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.
>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.
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
Ignore the hype in the headline, the point is that there's good evidence that inference in many circumstances isn't profitable.
So he's using their API prices as a proxy for token costs, doesn't actually know the actual inference prices, and ... that's your "good evidence?" This big sentence with all these "We don't knows?"
Does this idea upset you for some reason? Other people have analyzed this and come to similar conclusions, I just picked that one because it's the most recent example I've seen.
Feel free to look to a source that explains how LLM Internet is mostly profitable at this point, taking training costs into account. But I suspect you might have a hard time finding evidence of that.
How about waiting till after "AI" becomes capable of doing... anything even remotely resembling that, or displaying anything like actual volition?
"AI safety" consists of the same thing all industrial safety does: not putting a nondeterministic process in charge of life- or safety-critical systems, and only putting other automated systems in charge with appropriate interlocks, redundancy, and failsafes. It's the exact same thing it was when everybody was doing "machine learning" (and before that, "intelligent systems", and before that some other buzzword that anthropomorphized machines...) and not being cultishly weird about statistical text generators. It's the kind of thing OSHA, NTSB and the FAA (among others) do every day, not some semi-mystical religion built around detecting intent in a thing that can't actually intend anything.
If you want actual "AI safety", fund public safety agencies like NHTSA and the CPSC, not weird Silicon Valley cults.
I think it would pretty unfortunate to wait until AI is capable of doing something that "remotely resembles" causing an extinction event before acting.
> , or displaying anything like actual volition?
Define "volition" and explain how modern LLMs + agent scaffolding systems don't have it.
Nobody talks about a malfunctioning thermostat that makes a room too cold being "misaligned with human values" or a miscalibrated thermometer exhibiting "deception", even though both of those can carry very real risks to, or mislead, humans depending on what they control or relying on them being accurate. (Just ask the 737 MAX engineers about software taking improper actions based on faulty inputs -- the MAX's MCAS was not malicious, it was poorly-engineered.)
As to the last point, the burden of proof is not to prove a nonliving thing does not have mind or will -- it's the other way around. People without a programming background back in the day also regularly described ELIZA as "insightful" or "friendly" or other such anthropomorphic attributes, but nobody with even rudimentary knowledge of how it worked said "well, prove ELIZA isn't exhibiting free will".
Christopher Strachey's commentary on the ability of the computers of his day to do things like write simple "love letters" seems almost tailor-made for the current LLM hype:
"...with no explanation of the way in which they work, these programs can very easily give the impression that computers can 'think.' They are, of course, the most spectacular examples and ones which are easily understood by laymen. As a consequence they get much more publicity -- and generally very inaccurate publicity at that -- than perhaps they deserve."
Curious what others think about this direction, particularly in terms of practicality
In other words, relying on censoring the CoT can risk the effect of making the CoT altogether useless.
Basically: https://www.anthropic.com/research/reasoning-models-dont-say...
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.
This is better thought of as another form of context engineering. LLM's have no other short-term memory. Figuring out what belongs in the context is the whole ballgame.
(The paper talks about the risk of training on chain of thought, which changes the model, not monitoring it.)
All of those seem like very reasonable criteria that will naturally be satisfied absent careful design by model creators. We should expect latent deceptiveness in the same way we see reasoning laziness pop up quickly.
Incorrectness doesn't required intent to decieve. It's just being wrong
None of this requires the ML model to have any interiority.
The ML model needn’t really know what a person really is, etc. , as long as it behaves in ways that correspond to how something that did know these things would behave, and has the corresponding consequences.
If someone is role-playing as a madman in control of launching some missiles, and unbeknownst to them, their chat outputs are actually connected to the missile launch device (which uses the same interface/commands as the fictional character would use to control the fictional version of the device), then if the character decides to “launch the missiles”, it doesn’t matter whether there actually existed a real intent to launch the missiles, or just a fictional character “intending” to launch the missiles, the missiles still get launched.
Likewise, if Bob is role playing as a character Charles, and Bob thinks that on the other side of the chat, the “Alice” he is speaking to is actually someone else’s role play character, and the character Charles would want to deceive Alice to believe something (which Bob thinks that the other person would know that the claim Charles would make to be false, but the character would be fooled), but in fact Alice is an actual person who didn’t realize that this was a role play chatroom, and doesn’t know better than to believe “Charles”, the Alice may still be “deceived”, even though the real person Bob had no intent to deceive the real person Alice, it was just the fictional character Charles who “intended” to deceive Alice.
Then, remove Bob from the situation, replacing him with a computer. The computer doesn’t really have an intent to deceive Alice. But the fictional character Charles, well, it may still be that within the fiction, Charles intends to deceive Alice.
The result is the same.
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?
See: 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 to help me read papers more effectively without losing accuracy, and looking for more feature tuning. It's also open source :)
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...
Researchers are already pushing in this direction:
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
Can't monitor the chain of thought if it's no longer in a human legible format.
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.
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?
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.
rdtsc•14h ago
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•13h ago
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?"
the8472•13h ago
[0] https://thezvi.substack.com/p/ai-68-remarkably-reasonable-re... [1] https://arxiv.org/abs/2503.11926 [2] https://docs.google.com/spreadsheets/u/1/d/e/2PACX-1vRPiprOa...
jerf•13h ago
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.
antonvs•12h ago
I can't imagine why anyone who knows even a little about how these models work would believe otherwise.
The "chain of thought" is text generated by the model in response to a prompt, just like any other text it generates. It then consumes that as part of a new prompt, and generates more text. Those "thoughts" are obviously going to have an effect on the generated output, simply by virtue of being present in the prompt. And the evidence shows that it can help improve the quality of output. But there's no reason to expect that the generated "thoughts" would correlate directly or precisely with what's going on inside the model when it's producing text.