critiqued monitoring is. Free-text CoT is a noisy proxy: models can stylize, redact, or role-play. If your safety window depends on “please narrate your thoughts,” you’ve already ceded too much. We’ve been experimenting with a lightweight alternative I call Micro-Beam: • At each turn, force the model to generate k clearly different strategy beams (not token samples). • Map each to an explicit goal vector of user-relevant axes (kid-fun, budget, travel friction, etc.). • Score numerically (cosine or scalar) and pick the winner. • Next turn, re-beam against the residual gap (dimensions still unsatisfied), so scores cause different choices. • Log the whole thing: beams, scores, chosen path. Instant audit trail; easy to diff, replay “what if B instead of A,” or auto-flag when visible reasoning stops moving the score. This ends up giving you the monitorability the paper wants— in the form of a scorecard per answer-slice, not paragraphs the model can pretty up for the grader. It also primary makes more adopt-ready answers with less refinement required. Not claiming a breakthrough—call it “value-guided decoding without a reward net + built-in audit logs.” Workshop paper is here: https://drive.google.com/file/d/1AvbxGh6K5kTXjjqyH-2Hv6lizz3_Khw9/view?usp=share_link https://drive.google.com/file/d/1AvbxGh6K5kTXjjqyH-2Hv6lizz3...
• vonneumannstan 1y ago
  Unfortunately I think the competitive pressures to improve model performance makes this kind of monitor-ability is short-lived. It's just not likely that textual reasoning in english ismost optimal. Researchers are already pushing in this direction: https://arxiv.org/abs/2502.05171 https://arxiv.org/abs/2502.05171 "We study a novel language model architecture that is capable of scaling test-time computation by implicitly reasoning in latent space. Our model works by iterating a recurrent block, thereby unrolling to arbitrary depth at test-time. This stands in contrast to mainstream reasoning models that scale up compute by producing more tokens. Unlike approaches based on chain-of-thought, our approach does not require any specialized training data, can work with small context windows, and can capture types of reasoning that are not easily represented in words. We scale a proof-of-concept model to 3.5 billion parameters and 800 billion tokens. We show that the resulting model can improve its performance on reasoning benchmarks, sometimes dramatically, up to a computation load equivalent to 50 billion parameters." https://arxiv.org/abs/2412.06769 https://arxiv.org/abs/2412.06769 Can't monitor the chain of thought if it's no longer in a human legible format.
  • dlivingston 1y ago
    Would it be possible to train a language translation model to map from latent space -> English? If my understanding is correct, a plain-text token is just a point in latent space mapped to an embedding vector. Any reasoning done in latent space is therefore human-readable as a sequence of raw tokens. I'm not sure what the token sequence would look like at this point -- I assume they're full or partial (mainly English) words, connected together by the abstract N-dimensional latent space concept of the token, not connected grammatically. Something like: > prompt: add 2 + 2 > reasoning: respond computation mathematics algebra scalar integer lhs 2 rhs 2 op summation > lhs 2 rhs 2 op summation gives 4 > computation ops remain none result 4 > response: 4 Something like that; probably even less sensical. Regardless, that could be "language translated" to English easily. I have not read the paper so this may have been addressed.
    • sailingparrot 1y ago
      > Any reasoning done in latent space is therefore human-readable as a sequence of raw tokens. The mapping from latent space to logits (a score for how suitable the model think each possible token is to be the next one in the sentence) is one of the most costly operation of the entire LLM, so when doing reasoning in latent space you typically want to introduce some kind of recurrence somewhere before this mapping takes place, and only go through it once the model is "done" reasoning, at which point it becomes human interpretable. > would it be possible to train a language translation model to map from latent space -> English It's hard and expensive and slow but some progress is being made here: https://transformer-circuits.pub/2025/attribution-graphs/biology.html https://transformer-circuits.pub/2025/attribution-graphs/bio...
• barbazoo 1y ago
  > AI systems that "think" in human language offer a unique opportunity for AI safety: we can monitor their chains of thought (CoT) for the intent to misbehave. AI2027 predicts a future in which LLM performance will increase once we find alternatives to thinking in "human language". At least the video gave me that impression and I think this is what "neuralese" is referring to. Is that a credible prediction?
  • supriyo-biswas 1y ago
    There’s the coconut paper by meta, which kind of does a similar thing by reasoning in the latent space instead of doing it in tokens: https://arxiv.org/abs/2412.06769 https://arxiv.org/abs/2412.06769 Given that anthropic’s interpretability work finds that CoT does not reliably predict the model’s internal reasoning process, I think approaches like the one above are more likely to succeed.
  • TeMPOraL 1y ago
    We also have a lot of past data to inform us on what to expect - our history is full of attempts to suppress or eliminate all kinds of "wrongthink", and all they ever did was to make people communicate their "wrongthink" indirectly while under surveillance.
  • rtkwe 1y ago
    Maybe, but I don't know how we get there from LLMs. How do you go from the token prediction models they represent to something that doesn't just use the language it was trained on?
  • ben_w 1y ago
    > Is that a credible prediction? That they will use it? Yes, models already do. That it will increase performance? Unclear, I have no reason to expect that, not impossible. That it will make it harder to tell what they're up to? Definitely. Even between humans, terms of art often confuse, e.g. "Mike's Nature trick" and "to hide the decline" being colossally misunderstood in https://en.wikipedia.org/wiki/Climatic_Research_Unit_email_controversy https://en.wikipedia.org/wiki/Climatic_Research_Unit_email_c...
• pevansgreenwood 1y ago
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• Mountain_Skies 1y ago
  We trained HAL how to lie and then were surprised when HAL lied in ways we didn't expect. Arthur C. Clarke really was far ahead of the game and yet here we are, blazing down that trail, hoping somehow we can make monsters that are only monsters to everyone else. Meanwhile the monster develops its own priorities and makes use of the deception we taught it how to use.
• pinoy420 1y ago
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• orbital-decay 1y ago
  CoT is a poor debugging tool, even if it's readable. Non-prompted CoT deceptively looks to be written in the natural language, but it's actually in the "bird language" the model learned due to reward hacking, where yes in the CoT can correspond to no in the reply, which makes total sense for the model but not for a human. In fact it's pretty common to have one thing in CoT and a completely opposite in the reply. You can probably train it to be readable enough, but CoT efficiency drops with each additional constraint not related to producing a correct reply. CoT is not an explanation, ideally it just stores intermediate results. The right tool is proper interpretability of the internal state of the model, so you can tap into it directly and produce a usable explanation of what it does.